*Article* **Design and Development of a Heterogeneous Active Assisted Living Solution for Monitoring and Following Up with Chronic Heart Failure Patients in Spain**

**Francisco José Melero-Muñoz 1,2,\*, María Victoria Bueno-Delgado 2,3, Ramón Martínez-Carreras 2, Rafael Maestre-Ferriz 1, Miguel Ángel Beteta-Medina 1, Tomás Puebla-Martínez 1, Andrés Lorenzo Bleda-Tomás 1, Gorka Sánchez-Nanclares 4, Ricardo Pérez-de-Zabala <sup>5</sup> and Mónica Álvarez-Leon <sup>6</sup>**

	- <sup>3</sup> E-Lighthouse Network Solutions S.L, C/Angel s/n, 30202 Cartagena, Spain
	- <sup>4</sup> Servicio Murciano de Salud, Edif. Habitamia 5º, 30100 Murcia, Spain
	- <sup>5</sup> MIWEnergia, Parque Científico de Murcia, 30100 Espinardo, Spain
	- <sup>6</sup> Minsait–Av. Bruselas 35, 28108 Alcobendas, Spain
	- **\*** Correspondence: fj.melero@cetem.es

**Citation:** Melero-Muñoz, F.J.; Bueno-Delgado, M.V.; Martínez-Carreras, R.; Maestre-Ferriz, R.; Beteta-Medina, M.Á.; Puebla-Martínez, T.; Bleda-Tomás, A.L.; Sánchez-Nanclares, G.; Pérez-de-Zabala, R.; Álvarez-Leon, M. Design and Development of a Heterogeneous Active Assisted Living Solution for Monitoring and Following Up with Chronic Heart Failure Patients in Spain. *Sensors* **2022**, *22*, 8961. https://doi.org/ 10.3390/s22228961

Academic Editors: Bijan Najafi, Enrico G. Caiani and Gérald Thouand

Received: 30 September 2022 Accepted: 17 November 2022 Published: 19 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Abstract:** Heart failure is the most common disease among elderly people, and the risk increases with age. The use of smart Internet of Things (IoT) systems for monitoring patients with chronic heart failure (CHF) in a non-intrusive manner can result in better control of the disease, improving proactive healthcare through real-time and historical patient's data, promoting self-care in patients, reducing unneeded interaction between patients and doctors, reducing the number of hospitalizations and saving healthcare costs. This work presents an active assisted living (AAL) solution based on the IoT to provide a tele-assistance platform for CHF patients from the public health service of the region of Murcia in Spain, with formal and informal caregivers and health professionals also as key actors. In this article, we have detailed the methodology, results, and conclusions of the prevalidation phase for the set of IoT technologies to be integrated in the AAL platform, the first mandatory step before the deployment of a large-scale pilot that will lead to improving the innovation of the system from its current technology readiness level to the market. The work presented, in the framework of the H2020 Pharaon project, aims to serve as inspiration to the R&D community for the design, development, and deployment of AAL solutions based on heterogeneous IoT technologies, or similar approaches, for smart healthcare solutions in real healthcare institutions.

**Keywords:** AAL; IoT; healthcare; prevalidation; deployment; chronic heart failure; large-scale pilot; H2020

#### **1. Introduction**

In a rapidly ageing European society, there is a growing need for implementing information and communication technologies (ICT) and digital tools that improve the quality of life, independence, and overall health of older adults. In this context, the concept of ambient intelligence (AmI) appears to achieve a future where technology surrounds the users and helps them in their daily lives [1]. AmI leads to cutting-edge platforms referred to as active assisted living platforms [2,3]. The Internet of Things (IoT) [4] has also emerged as a set of technologies, systems, and design principles [5,6] that enable automation in many fields, such as remote and smart healthcare systems, playing an important role in AAL platforms and healthcare. A typical IoT environment consists of communication interfaces, sensors, advanced algorithms, and cloud interfaces [7]. Sensors are responsible for collecting data from various devices. Additionally, different communication technologies (wired and/or

wireless), such as wireless sensor networks (WSN), provide network and communication infrastructure [8], while advanced algorithms are used to analyze and process data [9]. Numerous client/server requests can be exchanged in the cloud environment and allow the users to have access to various types of services simultaneously [10,11]. Due to cloud computing challenges and high requisites of emerging 5G, such as latency, reliability, resource constraints, etc. Fog computing is used to overcome these limitations and run the same applications anywhere close to users with real-time analysis and efficient decision-making features [12,13].

Considerable effort is being made in the R&D community to integrate IoT technologies, communications [14], databases, and computing [15], and to develop standardized and integrated AAL platforms [16], but the diversity of these types of systems has created a very fragmented market and a lack of standardized architectures and protocols [17].

In the healthcare industry, sanitary systems are being revolutionized by the IoT paradigm [18]. This plays an important role in telemonitoring in hospitals, especially at homes for elderly people with chronic diseases [19]. By using this technology, healthcare systems can experience major effects such as a reduction in response time to detect anomalies, high-quality care, low hospitalization costs, and high life expectancy [18].

In the specific case of heart failure (HF), the most common disease in elderly people and one that increases in prevalence with age [20], the use of an IoT system for monitoring patients with chronic heart failure (CHF) in a non-intrusive manner can result in better control of the disease, improving the proactive healthcare and reducing unneeded interaction between patients and doctors, thereby reducing the number of hospitalizations and saving healthcare costs.

HF is a leading cause of hospitalization, representing 1–2% of all hospital admissions [21,22]. In Spain, the prevalence of HF is around 5% (higher than in other EU countries and USA); the rate rises with age to 8% between the ages of 65 and 74, and to 16% in persons aged 75 years and over [23]. These rates mean enormous health care resources are required, e.g., in Spain, during the 2015–2019 period, costs of HF patients were EUR 15,373 per patient, with HF hospitalizations being the most important determinant (51.0%). Medication costs represented only a small proportion of total costs [24]. The latest data published by Eurostat revealed that in 2018, HF caused 19,142 deaths in Spain, constituting 4.5% of all deaths, 3.4% in men and 5.6% in women [25]. Within this frame of reference, cardiologists and family doctors believe that the key to a better control of patients with CHF is creating a proactive healthcare solution through a non-intrusive and integrated monitoring system that unifies patients' medical history and makes it available to all relevant healthcare professionals. This will improve the safety and efficiency of healthcare by increasing the availability of patients' real-time information. In addition, the health and care community are convinced that it is crucial to promote self-care in patients by providing them and their caregivers with health education and training and other social and health resources.

This work presents an AAL solution based on IoT to provide a tele-assistance platform for CHF patients from the public health service of the region of Murcia in Spain. Initially focused on CHF patients aged 55 and over, the study also included formal and informal caregivers and healthcare professionals. For the three user groups, two scenarios are considered to provide a comprehensive healthcare solution:


Figure 1 summarizes the goals, roles and description of the two scenarios considered.


