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Wearable Devices for Physical Activity and Healthcare Monitoring

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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: 31 December 2025 | Viewed by 13071

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Special Issue Editor

Dr. Yehuda Weizman

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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
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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

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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 (7 papers)

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Research

18 pages, 3184 KB

Open AccessArticle

Boxing Punch Detection and Classification Using Motion Tape and Machine Learning

by Shih-Chao Huang, Taylor Pierce, Yun-An Lin and Kenneth J. Loh

Sensors 2025, 25(16), 5027; https://doi.org/10.3390/s25165027 - 13 Aug 2025

Viewed by 1135

Abstract

The objective of this study is to classify the types of boxing punches using machine learning algorithms that processed skin-strain time history measurements from a self-adhesive, elastic fabric, wearable sensor called Motion Tape. A human participant study was designed to capture movements during boxing training. Subjects were asked to perform multiple sets of punches during the entire test, which consisted of jabs and hooks with and without striking a heavy bag. The collected Motion Tape data was used to train and compare time series classification algorithms to identify the types of punches performed and associated conditions. The results demonstrated that Motion Tape, in combination with machine learning techniques, could effectively classify different punch types based on skin-strain measurements. These findings highlighted the potential of the system as an effective tool for human performance analysis in sports and biomechanics applications. Full article

(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)

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16 pages, 2107 KB

Open AccessArticle

Determination of Spatiotemporal Gait Parameters Using a Smartphone’s IMU in the Pocket: Threshold-Based and Deep Learning Approaches

by Seunghee Lee, Changeon Park, Eunho Ha, Jiseon Hong, Sung Hoon Kim and Youngho Kim

Sensors 2025, 25(14), 4395; https://doi.org/10.3390/s25144395 - 14 Jul 2025

Cited by 1 | Viewed by 1602

Abstract

This study proposes a hybrid approach combining threshold-based algorithm and deep learning to detect four major gait events—initial contact (IC), toe-off (TO), opposite initial contact (OIC), and opposite toe-off (OTO)—using only a smartphone’s built-in inertial sensor placed in the user’s pocket. The algorithm enables estimation of spatiotemporal gait parameters such as cadence, stride length, loading response (LR), pre-swing (PSw), single limb support (SLS), double limb support (DLS), and swing phase and symmetry. Gait data were collected from 20 healthy individuals and 13 hemiparetic stroke patients. To reduce sensitivity to sensor orientation and suppress noise, sum vector magnitude (SVM) features were extracted and filtered using a second-order Butterworth low-pass filter at 3 Hz. A deep learning model was further compressed using knowledge distillation, reducing model size by 96% while preserving accuracy. The proposed method achieved error rates in event detection below 2% of the gait cycle for healthy gait and a maximum of 4.4% for patient gait in event detection, with corresponding parameter estimation errors also within 4%. These results demonstrated the feasibility of accurate and real-time gait monitoring using a smartphone. In addition, statistical analysis of gait parameters such as symmetry and DLS revealed significant differences between the normal and patient groups. While this study is not intended to provide or guide rehabilitation treatment, it offers a practical means to regularly monitor patients’ gait status and observe gait recovery trends over time. Full article

(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)

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18 pages, 1005 KB

Open AccessArticle

FedEach: Federated Learning with Evaluator-Based Incentive Mechanism for Human Activity Recognition

by Hyun Woo Lim, Sean Yonathan Tanjung, Ignatius Iwan, Bernardo Nugroho Yahya and Seok-Lyong Lee

Sensors 2025, 25(12), 3687; https://doi.org/10.3390/s25123687 - 12 Jun 2025

Cited by 1 | Viewed by 911

Abstract

Federated learning (FL) is a decentralized approach that aims to establish a global model by aggregating updates from diverse clients without sharing their local data. However, the approach becomes complicated when Byzantine clients join with arbitrary manipulation, referred to as malicious clients. Classical techniques, such as Federated Averaging (FedAvg), are insufficient to incentivize reliable clients and discourage malicious clients. Other existing Byzantine FL schemes to address malicious clients are either incentive-reliable clients or need-to-provide server-labeled data as the public validation dataset, which increase time complexity. This study introduces a federated learning framework with an evaluator-based incentive mechanism (FedEach) that offers robustness with no dependency on server-labeled data. In this framework, we introduce evaluators and participants. Unlike the existing approaches, the server selects the evaluators and participants among the clients using model-based performance evaluation criteria such as test score and reputation. Afterward, the evaluators assess and evaluate whether a participant is reliable or malicious. Subsequently, the server exclusively aggregates models from these identified reliable participants and the evaluators for global model updates. After this aggregation, the server calculates each client’s contribution, prioritizing each client’s contribution to ensure the fair recognition of high-quality updates and penalizing malicious clients based on their contributions. Empirical evidence obtained from the performance in human activity recognition (HAR) datasets highlights FedEach’s effectiveness, especially in environments with a high presence of malicious clients. In addition, FedEach maintains computational efficiency so that it is reliable for efficient FL applications such as sensor-based HAR with wearable devices and mobile sensing. Full article

(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)

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21 pages, 5284 KB

Open AccessArticle

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

Cited by 1 | Viewed by 824

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 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 KB

Open AccessArticle

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 1919

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 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 KB

Open AccessArticle

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

Cited by 5 | Viewed by 2277

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 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 KB

Open AccessArticle

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 3182

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 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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