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Advanced Sensors for Health Monitoring in Older Adults

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

Deadline for manuscript submissions: 25 September 2024 | Viewed by 8236

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Special Issue Editors

Dr. Aly Chkeir

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Guest Editor

LIST3N-Laboratoire d'Informatique et Société Numérique, Université de Technologie de Troyes (UTT), 10004 Troyes, France
Interests: IoT in Healthcare; AI in Healthcare; elderly care technology; balance assessment; grip strength measurement; gait analysis; functional decline assessment; mobility impairment; fall risk and prevention; balance disorders; bioimpedance in health assessment; health questionnaires; corrective interventions for elderly; data fusion in healthcare; alert systems for elderly; decision making in elderly care

Dr. Farah Mourad-Chehade

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Guest Editor

LIST3N-Laboratoire d'Informatique et Société Numérique, Université de Technologie de Troyes (UTT), 10004 Troyes, France
Interests: artificial intelligence; data mining; pattern recognition; wireless sensor networks; sensors localization; biomedical signal processing; prediction and diagnoses of pathologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The aging global population faces increasing health challenges that require continuous monitoring and assessment for the maintenance of well-being and independence. Advanced sensor technology; including wearable devices; ambient sensors; remote sensing; and bio-integrated systems; has revolutionized health monitoring systems for older adults; enabling the accurate tracking and evaluation of their health status. The Special Issue entitled "Advanced Sensors for Health Monitoring in Older Adults" aims to showcase the latest research and developments in this field.

This platform encourages researchers to present their most recent innovations in sensor-based health monitoring for older adults; highlighting cutting-edge approaches to assess physiological parameters; detect early signs of health deterioration; and support preventive interventions while respecting privacy and ethical considerations.

We invite the submission of original research papers; reviews; and perspectives that address the challenges and opportunities in this area. Topics of interest include, but are not limited to:

Wearable and ambient sensors for continuous and non-intrusive health monitoring in older adults.
Remote sensing technologies and bio-integrated sensors for the real-time tracking of physiological and biochemical parameters.
Advanced data processing; analysis; and fusion techniques for comprehensive health assessment.
Machine learning and artificial intelligence methods for predictive health monitoring and personalized care recommendations.
The integration of multi-sensor systems for a holistic view of older adults' health and the early detection of age-related conditions.
Applications of advanced sensors in the geriatric care; rehabilitation; and management of chronic conditions.
Ethical considerations; privacy; and security issues in health monitoring for older adults.
The performance evaluation and validation of advanced sensor systems for health monitoring.

We look forward to receiving your valuable contributions to this Special Issue and hope that it will inspire further research and innovation in the field of advanced sensors for health monitoring in older adults.

Dr. Aly Chkeir
Dr. Farah Mourad-Chehade
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

elderly care technology
IoT in healthcare
balance assessment tools
grip strength measurement
gait analysis
standardized geriatric evaluation
autonomy in aging
mobility impairment
fall risk and prevention
balance disorders
bioimpedance in health assessment
health questionnaires
corrective interventions for elderly
data fusion in healthcare
AI in healthcare
alert systems for elderly
decision making in elderly care

Published Papers (5 papers)

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Research

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16 pages, 1508 KiB

Open AccessArticle

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups

by Justine Hellec, Serge S. Colson, Amyn Jaafar, Olivier Guérin and Frédéric Chorin

Sensors 2024, 24(5), 1427; https://doi.org/10.3390/s24051427 - 22 Feb 2024

Viewed by 598

Abstract

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)

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28 pages, 8265 KiB

Open AccessArticle

A Novel Smart Shoe Instrumented with Sensors for Quantifying Foot Placement and Clearance during Stair Negotiation

by Malarvizhi Ram, Vasilios Baltzopoulos, Andy Shaw, Constantinos N. Maganaris, Jeff Cullen, Thomas O’Brien and Patryk Kot

Sensors 2023, 23(24), 9638; https://doi.org/10.3390/s23249638 - 5 Dec 2023

Cited by 1 | Viewed by 1107

Abstract

Trips and slips are significant causal perturbations leading to falls on stairs, especially in older people. The risk of a trip caused by a toe or heel catch on the step edge increases when clearance is small and variable between steps. The risk of a slip increases if the proportion of the foot area in contact with the step is reduced and variable between steps. To assess fall risk, these measurements are typically taken in a gait lab using motion-capture optoelectronic systems. The aim of this work was to develop a novel smart shoe equipped with sensors to measure foot placement and foot clearance on stairs in real homes. To validate the smart shoe as a tool for estimating stair fall risk, twenty-five older adults’ sensor-based measurements were compared against foot placement and clearance measurements taken in an experimental staircase in the lab using correlations and Bland–Altman agreement techniques. The results showed that there was a good agreement and a strong positive linear correlation for foot placement (r = 0.878, p < 0.000) and foot clearance (r = 0.967, p < 0.000) between sensor and motion analysis, offering promise for advancing the current prototype into a measurement tool for fall risk in real-life staircases. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)

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24 pages, 10247 KiB

Open AccessArticle

Multimodal Gait Abnormality Recognition Using a Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BiLSTM) Network Based on Multi-Sensor Data Fusion

by Jing Li, Weisheng Liang, Xiyan Yin, Jun Li and Weizheng Guan

Sensors 2023, 23(22), 9101; https://doi.org/10.3390/s23229101 - 10 Nov 2023

Cited by 1 | Viewed by 1482

Abstract

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor’s type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time–frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson’s disease severity, surpassing DCLSTM’s 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)

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24 pages, 3836 KiB

Open AccessArticle

Developing a Smartwatch-Based Healthcare Application: Notes to Consider

by Ramin Ramezani, Minh Cao, Arjun Earthperson and Arash Naeim

Sensors 2023, 23(15), 6652; https://doi.org/10.3390/s23156652 - 25 Jul 2023

Cited by 2 | Viewed by 2721

Abstract

Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)

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Review

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13 pages, 983 KiB

Open AccessReview

Predictability of Fall Risk Assessments in Community-Dwelling Older Adults: A Scoping Review

by N. F. J. Waterval, C. M. Claassen, F. C. T. van der Helm and E. van der Kruk

Sensors 2023, 23(18), 7686; https://doi.org/10.3390/s23187686 - 6 Sep 2023

Viewed by 1311

Abstract

Fall risk increases with age, and one-third of adults over 65 years old experience a fall annually. Due to the aging population, the number of falls and related medical costs will progressively increase. Correct prediction of who will fall in the future is necessary to timely intervene in order to prevent falls. Therefore, the aim of this scoping review is to determine the predictive value of fall risk assessments in community-dwelling older adults using prospective studies. A total of 37 studies were included that evaluated clinical assessments (questionnaires, physical assessments, or a combination), sensor-based clinical assessments, or sensor- based daily life assessments using prospective study designs. The posttest probability of falling or not falling was calculated. In general, fallers were better classified than non-fallers. Questionnaires had a lower predictive capability compared to the other assessment types. Contrary to conclusions drawn in reviews that include retrospective studies, the predictive value of physical tests evaluated in prospective studies varies largely, with only smaller-sampled studies showing good predictive capabilities. Sensor-based fall risk assessments are promising and improve with task complexity, although they have only been evaluated in relatively small samples. In conclusion, fall risk prediction using sensor data seems to outperform conventional tests, but the method’s validity needs to be confirmed by large prospective studies. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults)

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