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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 June 2022) | Viewed by 51150

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Special Issue Editors

Dr. Cristina P. Santos

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Center for MicroElectroMechanical Systems (CMEMS), University of Minho, 4710-057 Braga, Portugal
Interests: human motion; human locomotion; human–robot interactions and collaboration; medical devices; neuro-rehabilitation of patients suffering from motor problems by means of bio-inspired robotics and neuroscience technologies
Special Issues, Collections and Topics in MDPI journals

Dr. Joana Figueiredo

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Postdoctoral researcher, Center for MicroElectroMechanical Systems (CMEMS), University of Minho, Minho, Portugal
Interests: gait rehabilitation robotics; wearable motion sensors; gait analysis; human motion recognition
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human motion analysis has the potential to be applied as an objective assessment tool of human postural and gait conditions. This analysis is fundamental for assessing motor behaviors in daily living and motor impairments in neurological conditions. Specifically, in the clinical context, human motion analysis may (i) support clinical-based diagnosis with objective and timeless information allowing better treatment decisions, (ii) evaluate the improvements during rehabilitation in a more objective manner than the clinical scales, and (iii) recognize and predict risk situations.

Thanks to technological advances in the field of motion monitoring, new potential applications for human motion analysis are emerging, such as in sport activities, worker performance and ergonomics, telerehabilitation, elderly monitoring, and wellness.

In general, all activities that involve motion might benefit from wearable sensor systems. They are increasingly used for human motion given their unobtrusiveness, light weight, low cost, and ease of use for all-day and any-place use without interfering with the user’s motion. These key features have made these sensors suitable for long-term and outdoor ambulatory applications. The effectiveness of human motion analysis using wearable sensor-based systems depends on the robustness of both sensors and data collection protocols.

In this context, this Special Issue aims to connect researchers in the field of emerging wearable sensor-based systems for human motion applications, focusing on postural and gait analysis, in order to share ideas and conceptual approaches and to discuss the recent advances in this field, addressing innovative solutions and emerging issues. Research detailing experimental validation in relevant scenarios are encouraged.

We will accept full-length research articles and reviews focused on this research topic. Topics of interest include, but are not limited to, the following:

Posture and gait analysis;
Human daily motion analysis;
Gait analysis of elderly and disabled people;
Home care motion sensing and analysis;
Wearable sensors and related techniques for medical decision making;
Wearable sensors and related techniques for motor diagnosis;
Wearable sensors and related techniques for human gait recognition;
Sensing technologies for ambulatory human motion analysis;
Advanced sensor signal processing;
Health monitoring systems;
Industry-related wearable sensors;
Innovative applications of wearable sensor systems.

Dr. Cristina P. Santos
Dr. Joana Figueiredo
Guest Editors

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Published Papers (13 papers)

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20 pages, 5986 KiB

Open AccessArticle

A Kinematic Information Acquisition Model That Uses Digital Signals from an Inertial and Magnetic Motion Capture System

by Andrea Catherine Alarcón-Aldana, Mauro Callejas-Cuervo, Teodiano Bastos-Filho and Antônio Padilha Lanari Bó

Sensors 2022, 22(13), 4898; https://doi.org/10.3390/s22134898 - 29 Jun 2022

Cited by 2 | Viewed by 1851

Abstract

This paper presents a model that enables the transformation of digital signals generated by an inertial and magnetic motion capture system into kinematic information. First, the operation and data generated by the used inertial and magnetic system are described. Subsequently, the five stages of the proposed model are described, concluding with its implementation in a virtual environment to display the kinematic information. Finally, the applied tests are presented to evaluate the performance of the model through the execution of four exercises on the upper limb: flexion and extension of the elbow, and pronation and supination of the forearm. The results show a mean squared error of 3.82° in elbow flexion-extension movements and 3.46° in forearm pronation-supination movements. The results were obtained by comparing the inertial and magnetic system versus an optical motion capture system, allowing for the identification of the usability and functionality of the proposed model. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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14 pages, 1698 KiB

Open AccessArticle

Quantitative Assessment of Hand Function in Healthy Subjects and Post-Stroke Patients with the Action Research Arm Test

by Jesus Fernando Padilla-Magaña, Esteban Peña-Pitarch, Isahi Sánchez-Suarez and Neus Ticó-Falguera