**Figure 1.** Scenarios of the Pharaon Murcia pilot.

This work is part of the overall research in progress regarding the H2020 Pharaon Project (Pilots for Healthy and Active Ageing) [26], which aims at providing a smart and active lifestyle for Europe's ageing population by creating a set of integrated and highly customizable interoperable open platforms with advanced services, devices, and tools in AAL, including IoT, artificial intelligence, robotics, cloud computing, smart wearables, big data, and intelligent analytics. Built upon mature existing state-of-the-art open platforms and technologies/tools, a user-centric approach is being followed for the deployment and the two-stages validation (prevalidation and large-scale pilots) in six different pilot sites: Murcia and Andalusia (Spain), Portugal, the Netherlands, Slovenia, and Italy.

The Pharaon ecosystem integrates a big set of services and functionalities which requires the involvement of a huge number of resources in order to achieve the necessary validation and trials in real-world scenarios. The validation within Pharaon is being performed through the six large-scale pilots proposed with different types of users, requirements, and chosen functionalities. Pharaon aims to carry out the unprecedented validation of different platforms simultaneously, each supporting a wide variety of advanced and customized assistive services and tools in six different large-scale pilots with the necessary resources. For example, the Murcia pilot will validate heart failure and non-intrusive home monitoring with alarm triggering, whereas the pilot in The Netherlands focuses on community building and providing tailored advice towards user empowerment in terms of health literacy.

This approach allows a sufficiently high number of users and healthcare professionals to use the system at each pilot site for a long-term period, and an unprecedented number of use cases, services, and technologies will be tested across all pilots.

In this work, the authors present the methodology, results, and conclusions of the prevalidation phase for the set of IoT solutions and their respective AAL platforms, which will introduce the tele-assistance platform for CHF patients from the public health service of the region of Murcia. The prevalidation stage includes the participation of all actors involved: patients, formal and informal caregivers, and healthcare professionals. Their early feedback regarding the functionality and usefulness of the tested technologies and platforms is the first mandatory step before the deployment of a large-scale pilot that will lead to improving the innovation of the system from its current technology readiness level (TRL) (6) to the market (9). The goal is to provide the R&D community with inspiration in the design and deployment of AAL solutions based on heterogeneous IoT technologies, or similar approaches, for smart healthcare solutions in real healthcare institutions.

The paper is organized as follows: Section 2 describes in detail the Pharaon Murcia pilot and the workplan followed for implementing the smart healthcare solution. Section 3 explains the engineering of user requirement tasks that were performed to identify the scenarios that define the use cases of the AAL healthcare solution and their requisites. Section 4 looks in depth at the system architecture that must perform the use cases and technical features of the hardware/software needed. Section 5 describes the roadmap implemented for the testing phase. Section 6 presents and discusses the results of the testing. Section 7 shows the conclusions and the future work recommendations. Finally, Section 8 presents a short discussion about the research limitations.

#### **2. Pharaon Murcia Pilot: Overview and Workplan for the Healthcare Solution Deployment**

The reduction in birth rate and the increase in life expectancy will, in the long term, lead to a progressive ageing of the population in the region of Murcia, which will manifest itself in an uninterrupted decline in the working-age population and a continued increase in the proportion of the population over 65 years of age [27]. One of the priorities of the research and innovation strategy for smart specialization in the region of Murcia [28] is related to health, biomedicine, and welfare, addressing, among other fields, housing care and ITC-supported social services, specialized care, access to services, and remote assistance.

The services and use cases to be deployed under Pharaon in the region of Murcia are aimed at building the foundations of a new telecare line in the region that will transcend the current model of health and care services that rely on the patients to notice when they need help. This new telecare model will allow patients to stay in their preferred environment and provide a more intense, effective, proactive, and less intrusive care and observation service. To do that, this work has been organized into a sequence of main steps, summarized in Figure 2. The first step consists of the elicitation and representation of the user requirements. This leads to the definition of the use case scenarios of the pilot and detailed technical requirements referring to the technologies to be used.

**Figure 2.** Work sequence within the Pharaon Murcia pilot.

The second step, informed by the results of step 1, has two main goals: to address the adaptations required for the compliance of all requirements, and the development and integration of the technologies and services in the platform.

Finally, step 3 consists of the testing phase, organized into two stages: (1) small-scale testing with a limited number of participants representing the different target users of the Murcia–Pharaon system. They provide early feedback regarding the functionality and usefulness of the system, enabling rapid and iterative improvements to address shortcomings discovered by actual system users while its implications are still manageable. The second

stage is (2) running the large-scale pilot, which involves all the pilot users in real-world scenarios for an extended length of time.

#### **3. Engineering of User Requirements in the Pharaon Murcia Pilot**

In [29], the authors summarized the co-design and user requirement engineering work carried out in the pilot study of the region of Murcia. During the co-design phase, the methodology for the co-design and representation of user requirements was defined as goal models with a set of components: functional goals, quality goals, and emotional goals, following work in [30]. Corresponding use case scenarios and user stories were also defined.

The methodology entailed several up-to-date co-design methods for user requirements' elicitation. The ISO 9241-210 standard on ergonomics of human–system interaction [31] was followed. The original plan for eliciting and representing user requirements was modified due to the COVID-19 outbreak, following three phases:

The first phase is initial desk research on co-design workshops, data, and results from previous initiatives in which the public health service provider of the region of Murcia participated: ProEmpower [32], ReadiForHealth [33], INC3A [34], and CARPRIMUR [35]. They helped to identify an initial set of requirements from the stakeholder's perspective in the form of functional, quality, and emotional goals.

The second phase is the design and launch of a questionnaire addressing target users of the Pharaon system that helped to define a map of barriers and opportunities in the region regarding the assistance of patients suffering from CHF. The goal was to enrich the results in the previous phase with the opinion of other representatives that were also target groups (older adults, informal caregivers, and health and care professionals). The participants were reached through an online questionnaire that was duly promoted at a regional level, and they participated in a set of virtual co-design sessions in different focus groups. In total, 250 responses (56% were patients, relatives, or caretakers, and 44% were health and care professionals) were gathered, and they helped to complete the initial goal framework.

Next was the virtual co-design phase, consisting of the arrangement of a virtual workshop and the creation of different focus groups where representatives from the different target users of the Pharaon system were involved: health and care providers, older adults, and informal caregivers. In these workshops, different questions were posed regarding the CHF, target users, use cases, scenarios, and technologies. The discussions helped to confirm the goals and requirements identified and new ones appeared.

The outcomes of the three phases resulted in the definitive set of quality, functional, and emotional goals, and this was the basis for defining the three goal models of the Pharaon Murcia Pilot (Figure 3):


The results also helped in identifying, clarifying, and organizing the system requirements in the Murcia pilot through the definition of seven use cases and the associated technology requirements [29]:


**Figure 3.** Representation of the Pharaon Murcia Pilot goal model "Get involved in the health and care process" [29].

#### **4. Pharaon Murcia Pilot: Architecture and System Development**

The technical requirements identified in the use cases found in step 1 of the workplan led to the selection of the technologies to be implemented (see Table 1) to cover the two scenarios, the goal models and the use cases with the software applications and platforms required (see Figures 4 and 5).

