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Wearable Devices for Physical Activity and Healthcare Monitoring
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Wearable Devices for Physical Activity and Healthcare Monitoring

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 4088

Special Issue Editor

Dr. Yehuda Weizman

E-Mail Website
Guest Editor
Faculty of Engineering Science, University of Bayreuth, D-95440 Bayreuth, Germany
Interests: sensors; smart equipment; wearable technology; sports engineering; data analytics product innovation; electronics design; gait analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors have received much attention following the recent technological advances and the arrival of mobile devices. Their proven ability as an essential clinical tool provides unique insights into different physical activity and healthcare monitoring applications, both in day-to-day and controlled environments. These devices have become increasingly capable of collecting accurate data, and have demonstrated computing capabilities, including AI and machine learning. Now, more than ever, wearable technologies provide fertile ground for countless applications in public health, sedentary behaviour, remote clinical monitoring, and digital healthcare.

We welcome research studies, as well as review manuscripts, focusing on the application of wearables for objective recognition, which might include, but is not limited to, the following topics:

New keywords:

  • Physiological disorders;
  • Disability;
  • Orthopaedics;
  • Epidemiology;
  • Public health;
  • Detection;
  • Prevention;
  • Sedentary behaviour;
  • Digital healthcare;
  • Wearable motion and pressure sensors;
  • Textile sensors for human movement

Dr. Yehuda Weizman
Guest Editor

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

  • physiological disorders
  • disability
  • orthopaedics
  • epidemiology
  • public health
  • detection
  • prevention
  • sedentary behaviour
  • digital healthcare
  • wearable motion and pressure sensors
  • textile sensors for human movement

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Published Papers (4 papers)