Sensors 2022, 22(10), 3604; https://doi.org/10.3390/s22103604 - 10 May 2022

Cited by 7 | Viewed by 2667

Abstract

The Action Research Arm Test (ARAT) can provide subjective results due to the difficulty assessing abnormal patterns in stroke patients. The aim of this study was to identify joint impairments and compensatory grasping strategies in stroke patients with left (LH) and right (RH) hemiparesis. An experimental study was carried out with 12 patients six months after a stroke (three women and nine men, mean age: 65.2 ± 9.3 years), and 25 healthy subjects (14 women and 11 men, mean age: 40.2 ± 18.1 years. The subjects were evaluated during the performance of the ARAT using a data glove. Stroke patients with LH and RH showed significantly lower flexion angles in the MCP joints of the Index and Middle fingers than the Control group. However, RH patients showed larger flexion angles in the proximal interphalangeal (PIP) joints of the Index, Middle, Ring, and Little fingers. In contrast, LH patients showed larger flexion angles in the PIP joints of the Middle and Little fingers. Therefore, the results showed that RH and LH patients used compensatory strategies involving increased flexion at the PIP joints for decreased flexion in the MCP joints. The integration of a data glove during the performance of the ARAT allows the detection of finger joint impairments in stroke patients that are not visible from ARAT scores. Therefore, the results presented are of clinical relevance. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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16 pages, 3737 KiB

Open AccessArticle

Walk-IT: An Open-Source Modular Low-Cost Smart Rollator

by Manuel Fernandez-Carmona, Joaquin Ballesteros, Marta Díaz-Boladeras, Xavier Parra-Llanas, Cristina Urdiales and Jesús Manuel Gómez-de-Gabriel

Sensors 2022, 22(6), 2086; https://doi.org/10.3390/s22062086 - 8 Mar 2022

Cited by 7 | Viewed by 3727

Abstract

Rollators are widely used in clinical rehabilitation for gait assessment, but gait analysis usually requires a great deal of expertise and focus from medical staff. Smart rollators can capture gait parameters autonomously while avoiding complex setups. However, commercial smart rollators, as closed systems, can not be modified; plus, they are often expensive and not widely available. This work presents a low cost open-source modular rollator for monitorization of gait parameters and support. The whole system is based on commercial components and its software architecture runs over ROS2 to allow further customization and expansion. This paper describes the overall software and hardware architecture and, as an example of extended capabilities, modules for monitoring dynamic partial weight bearing and for estimation of spatiotemporal gait parameters of clinical interest. All presented tests are coherent from a clinical point of view and consistent with input data. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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19 pages, 4691 KiB

Open AccessArticle

Frequency-Domain sEMG Classification Using a Single Sensor

by Thekla Stefanou, David Guiraud, Charles Fattal, Christine Azevedo-Coste and Lucas Fonseca

Sensors 2022, 22(5), 1939; https://doi.org/10.3390/s22051939 - 2 Mar 2022

Cited by 6 | Viewed by 3249

Abstract

Working towards the development of robust motion recognition systems for assistive technology control, the widespread approach has been to use a plethora of, often times, multi-modal sensors. In this paper, we develop single-sensor motion recognition systems. Utilising the peripheral nature of surface electromyography (sEMG) data acquisition, we optimise the information extracted from sEMG sensors. This allows the reduction in sEMG sensors or provision of contingencies in a system with redundancies. In particular, we process the sEMG readings captured at the trapezius descendens and platysma muscles. We demonstrate that sEMG readings captured at one muscle contain distinct information on movements or contractions of other agonists. We used the trapezius and platysma muscle sEMG data captured in able-bodied participants and participants with tetraplegia to classify shoulder movements and platysma contractions using white-box supervised learning algorithms. Using the trapezius sensor, shoulder raise is classified with an accuracy of 99%. Implementing subject-specific multi-class classification, shoulder raise, shoulder forward and shoulder backward are classified with a 94% accuracy amongst object raise and shoulder raise-and-hold data in able bodied adults. A three-way classification of the platysma sensor data captured with participants with tetraplegia achieves a 95% accuracy on platysma contraction and shoulder raise detection. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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16 pages, 3751 KiB