**Table 1.** Technologies of the Pharaon Murcia pilot.




**Figure 4.** Overview of scenarios, technologies, and platform of the Pharaon Murcia pilot.

**Figure 5.** User interfaces of the technologies used in the Pharaon Murcia Pilot.

During step 2, technology providers worked on the adaptations of the technologies for the compliance of all users' needs identified in step 1 and for their integration into the Onesait Healthcare Data Platform, following the architecture agreed (see Figure 6).

For the six pilots, the architecture description was jointly carried out as a modelling exercise, having the defined use case scenarios and requirements for each pilot as the main input. Starting from the high-level abstraction and working towards adding more details, the explicit representation of the concept and the Pharaon ecosystem becomes clearer to everyone involved. It was decided that a technology-agnostic reference architecture model should be followed, focused on standard-based, non-AAL-specific, and somewhat recognized models, along with an adaptation of the 4 + 1 view model of architecture [36] with some additional views to ensure a high degree of coherence in the process of documenting the Pharaon architecture (see Figure 7).

Based on the experience and best practice of previous similar projects and initiatives [37–42], the Pharaon Reference Architecture is built on the common approach to "horizontal" functional layering that reflects the IoT implementations across various domains, and is expanded with two additional dimensions, cross-cutting functions, and properties.

Regarding the functional layers of the Murcia pilot architecture:


processing services. It comprises the storage and rule engines from Amicare, uGRID, and Smartband, and the Onesait Healthcare Data platform.


**Figure 6.** Murcia pilot high-level architecture.

**Figure 7.** Pharaon architecture view model.

Figure 6 represents the architecture of the Murcia pilot at a high level, showing the elements of each of the horizontal functional layers and their cross-cutting functions, which address additional functionalities that are not linked to a single layer but whose provision requires spanning across several layers. This includes security, privacy, reliability, etc. Table 2 lists the technical description of each technology, including the interactions, technologies, protocols, and security implemented.

**Table 2.** Interactions between the elements of the Murcia pilot architecture and their technologies, protocols, and security.


#### **5. Pharaon Murcia Pilot: Testing the Smart Healthcare Solution**

A two-stage approach was defined for testing the Pharaon system with target users in all pilots:

1. The prevalidation stage, consisting of some initial real-world validation at a small scale, where a reduced number of participants representing the different target users (older adults, informal caregivers, and health professionals) test the technologies described above. The goal is to collect the opinion of the users regarding the use of these technologies in specific situations and to analyze the key performance indicators (KPIs) focused on the users' level of autonomy, confidence, technology experience, usability, etc. These tests also help to detect bugs/problems/improvements and solve them before large-scale deployment.

2. The deployment of large-scale pilots with a significant number of target users testing the Pharaon system for a period of at least 12 months, where the impact is assessed at different levels according to a set of indicators agreed.

The present work includes the methods employed and results obtained during the prevalidation stage because, at the time of writing, the large-scale deployment had just started, and no significant data have been collected to evaluate the impact.

The six Pharaon pilots followed a common methodology described in a prevalidation protocol for all pilot sites to assess difficulties and willingness to use and for bug collection. For the Murcia pilot, this protocol comprised the following steps (see Figure 8):



**Table 3.** Inclusion and exclusion criteria defined for users involved in the Pharaon Murcia pilot.


**Table 3.** *Cont.*

<sup>1</sup> According to the clinical frailty scale of the Canadian Study on Health and Ageing (CSHA) living at home [43].

**Figure 8.** Prevalidation protocol followed by the six Pharaon pilots.




**Table 4.** *Cont.*

**Table 5.** Key performance indicators used during the prevalidation stage.


Regarding the KPIs agreed upon (see Table 5), the one focused on usage difficulties found by the users has been evaluated through the ASQ questionnaire, with three questions (see Table 6) and a seven-level scale for each one. For the analysis of the results, the rates equal to or higher than 5 were interpreted as no difficulty. The questionnaire also comprised a "Comments" section for registering any difficulties or positive feedback about the scenario evaluated. The KPI focused on willingness to use was assessed by the System Usability Scale (SUS) questionnaire (see Table 7), a method that allows us to obtain a general view of subjective assessments of usability for a wide variety of products and services, including hardware, software, mobile devices, websites, and applications. Due to the heterogeneity of ICT solutions that comprise the Pharaon pilots, SUS was the user-testing tool agreed upon by all partners in the Pharaon project, even though there is other research focused on healthcare solutions that use the popular Technology Acquisition Model (TAM) questionnaire as the tool to measure the technology acceptability [44].

**Table 6.** Sample questions in the After Scenario Questionnaire (ASQ).


The SUS scale comprises 10 items that participants had to score from 1 to 5 once the scenarios of each technology were finalized; the calculation of the results based on the participants' scores gives a general score between 0 and 100.

SUS Score = 2.5 × [(SUS1 + SUS3 + SUS5 + SUS7 + SUS9-5) + (25 − SUS2 − SUS4 − SUS6 − SUS8 − SUS10)]

Table 8 shows the general guideline on SUS Score interpretation provided by some authors [45]. The prevalidation protocol considered those solutions as accepted if the average score of SUS was graded as A (excellent) or B (Good).


**Table 8.** General guideline of SUS Score interpretation [45].

Finally, it is necessary to provide a deeper explanation of the way the steps 5–7 were performed during the prevalidation (see Figure 8):


It is important to remark that the second prevalidation phase was affected by COVID-19 restrictions, and some testing sessions were performed in hybrid mode following the premises of the Murcia health service.

#### **6. Results**

During the prevalidation stages, 44 people participated in testing sessions 6 (1) and 6 (2), namely:


Table 9 shows data from participants related to the following five variables: type of user, gender, year of birth, digital skills, and education level.


**Table 9.** Socio-demographic information of the recruited participants.


**Table 9.** *Cont.*

(\*) No experience, Some experience, Some experience with autonomy, Experienced/Proficient. (\*\*) EQF Level 1: Primary Education; EQF Level 2: Academic Secondary School Lower Cycle, New Secondary School, and Lower Secondary School; EQF Level 3: Academic Secondary School Upper Cycle, Intermediate and Higher VET (up to 3rd grade); EQF Level 4: Post-secondary non-tertiary education; EQF Level 5: Short-cycle tertiary education; EQF Level 6: Bachelor's Degree, Higher Apprenticeship; EQF Level 7: Master's Degree, postgraduate certificate and diplomas; EQF Level 8: Doctorate or Equivalent.

Note that, although the number of participants (samples) in the prevalidation phase may seem small, many practitioners in the industry have adopted the five-users rule as standard practice for user-testing, which points out that five participants are enough for getting a useful result for testing usability.

All participants filled in the ASQ and SUS. The statistical methodology used consisted of a set of sequential procedures for handling the qualitative and quantitative research data: collection, counting, presentation, synthesis, and analysis. The analysis was performed using descriptive and exploratory analysis. The descriptive analysis served to describe the set of data, thus obtaining the parameters that distinguish the characteristics of a set of data. The reasons for carrying out this analysis are that it allowed us to know, in detail, the information we had and to know the way in which the information was structured. It helps to make deductions directly from the data and parameters obtained. The exploratory analysis consisted of a set of statistical techniques whose purpose was to obtain a basic understanding of the data, allowing the detection of salient features, such as unexpected and outliers. In this work, the mean was the main statistical technique used. The mean scores and global mean for each questionnaire and question are summarized in Tables 10–13.