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Research

21 pages, 5284 KiB  
Article
Validity of a Single Inertial Measurement Unit to Measure Hip Range of Motion During Gait in Patients Undergoing Total Hip Arthroplasty
by Noor Alalem, Xavier Gasparutto, Kevin Rose-Dulcina, Peter DiGiovanni, Didier Hannouche and Stéphane Armand
Sensors 2025, 25(11), 3363; https://doi.org/10.3390/s25113363 - 27 May 2025
Viewed by 254
Abstract
Hip flexion range of motion (ROM) during gait is an important surgery outcome for patients undergoing total hip arthroplasty (THA) that could help patient monitoring and rehabilitation. To allow systematic measurements during patients’ clinical pathways, hip ROM measurement should be as simple and [...] Read more.
Hip flexion range of motion (ROM) during gait is an important surgery outcome for patients undergoing total hip arthroplasty (THA) that could help patient monitoring and rehabilitation. To allow systematic measurements during patients’ clinical pathways, hip ROM measurement should be as simple and cheap as possible to ensure patient and clinician acceptance. Single IMU options can match these requirements and offer measurements both during daily living conditions and standardized clinical tests (e.g., 10 m walk, timed up-and-go). However, single-IMU approaches to measure hip ROM have been limited. Thus, the objective of this study was to explore the accuracy of one IMU in measuring hip ROM during gait and to determine whether a single-IMU approach can provide results comparable to those of multi-IMU systems. To assess this, machine learning models were employed, ranging from the simplest (linear regression) to more complex approaches (artificial neural networks). Eighteen patients undergoing THA and seven controls were measured using a 3D opto-electronic motion capture system and one thigh-mounted IMU. Hip ROM was predicted from thigh ROM using regression and classification models and was compared to the reference hip ROM. Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. These results showed that one IMU can measure hip ROM with errors comparable to those of two-IMU methods, with potential for improvement. Using multiple linear regression was sufficient and more appropriate than employing complex ANN models. This approach offers simplicity and acceptance to users in clinical settings. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
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11 pages, 1245 KiB  
Article
Estimation of 3D Ground Reaction Force and 2D Center of Pressure Using Deep Learning and Load Cells Across Various Gait Conditions
by Junggil Kim, Ki-Cheon Kim, Gyerae Tack and Jin-Seung Choi
Sensors 2025, 25(11), 3357; https://doi.org/10.3390/s25113357 - 26 May 2025
Viewed by 355
Abstract
Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (GRF) and two-axis center of pressure (CoP) using a [...] Read more.
Traditional force plate-based systems offer high measurement precision but are limited to laboratory settings, restricting their use in real-world environments. To address this, we propose a method for estimating a three-axis ground reaction force (GRF) and two-axis center of pressure (CoP) using a shoe embedded with three uniaxial load cells. The estimation was conducted under five gait conditions: straight walking, turning, uphill, downhill, and running. Data were collected from 40 healthy young adults. Four deep-learning models—Fully Connected Neural Network (FCNN), Convolutional Neural Network (CNN), Sequence-to-Sequence Long Short-Term Memory (Seq2Seq-LSTM), and Transformer—were evaluated. Among them, Seq2Seq-LSTM and CNN achieved the highest performance in predicting both GRF and CoP. However, the medio-lateral (ML) components showed lower accuracy than the vertical and anterior–posterior directions. In slope conditions, particularly for vertical GRF, relatively higher root mean-square error (RMSE) values were observed. Despite some variation across gait types, predicted values showed high agreement with measurements. Compared with previous studies, the proposed method achieved comparable or better performance with a minimal sensor setup. These findings highlight the feasibility of accurate GRF and CoP estimation in diverse gait scenarios and support the potential for real-world applications. Future work will focus on sensor optimization and broader population validation. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
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29 pages, 34115 KiB  
Article
Sliding-Window CNN + Channel-Time Attention Transformer Network Trained with Inertial Measurement Units and Surface Electromyography Data for the Prediction of Muscle Activation and Motion Dynamics Leveraging IMU-Only Wearables for Home-Based Shoulder Rehabilitation
by Aoyang Bai, Hongyun Song, Yan Wu, Shurong Dong, Gang Feng and Hao Jin
Sensors 2025, 25(4), 1275; https://doi.org/10.3390/s25041275 - 19 Feb 2025
Viewed by 1091
Abstract
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention [...] Read more.
Inertial Measurement Units (IMUs) are widely utilized in shoulder rehabilitation due to their portability and cost-effectiveness, but their reliance on spatial motion data restricts their use in comprehensive musculoskeletal analyses. To overcome this limitation, we propose SWCTNet (Sliding Window CNN + Channel-Time Attention Transformer Network), an advanced neural network specifically tailored for multichannel temporal tasks. SWCTNet integrates IMU and surface electromyography (sEMG) data through sliding window convolution and channel-time attention mechanisms, enabling the efficient extraction of temporal features. This model enables the prediction of muscle activation patterns and kinematics using exclusively IMU data. The experimental results demonstrate that the SWCTNet model achieves recognition accuracies ranging from 87.93% to 91.03% on public temporal datasets and an impressive 98% on self-collected datasets. Additionally, SWCTNet exhibits remarkable precision and stability in generative tasks: the normalized DTW distance was 0.12 for the normal group and 0.25 for the patient group when using the self-collected dataset. This study positions SWCTNet as an advanced tool for extracting musculoskeletal features from IMU data, paving the way for innovative applications in real-time monitoring and personalized rehabilitation at home. This approach demonstrates significant potential for long-term musculoskeletal function monitoring in non-clinical or home settings, advancing the capabilities of IMU-based wearable devices. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
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11 pages, 454 KiB  
Article
Effectiveness of a Telerehabilitation-Based Exercise Program in Patients with Chronic Neck Pain—A Randomized Clinical Trial
by Laura Guerra-Arencibia, Cristina Santana-Déniz, Daniel Pecos-Martín, Samuel Fernández-Carnero, Nerea de Miguel-Hernando, Alexander Achalandabaso-Ochoa and Daniel Rodríguez-Almagro
Sensors 2024, 24(24), 8069; https://doi.org/10.3390/s24248069 - 18 Dec 2024
Viewed by 1784
Abstract
Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals’ quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results. This study [...] Read more.
Background: Non-specific chronic neck pain is a prevalent musculoskeletal disorder with a significant impact on individuals’ quality of life. The lack of consensus on effective therapeutic management complicates the establishment of standardized treatment protocols. Home exercise programs have yielded positive results. This study aimed to assess the effectiveness of a telerehabilitation program distributed through videoconferencing for patients with non-specific chronic neck pain compared to a home-based exercise program. Methods: A randomized controlled trial was conducted involving 36 participants who were divided into two groups: the experimental group (n = 18) received manual therapy combined with telerehabilitation, while the home-based group (n = 18) received the same manual therapy treatment along with recommendations for home exercises. Key outcome measures, including neck-related disability, kynesiophobia, anxiety and depression, pain intensity, pressure pain threshold, quality of life, and adherence to self-treatment, were evaluated at baseline and post-treatment. Results: No statistically significant differences were observed between groups. However, both groups demonstrated improvements in all study variables except for the mental component of quality of life immediately post-treatment. Conclusions: After eight weeks of manual therapy and exercise, both the telerehabilitation and home-based exercise programs resulted in significant improvements in disability, pain, and kynesiophobia, indicating that telerehabilitation is as effective as home-based exercise. Full article
(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)
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