Open AccessArticle

A Wearable System Based on Multiple Magnetic and Inertial Measurement Units for Spine Mobility Assessment: A Reliability Study for the Evaluation of Ankylosing Spondylitis

by Adriana Martínez-Hernández, Juan S. Perez-Lomelí, Ruben Burgos-Vargas and Miguel A. Padilla-Castañeda

Sensors 2022, 22(4), 1332; https://doi.org/10.3390/s22041332 - 10 Feb 2022

Cited by 3 | Viewed by 2858

Abstract

Spinal mobility assessment is essential for the diagnostic of patients with ankylosing spondylitis. BASMI is a routine clinical evaluation of the spine; its measurements are made with goniometers and tape measures, implying systematic errors, subjectivity, and low sensitivity. Therefore, it is crucial to develop better mobility assessment methods. The design, implementation, and evaluation of a novel system for assessing the entire spine’s motion are presented. It consists of 16 magnetic and inertial measurement units (MIMUs) communicated wirelessly with a computer. The system evaluates the patient’s movements by implementing a sensor fusion of the triaxial gyroscope, accelerometer, and magnetometer signals using a Kalman filter. Fifteen healthy participants were assessed with the system through six movements involving the entire spine to calculate continuous kinematics and maximum range of motion (RoM). The intrarater reliability was computed over the observed RoM, showing excellent reliability levels (intraclass correlation >0.9) in five of the six movements. The results demonstrate the feasibility of the system for further clinical studies with patients. The system has the potential to improve the BASMI method. To the best of our knowledge, our system involves the highest number of sensors, thus providing more objective information than current similar systems. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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15 pages, 3719 KiB

Open AccessArticle

Force Myography-Based Human Robot Interactions via Deep Domain Adaptation and Generalization

by Umme Zakia and Carlo Menon

Sensors 2022, 22(1), 211; https://doi.org/10.3390/s22010211 - 29 Dec 2021

Cited by 7 | Viewed by 2689

Abstract

Estimating applied force using force myography (FMG) technique can be effective in human-robot interactions (HRI) using data-driven models. A model predicts well when adequate training and evaluation are observed in same session, which is sometimes time consuming and impractical. In real scenarios, a pretrained transfer learning model predicting forces quickly once fine-tuned to target distribution would be a favorable choice and hence needs to be examined. Therefore, in this study a unified supervised FMG-based deep transfer learner (SFMG-DTL) model using CNN architecture was pretrained with multiple sessions FMG source data (D_s, T_s) and evaluated in estimating forces in separate target domains (D_t, T_t) via supervised domain adaptation (SDA) and supervised domain generalization (SDG). For SDA, case (i) intra-subject evaluation (D_s ≠ D_t-SDA, T_s ≈ T_t-SDA) was examined, while for SDG, case (ii) cross-subject evaluation (D_s ≠ D_t-SDG, T_s ≠ T_t-SDG) was examined. Fine tuning with few “target training data” calibrated the model effectively towards target adaptation. The proposed SFMG-DTL model performed better with higher estimation accuracies and lower errors (R² ≥ 88%, NRMSE ≤ 0.6) in both cases. These results reveal that interactive force estimations via transfer learning will improve daily HRI experiences where “target training data” is limited, or faster adaptation is required. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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19 pages, 1990 KiB