**Table 10.** ASQ mean scores for the individual technologies during the pre-validation phase 1.



**Table 10.** *Cont.*

**Table 11.** SUS mean scores for the individual technologies during the pre-validation phase 1.


In the first prevalidation phase, the mean score of the ASQ questions for each technology/scenario resulted in a value higher than 5 for all questions (see Table 10). Only one of nine patients (11.11%) scored questions ASQ1 and ASQ2 below 5 for Amicare scenario 1, considering the scenario somewhat difficult and not so fast to complete. The global mean for all questions reveals that almost all participants performed the requested scenarios with no difficulties, in an acceptable time, and felt supported throughout the process.

Regarding usability, the mean SUS scores obtained from all technologies (Table 10) were above 80.3, achieving an "A" grade (Excellent). Only two participants rated the smartband solution with 80 points.

From the results of the prevalidation phase 1, we concluded that the KPIs were reached for all technologies/scenarios, fulfilling the requisites of the first testing phase of prevalidation.

In the second prevalidation phase, the mean scores (Tables 12 and 13) were better than in the previous sessions in almost all technologies/scenarios, which means that participants are more satisfied with the solution in terms of difficulty and usability. From the eight patients that tested the MyHealth App module from Onesait Healthcare Data, the rates given to the SUS questions of only one patient (12.5%) achieved a score below 80.5. The six caregivers scored all individual ASQ questions with 5 or above, and for the Homecare module from Onesait Healthcare Data, at least one ASQ question from scenarios 5, 6, 9, 10, and 12 were scored as 4 by four professionals (28.5%). The rates given to the SUS questions of two health professionals (12.28%) received a score below 80.5.

Finally, it is important to mention that, during the first prevalidation phase, only two bugs (minor issues) were reported in Gitlab related to the smartband solution that were solved and the software was updated for step 6 (2). No bugs were reported during the second phase of pre-validation.

**Table 12.** ASQ mean scores for Onesait Healthcare Data with the integrated technologies during the pre-validation phase 2.


**Table 13.** SUS mean scores for Onesait Healthcare Data with the integrated technologies during the pre-validation phase 2.


#### **7. Conclusions and Future work**

This work presented an innovative AAL solution built upon novel and mature existing IoT technologies with the aim of providing a tele-assistance platform for CHF patients from the public health service of the region of Murcia in Spain. The solution has been designed with a user-centric approach, but also involves formal and informal caregivers and health professionals. From the authors' point of view, this work has been used as a useful guideline to provide the R&D community with inspiration for the design and deployment of AAL solutions based on heterogeneous IoT technologies, or similar approaches, for smart healthcare solutions in real healthcare institutions.

It is important to note some key points of the work presented, as follows. The starting point must be a set of needs identified. Normally, healthcare professionals or institutions are the ones who detect the need. In this work, the needs were detected by the healthcare institution of the Murcia region for CHF patients. After that, a workplan of well-defined steps must be agreed upon by all parties. It must include at least engineering of user requirements, design of the system architecture and development, and testing of the solution for a final step of large-scale deployment. For each step, a clear methodology must be defined and executed with a set of expected outputs identified, which will serve as input for the next step(s). The results obtained from each step must be analyzed, identifying the improvements to implement and lessons learnt.

Regarding the steps defined for this work in depth, it is important to highlight some important issues regarding timing, participants, key points of the execution, and lessons learnt. In the engineering of user requirements, at least six months of implementation were required, with the participation of representatives of all target users involved in the final AAL solution. In a healthcare solution such as the one presented, patients, formal and informal caregivers, and healthcare professionals are expected to be involved at least. Moreover, a high number of participants help to better define the co-design and representation of user requirements as goal models, use case scenarios, and user stories. In this work, up to 250 people participated in the different initiatives, workshops, and meetings organized to define them.

The step focused on architecture definition, development, and integration is a technical approach with a high load of software development. The duration will depend on the maturity of the technologies involved, the functional requirements, and the final healthcare product expected. In this work, this step was planned for one year of work and was focused on two sequential goals. First, the definition of a functional architecture with a set of functional layers and others spanned across several layers, such as security, privacy and reliability, defining the interactions and protocols to use. Second, the development and adaptations of three innovative IoT technologies—Amicare, Smartband, uGRID—and the Homecare and MyHealth App as modules of the Onesait Healthcare Data platform. The main lesson learnt was the importance of using open and interoperable interfaces. Although some IoT solutions are shown as plug and play, minor or major adoptions must almost always be carried out. Data privacy and security are also a must.

Regarding the prevalidation step of the AAL solution, the mandatory assessment of the functionality, usability, and acceptance of the solution by the final users is noteworthy. It entails the participation of all actors involved in this work: patients, formal and informal caregivers, and health professionals. Moreover, all technologies must be monitored during the prevalidation to ensure good performance. In this work, the prevalidation involved the testing of each stand-alone IoT technology and the testing of all IoT technologies integrated in the final healthcare platform solution. Over two months, the authors collected feedback on the tested solution in both phases with 44 participants involved. From the results, the authors concluded that, on average, the rate of acceptance and usability of all technologies and software solutions was higher than expected, and no major bugs or unexpected issues were detected. Then, the product will be ready to be launched in a large-scale pilot.

As a final conclusion, the steps executed, explained in detail in this paper, have been essential in utilizing the future actions that will improve the innovation of the healthcare platform solution presented at its current TRL of 6, to launch a final development in view of creating a large-scale pilot, where around 450 participants are expected in the Murcia region, and to design and develop the impact, exploitation, and business plan.

#### **8. Discussion**

Although the work presented has been performed to a high professional quality, following guidelines agreed under a H2020 European project framework, and the results of the work performed have been very positive and useful, it is also important to mention that the authors have identified in this contribution a set of limitations without undermining the quality and integrity of the research. In this section, we summarize them and discuss how they could impact the work and results, and we provide countermeasures and alternatives for future work if this applies.

This work has been performed thanks to the collaborative work of six different organizations and professionals of different disciplines. In addition, more than 300 participants have been reached to participate during the different stages of the research work, which involved difficult organization and coordination. The COVID-19 outbreak added more difficulties due to the impossibility of performing face-to-face meetings and other organizational problems, which resulted in all tasks being redesigned and rescheduled. Time constraints also affected the research negatively, setting as future work some tasks that could have been finished or that at least had sufficient results which could have been included at the time this contribution was written, e.g., the experience of the large-scale pilot under execution.

Some research/development limitations have been also identified during the development of the integrated ICT solution, due to the architecture complexity and high-quality requirements and/or the lack of knowledge of some specific programming languages mandatory for the final integration with the platforms provided by Indra-Minsait.

Data and statistics have also been a limiting factor of the research performed. During the prevalidation phase, 44 participants were reached. Although it is a sufficient sample size for analyzing usability using SUS, the use of other evaluation questionnaires may need more samples, or some researchers may even consider 44 a low number of participants in a testing phase of a large healthcare ICT solution.

The lack of similar research works in the scientific literature has also limited the comparison with similar approaches and the enrichment that a state of art offer to the research contributions.

Finally, we remark that the work developed has been an ad hoc solution for specific target users and healthcare providers, and may show a strong regional focus, limiting its impact. However, some regional, national, and international stakeholders have already shown interest in the work for replicating the solution, or part of it, for other healthcare systems or types of patients (e.g., palliative healthcare).

**Author Contributions:** Conceptualization, F.J.M.-M., M.V.B.-D., R.M.-C., R.M.-F., M.Á.B.-M., T.P.-M., A.L.B.-T., G.S.-N., R.P.-d.-Z. and M.Á.-L.; Data curation, F.J.M.-M., R.M.-C. and T.P.-M.; Formal analysis, F.J.M.-M., M.V.B.-D., R.M.-C., R.M.-F., M.Á.B.-M., T.P.-M., A.L.B.-T., G.S.-N., R.P.-d.-Z. and M.Á.-L.; Funding acquisition, F.J.M.-M. and M.V.B.-D.; Investigation, F.J.M.-M., R.M.-C. and M.Á.B.- M.; Methodology, F.J.M.-M., M.V.B.-D. and R.M.-C.; Project administration, F.J.M.-M. and M.V.B.-D.; Resources, F.J.M.-M., M.V.B.-D., R.M.-C., R.M.-F., M.Á.B.-M., T.P.-M., A.L.B.-T., G.S.-N., R.P.-d.-Z. and M.Á.-L.; Software, F.J.M.-M., M.V.B.-D., R.M.-C., R.M.-F., M.Á.B.-M., T.P.-M., A.L.B.-T., G.S.-N., R.P.-d.-Z. and M.Á.-L.; Supervision, F.J.M.-M., M.V.B.-D. and R.M.-F.; Validation, F.J.M.-M., M.V.B.-D., R.M.-C. and G.S.-N.; Visualization, F.J.M.-M., M.V.B.-D. and R.M.-F.; Writing—original draft, F.J.M.- M., M.V.B.-D. and R.M.-C.; Writing—review and editing, F.J.M.-M., M.V.B.-D. and R.M.-F. All authors have read and agreed to the published version of the manuscript.

**Funding:** Funding for this research is provided by the European Commission under the EU Horizon 2020 Pharaon Project 'Pilots for Healthy and Active Ageing', Grant Agreement no. 857188, Grant PID2020-112675RB-C41 funded by MCIN/AEI/10.13039/501100011033, GO2EDGE (Ref. RED2018- 02585-T) and Onofre-3 Grant PID2020-112675RB-C41 funded by MCIN/AEI/10.13039/501100011033. This publication is based upon work from COST Action CA16226 Indoor living Space Improvement: Smart Habitat for the Elderly supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu (accessed on 29 September 2022).

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki and approved by the Permanent Commission of the Internal Scientific Committee of the Biomedical Research Institute of Murcia (IMIB).

**Informed Consent Statement:** Informed consent was obtained from all the participants involved in the study.

**Data Availability Statement:** All the data reported in this work are private, including the software developments, available in the private repository GitLab https://gitlab.com/pharaongroup (accessed on 29 September 2022).

**Acknowledgments:** The authors acknowledge the support provided by the partners involved in the Murcian Pilot and the full consortium of the Pharaon project.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Review* **Can the Eight Hop Test Be Measured with Sensors? A Systematic Review**

**Luís Pimenta 1, Nuno M. Garcia <sup>2</sup> , Eftim Zdravevski <sup>3</sup> , Ivan Chorbev 3, Vladimir Trajkovik <sup>3</sup> , Petre Lameski <sup>3</sup> , Carlos Albuquerque 4,5,6 and Ivan Miguel Pires 1,2,\***


**Abstract:** Rehabilitation aims to increase the independence and physical function after injury, surgery, or other trauma, so that patients can recover to their previous ability as much as possible. To be able to measure the degree of recovery and impact of the treatment, various functional performance tests are used. The Eight Hop Test is a hop exercise that is directly linked to the rehabilitation of people suffering from tendon and ligament injuries on the lower limb. This paper presents a systematic review on the use of sensors for measuring functional movements during the execution of the Eight Hop Test, focusing primarily on the use of sensors, related diseases, and different methods implemented. Firstly, an automated search was performed on the publication databases: PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Secondly, the publications related to the Eight-Hop Test and sensors were filtered according to several search criteria and 15 papers were finally selected to be analyzed in detail. Our analysis found that the Eight Hop Test measurements can be performed with motion, force, and imaging sensors.

**Keywords:** Eight Hop Test; systematic review; measurement; sensors; diseases

#### **1. Introduction**

The Eight Hop Test is a hop exercise that consists of jumping with one leg in a circuit in the form of the number eight [1,2]. This test is helpful to evaluate the physical strength of individuals that suffered from some disease related to the lower limb [2,3].

Different kinds of sensors available in the market allow the measurement of patterns related to different movements [4–6]. It can handle the creation of automatic methods to the empowerment of the physical treatments [7–9]. These methods are important to give the same opportunities to rural environments in terms of treatments and monitoring of health conditions [10–12]. There are different types of sensors, but the sensors that are especially important for these measurements are the sensors for motion detection, which are available in commonly used mobile devices [13–15]. The positioning of these devices has different limitations, but it is relatively easy due to different support straps that allow these devices' statical position [16,17]. Therefore, the technology may help clinicians or scientists to study the detailed biomechanical parameters of jumping during rehabilitation programs as relevant variables for clinically significant scores and decide on the initiation of RTS with more confidence [18].

**Citation:** Pimenta, L.; Garcia, N.M.; Zdravevski, E.; Chorbev, I.; Trajkovik, V.; Lameski, P.; Albuquerque, C.; Pires, I.M. Can the Eight Hop Test Be Measured with Sensors? A Systematic Review. *Sensors* **2022**, *22*, 3582. https://doi.org/10.3390/ s22093582

Academic Editor: Andrea Cataldo

Received: 23 March 2022 Accepted: 6 May 2022 Published: 8 May 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

This review is included in a project related to the automation of the measurement of different results of the different physical functional tests, including the Heel-Rise Test [19,20], Timed-Up and Go Test [6,21], Ten Meter Walk Test [22], Six-Minute Walk Test [23], Functional Reach Test [24], 30-s Chair Stand Test [25,26], and Sit-to-Stand Test [27], where the development of different solutions involved in further studies. Furthermore, it is helpful and benefits the creation of Enhanced Living Environments [28,29]. The Eight Hop Test is mainly associated with different musculotendinous injuries, such as Cruciate Ligament of the Knee, Medial patellofemoral ligament, and Achilles tendon [18] and with injuries on the anterior cruciate ligament and gluteus medius [30,31].

The physical functional tests, such as bilateral or unilateral vertical and horizontal jump tests, require muscle strength and neuromuscular coordination for dynamic joint stability, which deteriorates with a knee injury [32,33]. In this regard, functional tests have been widely evaluated in laboratories using motion capture cameras, force platforms, and contact mattresses [32,34] to better understand biomechanical changes after knee injuries.