Open AccessArticle

The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions

by Mohsen Sharifi Renani, Abigail M. Eustace, Casey A. Myers and Chadd W. Clary

Sensors 2021, 21(17), 5876; https://doi.org/10.3390/s21175876 - 31 Aug 2021

Cited by 32 | Viewed by 6072

Abstract

Gait analysis based on inertial sensors has become an effective method of quantifying movement mechanics, such as joint kinematics and kinetics. Machine learning techniques are used to reliably predict joint mechanics directly from streams of IMU signals for various activities. These data-driven models require comprehensive and representative training datasets to be generalizable across the movement variability seen in the population at large. Bottlenecks in model development frequently occur due to the lack of sufficient training data and the significant time and resources necessary to acquire these datasets. Reliable methods to generate synthetic biomechanical training data could streamline model development and potentially improve model performance. In this study, we developed a methodology to generate synthetic kinematics and the associated predicted IMU signals using open source musculoskeletal modeling software. These synthetic data were used to train neural networks to predict three degree-of-freedom joint rotations at the hip and knee during gait either in lieu of or along with previously measured experimental gait data. The accuracy of the models’ kinematic predictions was assessed using experimentally measured IMU signals and gait kinematics. Models trained using the synthetic data out-performed models using only the experimental data in five of the six rotational degrees of freedom at the hip and knee. On average, root mean square errors in joint angle predictions were improved by 38% at the hip (synthetic data RMSE: 2.3°, measured data RMSE: 4.5°) and 11% at the knee (synthetic data RMSE: 2.9°, measured data RMSE: 3.3°), when models trained solely on synthetic data were compared to measured data. When models were trained on both measured and synthetic data, root mean square errors were reduced by 54% at the hip (measured + synthetic data RMSE: 1.9°) and 45% at the knee (measured + synthetic data RMSE: 1.7°), compared to measured data alone. These findings enable future model development for different activities of clinical significance without the burden of generating large quantities of gait lab data for model training, streamlining model development, and ultimately improving model performance. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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16 pages, 3504 KiB

Open AccessArticle

Towards Human Motion Tracking Enhanced by Semi-Continuous Ultrasonic Time-of-Flight Measurements

by Silje Ekroll Jahren, Niels Aakvaag, Frode Strisland, Andreas Vogl, Alessandro Liberale and Anders E. Liverud

Sensors 2021, 21(7), 2259; https://doi.org/10.3390/s21072259 - 24 Mar 2021

Cited by 7 | Viewed by 3353

Abstract

Human motion analysis is a valuable tool for assessing disease progression in persons with conditions such as multiple sclerosis or Parkinson’s disease. Human motion tracking is also used extensively for sporting technique and performance analysis as well as for work life ergonomics evaluations. Wearable inertial sensors (e.g., accelerometers, gyroscopes and/or magnetometers) are frequently employed because they are easy to mount and can be used in real life, out-of-the-lab-settings, as opposed to video-based lab setups. These distributed sensors cannot, however, measure relative distances between sensors, and are also cumbersome when it comes to calibration and drift compensation. In this study, we tested an ultrasonic time-of-flight sensor for measuring relative limb-to-limb distance, and we developed a combined inertial sensor and ultrasonic time-of-flight wearable measurement system. The aim was to investigate if ultrasonic time-of-flight sensors can supplement inertial sensor-based motion tracking by providing relative distances between inertial sensor modules. We found that the ultrasonic time-of-flight measurements reflected expected walking motion patterns. The stride length estimates derived from ultrasonic time-of-flight measurements corresponded well with estimates from validated inertial sensors, indicating that the inclusion of ultrasonic time-of-flight measurements could be a feasible approach for improving inertial sensor-only systems. Our prototype was able to measure both inertial and time-of-flight measurements simultaneously and continuously, but more work is necessary to merge the complementary approaches to provide more accurate and more detailed human motion tracking. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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17 pages, 571 KiB

Open AccessArticle

Identification of Patients with Sarcopenia Using Gait Parameters Based on Inertial Sensors

by Jeong-Kyun Kim, Myung-Nam Bae, Kang Bok Lee and Sang Gi Hong

Sensors 2021, 21(5), 1786; https://doi.org/10.3390/s21051786 - 4 Mar 2021

Cited by 20 | Viewed by 3866

Abstract

Sarcopenia can cause various senile diseases and is a major factor associated with the quality of life in old age. To diagnose, assess, and monitor muscle loss in daily life, 10 sarcopenia and 10 normal subjects were selected using lean mass index and grip strength, and their gait signals obtained from inertial sensor-based gait devices were analyzed. Given that the inertial sensor can measure the acceleration and angular velocity, it is highly useful in the kinematic analysis of walking. This study detected spatial-temporal parameters used in clinical practice and descriptive statistical parameters for all seven gait phases for detailed analyses. To increase the accuracy of sarcopenia identification, we used Shapley Additive explanations to select important parameters that facilitated high classification accuracy. Support vector machines (SVM), random forest, and multilayer perceptron are classification methods that require traditional feature extraction, whereas deep learning methods use raw data as input to identify sarcopenia. As a result, the input that used the descriptive statistical parameters for the seven gait phases obtained higher accuracy. The knowledge-based gait parameter detection was more accurate in identifying sarcopenia than automatic feature selection using deep learning. The highest accuracy of 95% was achieved using an SVM model with 20 descriptive statistical parameters. Our results indicate that sarcopenia can be monitored with a wearable device in daily life. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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27 pages, 5708 KiB