The monitoring of the biomechanics of the lower limbs during functional activities may help in the decision making related to the performance of sports or working activities after injury and prevention of osteoarthritis [35]. The analyzed test in this review allows the doctors to check the deficit of the significant underlying muscle deficits and ligament instability are still present throughout the post-operative rehabilitation period [35].

The study's purpose consists of a systematic review related to the measurement of the results of the Eight Hop Test with sensors, including motion force and imaging sensors. With the Eight Hop Test, the sensors can reach different results.

This paragraph ends the introductory section of this systematic review. Next, Section 2 describes research questions, inclusion criteria, search strategy, and analyzed study characteristics. Section 3 shows each study's results and summary. Third, in Section 4, we discuss and highlight the most critical points, and finally, Section 5 concludes the paper.

#### **2. Methodology**

#### *2.1. Research Questions*

The questions of this systematic review were focused on: (RQ1) Which devices can be used in the Eight Hop Test? (RQ2) Which data are related to the different types of diseases diagnosed by the Eight Hop Test? (RQ3) What are the benefits of implementing technological methods for measuring the results of the Eight Hop Tests?

#### *2.2. Inclusion Criteria*

The exercises and the sensors/equipment that had been used on the measurements of the Eight Hop Test results were based on the following inclusion criteria: (1) studies that measured different parameters related to the Eight Hop Test with sensors/equipment; (2) studies that used Eight Hop Test as the primary method; (3) studies that relate the Eight Hop Test with some diseases; (4) studies that clearly present results and population; (5) studies that were published between 2012 and 2021; (6) studies written in English.

#### *2.3. Search Strategy*

The Eight Hop Test consists of activities based on hopping used for rehabilitation purposes, and some studies only use a few of them. The search was performed with a Natural Language Processing (NLP)-based framework [36] in the following databases: IEEE Xplore; PMC; Pubmed Central; Springer; Association for Computing Machinery (ACM); Elsevier; and Multidisciplinary Digital Publishing Institute (MDPI). The keywords applied for this research were: "Eight hop test sensors"; "Eight hop test exercises"; and "hopping". Each study was filtered using the defined criteria presented in Section 2.2. The research was performed on 2 November 2021.

#### *2.4. Extraction of Study Characteristics*

There are specific parameters extracted from the studies. The information from the studies was divided and presented in Table 1 by the following terms: year of publication; population; the purpose of the studies; sensor/equipment; types of methods; and diseases. Some studies do not mention all the information required in Table 1, but they always gave precious data to help answer the questions in Section 2.1. The lack of information provided in the studies was compensated by contacting the respective authors of the analyzed studies. The information related to implementing the Eight Hop Test with technological equipment is limited, and this subject needs more research.


#### **Table 1.** Study analysis.


**Table 1.** *Cont*.

#### **3. Results**

#### *3.1. Summary of the Search Process Results*

As presented in Figure 1, the review has 9608 articles, 2770 of which are duplicates, and 6747 are marked as ineligible. For these reasons, the articles were removed correctly. The remaining 91 were filtered as well. In the filtering process (including the complete text evaluation), we found that 10 were Review/Survey, 61 were not related, one presented a quiz, two were not written in the English language, and two were not available. The remaining 15 papers were included in the qualitative synthesis and quantitative synthesis. In summary, we examined 15 scientific articles.

**Figure 1.** Flow diagram of identification and inclusion of papers.

Based on the results presented in Table 1, the analyzed studies were published between 2012 and 2021, reporting that the major part of the studies was published in 2021 (five studies), and, before that, only 10 studies are scarcely distributed between 2012 and 2020. By analyzing the location where the studies have been performed, a major part of the studies were performed in the United States of America (four studies), Australia (three studies), and United Kingdom (two studies), where the remaining studies are distributed by the globe. Regarding the sensors/equipment used, the most relevant used are cameras (five studies), reflective markers (four studies), force plates (four studies), electrodes (three studies), accelerometer (three studies), stopwatches (two studies), ultrasound scanner (two studies), metronome (two studies), and measuring tape (two studies), where the remaining sensors/equipment are used in only one study. Regarding the different types of diseases, only nine studies included people with specific diseases, including injury in an anterior cruciate ligament (four studies), Achilles tendon injury (two studies), bronchiectasis (one study), gluteus medius injury (one study), and Patellofemoral ligament injury (one study). In addition, all the studies used statistical and mathematical methods to prove the test's veracity.

#### *3.2. Main Results, Benefits and Limitations of the Selected Studies*

In Table 2, we summarize the main results, benefits and limitations of the selected studies relevant to measurement of the results of the Eight Hop Test.


#### **Table 2.** Study results and benefits.


**Table 2.** *Cont*.

#### *3.3. Qualitative Synthesis of the Most Relevant Works*

Baxter et al. [37] used four different types of sensors/equipment to implement several methods. The study used reflective markers, a 12-camera motion capture system, threeembedded force plates, and open-source musculoskeletal modeling software to perform the analysis. The study analyzes which rehabilitation exercises, such as single-leg hop, make a more robust and durable Achilles tendon. For that reason, eight young adults were put to the test. During the tests, enough data was collected to develop an exercise progression that helps increase the Achilles tendon's strength based on the magnitude duration and rate of tendon loading. In conclusion, peak Achilles tendon loads varied more than 12-fold, from 0.5 bodyweights during a seated hell raise to 7.3 bodyweights during a forward single leg hop.

In [38], the authors used motion and force sensors, such as accelerometer and dynamometer, to study which Hop Tests syncs better with isokinetic knee extensor strength, the deficits after an injury in the anterior cruciate ligament reconstruction, and which hop test correlates more with isokinetic knee extensor strength. Thirty-four males and sixteen females with surgery in the past 9–12 months (16–50 years of age) were assessed for the study. The hop tests presented in the study are single (SHD), triple (THD), and triple crossover (TCHD) hop for distance, six minute timed hop (6 MTH), single medial (MHD), and single lateral (LHD) hop for distance, single countermovement jump (SLCMJ) and timed speedy hop (SHT). The results show that specific hop tests such as single medial and

single countermovement jump correlated the most with isokinetic knee extensor when the more sophisticated testing equipment is lacking.

Ebert et al. [39] used an accelerometer and a stopwatch to find if the eight hop tests can identify limb asymmetry after anterior cruciate ligament reconstruction. For this study, fifty patients (34 males and 16 females) were assessed 9–12 months following anterior cruciate ligament reconstruction. The test was made in both limbs in a randomized order. These included single (SHD), triple (THD), and triple crossover (TCHD) hop for distance, six minute timed hop (6 MTH), single medial (MHD). Single lateral (LHD) hop for distance, single countermovement jump (SLCMJ), and timed speedy hop (TSHT). The results showed that single lateral hop, single medial hop, timed speedy hop, and single countermovement jump was the best physical exercises to demonstrate the functional limb asymmetry among the patients. In conclusion, if the purpose is to detect lingering functional deficits, it is recommended to incorporate the previous-hop test mentioned.

The authors of [40] tested the fundamental skill and physical activity of children with bronchiectasis using ActiGraph GT3x and an accelerometer to show if the performance is affected by the disease. Forty-six children with bronchiectasis (mean age 7.5 ± 2.6 years, 63% Male) were recruited from the Queensland Children's Hospital, Brisbane. The children were measured by normal quotidian activities like sedentary, light-intensity, games, walking, running, and moderate-to-vigorous activities. The results showed that children with bronchiectasis tend to delay their fundamental skills development. Fewer than 5% of children demonstrated mastery in the run, gallop, hop, and leap, while fewer than 10% demonstrated the ability to perform the two-handed strike, overarm throw, and underarm throw. Only eight of the 46 children (17.4%) achieved their age equivalency for locomotor skills, while just four (8.7%) completed their object control skills. It is important to note that children in their age equivalency had significantly more time in daily physical activity during the tests.

The authors of [41] used SECA portable stadiometer, Nikon video camera, and Windows Media Player 2013 to examine primary school children's fundamental movement skill proficiency levels. It recruited 219 participants (111 boys, 108 girls) aged between 7–10 years from three schools in central England to perform eight skills related to locomotor, object control, and stability skills. The eight fundamental skills involved run, jump, hop, skip, catch, overarm throw, underarm throw, and stability. The results find that any child could master all the fundamental skills mentioned. The conclusion says that to improve essential skills in all children, the effort should focus on stability skills (improving coordination) and force/power production.

In [42], the authors proposed an evaluation of a medial patellofemoral ligament using patient-reported measures and functional testing. For this study, 24 patients with a medical record between 2008 and 2013 were examined with a control group of uninjured persons of the same age and gender. The evaluation had two phases. In the first part, questionnaires evaluated the knee function based on the Tegner score, the knee injury and osteoarthritis outcome score (KOOS), the Lysholm score, SF-36, and EQ-5D-3L. The second part was the functional performance that involved square jump, steps down test, and the single-leg hop for distance. The results were: patients 11.5 sets for the square jump versus control 21 sets, 11.5 sets for the step-down test versus control 22 sets, and 77 cm for the single-leg hop for distance versus control 126 cm. The patients showed worse results than the control group in all tests, which led the study to conclude that patients with a medial patellofemoral ligament reconstruction do not regain normal knee function.

In [43], the authors examine the functional outcomes, including static-dynamic postural stability of patients with an associated gluteus medius treated injury. For this study, 16 patients were chosen with the clinical record (treated with an antegrade trochanteric IMN) between January 2009 and July 2013 and eight healthy male controls. Some data was gathered before the physical activity, including muscle strength, static and dynamic postural stability, and fall risk. The measurements included the participation of imaging sensors electromyography (EMG). The study results showed that patients with an

antegrade trochanteric IMN are more likely to have a good balance but poor functional performance. Still, more studies are needed to find the reason behind the results.

In [44], the author did not use any sensor to perform the test, which was evaluated by an examinator. The study considered 16 non-injured participants and 32 anterior cruciate ligament reconstruction participants. It was intended to examine the test-retest reliability of single-hop tests in the forward, medial and rotational direction and then detect limb asymmetries of the medial rotational hop tests, compared to forward hop tests made for the participants with a reconstructed anterior cruciate ligament. For the tests, they used some hop exercises like the single hop for distance (SH), triple hop for distance (TH), medial side triple hop for distance (MSTH), and 90◦ medial rotation hop for distance (MRH). The non-injured participants were tested twice, and the anterior cruciate ligament participants once. To prove the methods, it was calculated the intraclass correlation coefficients (ICCs), the standard errors of measurement (SEM), and the most negligible detectable differences (SDD). In the end, these exercises are reliable for rehabilitation purposes. Medial and rotational hope tests have the probability of showing limb asymmetries in a person with a reconstructed anterior cruciate ligament compared to the forward hope test.

In [45], the study's authors examine the best exercises (including hopping) in runners with Achilles tendinopathy based on self-reported pain. Fifteen male runners with Achilles tendinopathy were tested by loading the Achilles tendon with running, sprinting, hopping, jumping, and morning stiffness. The pain was measured before and after the workout with a numeric rating scale where zero means "no pain" and ten is "the worst possible pain". In total, 100% of the participants were recruited, 87% retention, and 93% followed-up. Exercise adherence was 70%. However, fidelity was 50%. Three participants suffered adverse events due to not following the advised exercises. Still, five participants were satisfied, and eight were very satisfied. In conclusion, the recommended education and training with pain-guided hopping positively impacts recreational runners with Achilles Tendinopathy.

Owusu-Akyaw et al. [46] used a magnetic resonance scanner to extract images from the knees before and after each subject performed a series of 60 single-legged hops. Then the images were converted into three-dimensional surface models of cartilage and bone to assess the cartilage characteristics in terms of thickness distribution. Eight male subjects with unilateral anterior cruciate ligament consented to participate in this study. The results found that the anterior cruciate ligament was associated with decreased patellar cartilage thickness by noticing that exercise would induce cartilage strain compared to the uninjured knees.

Reuter et al. [47] took the top German decathlon team, a group of eight professional athletes, to perform some high-end exercises to study postural control while exercising. Star Excursion Balance Test (SEBT), single hop for distance (SLH), crossover hop for distance (COH), triple hop for distance (TH) were used to perform the studies. The results demonstrated a correlation between the single-leg hop test and the star excursion balance test in terms of performance. These two exercises are the most efficient to determine overall postural control in athletes. For measurements, a measuring tape was used.

The author of [48] used Wireless electrodes, ultrasound probe, athletic tape, retroreflective markers, and MX03 + NIR Cameras to perform the studies in eight college-aged males with no musculoskeletal injury. The study's primary purpose was to investigate changes in plantar flexor contractile component length, plantar flexor muscle activity, and tendon length and how it could reduce mechanical efficiency during exhaustive hopping exercises. For the study, eight college-aged males with no musculoskeletal injury, neuromuscular disease, or functional limitation in their legs participated in a complete hopping exercise to the absolute limit. In that time, the data was collected and analyzed. The results found that the mechanical efficiency of hopping did not change and remained the same.

Wibawa et al. [49] used a Gait Laboratory (Dept of Rehabilitation Medicine, UMCG, The Netherlands) to perform the tests, including imaging and force sensors to analyze

muscle activities like normal walking, one-legged forward, and side jumping with a Musculoskeletal Modeling System. A nine-meter-long walkway, force plates, Vicon Motion System, cameras, reflective markers, and electrodes were used to measure and analyze ten healthy subjects (six males and four females) during the exercises. Each subject was evaluated, and then the values obtained by doing muscle activity were compared with the Musculoskeletal Modeling System. Some individuals were excluded during the study due to abnormal walking, marker trajectory error, and errors in market data. The other included three trials contributing enough data to conclude the investigation. The electrodes measured the right leg of the subjects. The correlation between sensors and the Eight Hop Test can be a game-changing move when the problem is finding how the injuries comport during the exercises. In conclusion, the study showed differences between the data and the model extracted from the Musculoskeletal Modeling System.