Open AccessArticle

Evaluation of Inertial Sensor Data by a Comparison with Optical Motion Capture Data of Guitar Strumming Gestures

by Sérgio Freire, Geise Santos, Augusto Armondes, Eduardo A. L. Meneses and Marcelo M. Wanderley

Sensors 2020, 20(19), 5722; https://doi.org/10.3390/s20195722 - 8 Oct 2020

Cited by 12 | Viewed by 4250

Abstract

Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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15 pages, 2050 KiB

Open AccessArticle

Artificial Neural Networks in Motion Analysis—Applications of Unsupervised and Heuristic Feature Selection Techniques

by Marion Mundt, Arnd Koeppe, Franz Bamer, Sina David and Bernd Markert

Sensors 2020, 20(16), 4581; https://doi.org/10.3390/s20164581 - 15 Aug 2020

Cited by 25 | Viewed by 4632

Abstract

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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24 pages, 6012 KiB

Open AccessArticle

Wearable Inertial Sensor System towards Daily Human Kinematic Gait Analysis: Benchmarking Analysis to MVN BIOMECH

by Joana Figueiredo, Simão P. Carvalho, João Paulo Vilas-Boas, Luís M. Gonçalves, Juan C. Moreno and Cristina P. Santos

Sensors 2020, 20(8), 2185; https://doi.org/10.3390/s20082185 - 12 Apr 2020

Cited by 14 | Viewed by 5385

Abstract

This paper presents a cost- and time-effective wearable inertial sensor system, the InertialLAB. It includes gyroscopes and accelerometers for the real-time monitoring of 3D-angular velocity and 3D-acceleration of up to six lower limbs and trunk segment and sagittal joint angle up to six joints. InertialLAB followed an open architecture with a low computational load to be executed by wearable processing units up to 200 Hz for fostering kinematic gait data to third-party systems, advancing similar commercial systems. For joint angle estimation, we developed a trigonometric method based on the segments’ orientation previously computed by fusion-based methods. The validation covered healthy gait patterns in varying speed and terrain (flat, ramp, and stairs) and including turns, extending the experiments approached in the literature. The benchmarking analysis to MVN BIOMECH reported that InertialLAB provides more reliable measures in stairs than in flat terrain and ramp. The joint angle time-series of InertialLAB showed good waveform similarity (>0.898) with MVN BIOMECH, resulting in high reliability and excellent validity. User-independent neural network regression models successfully minimized the drift errors observed in InertialLAB’s joint angles (NRMSE < 0.092). Further, users ranked InertialLAB as good in terms of usability. InertialLAB shows promise for daily kinematic gait analysis and real-time kinematic feedback for wearable third-party systems. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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10 pages, 788 KiB

Open AccessBrief Report

An Instrumented Assessment of a Rhythmic Finger Task among Children with Motor Coordination Difficulties

by Artal Keinan, Tami Bar-Shalita and Sigal Portnoy

Sensors 2020, 20(16), 4554; https://doi.org/10.3390/s20164554 - 14 Aug 2020

Cited by 1 | Viewed by 3211

Abstract

Background: Coordination is crucial for motor function, yet objective clinical evaluations are limited. We therefore developed and tested the reliability and validity of a low-cost sensorized evaluation of a rhythmic finger task. Methods: Children with coordination difficulties (n = 24) and typically developing children (n = 24) aged from 5 to 7 years performed the Sensorized Finger Sequencing Test (SFST), a finger sequencing test that records the correct sequence, total time, and the standard deviation (SD) of touch time. Additionally, motor performance tests and parents’ reports were applied in order to test the reliability and validity of the SFST. Results: The study group had significantly greater thumb-finger test scores—total time in the dominant hand (p = 0.035) and the SD of the touch time in both dominant (p = 0.036) and non-dominant (p = 0.032) hands. Motor performance tests were not correlated with the SFST. Test–retest reliability in 10 healthy children was found for the SD of touch time in the dominant hand (r = 0.87, p = 0.003). Conclusions: The SFST was successful in assessing the movement pattern variability reported in children with motor difficulties. This exploratory study indicates that the low-cost SFST could be utilized as an objective measure for the assessment of proprioception components, which currently are overlooked by standardized motor performance assessments. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Motion Analysis)

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