In [50], the authors used force plates, cameras, retro-reflective markers, and a digital metronome to analyze the center of pressure locations during two-legged hopping. By following the university ethics committee's approval, eight healthy and active adults (five females; three males) consented to participate in the study, doing at least ten jumps in a specific frequency. The attachment made the measurements of retro-reflective markers to a particular joint (metatarsophalangeal joint). The results showed that using retro-reflective markers in specific joints can determine the center of pressure during quiet standing and two-legged hopping at a particular frequency. Still, the results are limited to quiet standing and two-legged hopping in healthy adults. For that reason, more investigation is required to assure the accuracy of the method in walking and running or with clinical populations.

The authors of [51] used a population of fifteen college-age males, with right lower extremity dominance, to determine which exercises related to strength, endurance, flexibility, motor control, and function are more reliable in clinical measurements. It used Biodex System 3 pro and Biodex Balance System SD to collect some data before their studies began. For each test, there was a different exercise. Strength: eight isometric tests and a sit-up test. Endurance tests: the trunk flexor test, trunk extensor test, and bilateral side bridge tests. Flexibility tests: the sit-and-reach test and active range of the trunk and hip joint motions. Motor control: limb balance test and proprioception via passive reposition tests of the hips. Functional: squat test and single-leg hop test for time and distance. The results showed that endurance tests are the most reliable for clinical measurements, followed by flexibility, strength, motor control, and functionality.

#### *3.4. Relationship between Studies, Sensors and Diseases*

For this section, Table 3 represents a relation between sensors and diseases used to prove that studying different types of diseases directly related to the Eight Hop test is important. Still, the combination of the different methods and well-formed strategies has equal importance when monitoring people with various diseases using the Eight Hop test.


**Table 3.** Relation between diseases and sensors used.

#### **4. Discussion**

#### *4.1. Summary of Relationship between Sensors and Diseases*

The Eight Hop Test, specifically, was not present in any studies. However, the data collected from each study can help us understand which sensors are used in Hop Tests since the Eight Hop Test is a part of Hop Tests.

Some studies were based on problems related to a specific disease, and Figure 2 demonstrates various diseases that conducted the studies, where six studies were made with healthy people, four studies were performed with people with anterior cruciate ligament reconstruction, and the other studies included people with Achilles tendon (two studies) and patellofemoral ligament (two studies) as the leading injury. Gluteus Medius and Borchiectasis were mentioned in one publication each.

As presented in Table 4, the sensors available in the different studies were distributed in various categories, such as medical sensors, motion sensors, time counting equipment, imaging sensors, force sensors, and support equipment/consumables. Regarding the sensors used in the various studies, force sensors (four studies), such as force plates, dynamometer, Biodex System 3 pro, and Biodex Balance System SD had the most variety with four different sensors, followed by imaging sensors (seven studies), such as cameras, magnetic resonance scanner, and ultrasound scanner, and motion sensors (three studies), such as accelerometer, Vicon motion system, and Actigraph GT3x that had a variety of three different sensors.

**Figure 2.** Distribution of the various diseases by the studies.


#### *4.2. Relationship between Ages of Participants and Studies*

Regarding people's gender, the studies averaged 20.6 males and 12.8 females, including children and adults. Figure 3 presents the distribution of the ages of the different participants in the analyzed studies, where more than seven studies included individuals aged between 20 and 34 years old.

All studies used statistical and mathematical methods to study the results. The most used feature was the distance followed by time and the number of repetitions. Not all studies used sensors as the primary source of collecting data, where some of them were based on measuring distances and examining motion captures.

**Figure 3.** Relation of ages and number of studies.

#### *4.3. Final Remarks*

We can conclude that more studies are needed to develop a global solution for precise measurements. There is no proven evidence that the use of sensors in the Eight Hop test is essential, but according to the studies, it helps contribute to the fidelity and viability of the measurements.

After a deep analysis of the fifteen studies presented in this systematic review, we can find answers to our main questions. Regarding the RQ1, "Which devices can be used to perform studies in the Eight Hop Test?", we verified that the most common sensors used were the imaging sensors such as cameras, magnetic resonance, and ultrasound scanners. Furthermore, we have force sensors, time counting sensors, and motion sensors. More than half of the studies mention the need for support equipment/consumables, helping in the measuring, and completing the purpose of the sensors in these studies.

Concerning RQ2, "Which data are related to the different types of diseases diagnosed by the Eight Hop Test?", the analyzed studies show that different diseases require different sets of sensors and sensor data. For the same disease, for example Anterior cruciate ligament, different studies use different sensors. Additional research is needed to find out which sensor gives the best results, since no comparative analysis could be performed due to the varying experimental setups of the analyzed studies. Table 3 shows the relation between the sensors and the various diseases.

Finally, regarding RQ3, "What are the benefits of implementing technological methods for the measurements of the results of the Eight Hop Tests?", we verified that in addition to the limitations presented in Table 2 the studies showed some benefits related to rehabilitation and empowerment on the clinical information.

More studies are needed on this topic, but one thing is sure: we can use sensors to measure possible results prevenient from the Eight Hop Test.

#### **5. Conclusions**

In this review, a total of 15 studies were selected based on the inclusion criteria and thoroughly analyzed. The review identified which sensors are used in the Eight Hop Test, which are the most used sensors, the relevance of sensors in measurements, which diseases are related to the Eight Hop Test, and which methods can be used to perform the Eight Hop Test. It is important to mention that there is a lack of studies to develop a method for analyzing the Eight Hop Test with sensors. However, the sensors increase the viability of the measurements and help clinical teams to perform better diagnostics in health.

As future work, a mobile application will be developed to create a new method for the commodity measurement of the results of the Eight Hop Test that will be integrated with other ongoing studies related to the construction of a Personal Digital Life Coach.

**Author Contributions:** Conceptualization, L.P., N.M.G. and I.M.P.; methodology, L.P.; validation, N.M.G. and I.M.P.; formal analysis, L.P.; investigation, L.P.; data curation, L.P.; writing—original draft preparation, L.P., N.M.G. and I.M.P.; writing—review and editing, L.P., N.M.G., E.Z., I.C., P.L., V.T., C.A. and I.M.P.; supervision, N.M.G. and I.M.P.; funding acquisition, C.A., N.M.G. and I.M.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is funded by FCT/MEC through national funds and, when applicable, co-funded by the FEDER-PT2020 partnership agreement under the project UIDB/50008/2020. This work is also funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project UIDB/00742/2020.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This article is based upon work from COST Action IC1303-AAPELE—Architectures, Algorithms, and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. It boosts their research, career, and innovation. More information is available at www.cost.eu. Furthermore, we would like to thank the Politécnico de Viseu for their support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel. +41 61 683 77 34 Fax +41 61 302 89 18 www.mdpi.com

*Sensors* Editorial Office E-mail: sensors@mdpi.com www.mdpi.com/journal/sensors

MDPI St. Alban-Anlage 66 4052 Basel Switzerland Tel: +41 61 683 77 34

www.mdpi.com ISBN 978-3-0365-7027-3