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Electronics
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Wearable Sensors for Human Position, Attitude and Motion Tracking
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Wearable Sensors for Human Position, Attitude and Motion Tracking

A special issue of Electronics (ISSN 2079-9292).

Deadline for manuscript submissions: 15 October 2025 | Viewed by 2029

Special Issue Editor

Dr. Lei Jing

E-Mail Website
Guest Editor
Graduate School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizuwakamatsu 965-8580, Japan
Interests: wearable devices; AI; communication and network engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in wearable sensor technologies have revolutionized the way we monitor and analyse human movement, enabling breakthroughs across various domains, including healthcare, sports, human–computer interaction, and robotics. Wearable sensors, such as inertial measurement units (IMUs), tactile sensors, optical sensors, and biosensors, have become essential tools for tracking human position, attitude, and motion, with their high precision, portability, and real-time capabilities. These technologies offer unprecedented opportunities for personalized monitoring, rehabilitation, and performance optimization in dynamic and diverse environments.

This Special Issue of Electronics focuses on the design, implementation, and applications of wearable sensor systems for human position, attitude, and motion tracking. It seeks to highlight cutting-edge research and interdisciplinary approaches to addressing challenges in sensor design, data processing, and integration.

Dr. Lei Jing
Guest Editor

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • wearable sensor design
  • data glove
  • e-textile sensor
  • multimodal sensing
  • data fusion
  • daily activity recognition
  • motion tracking
  • deep data processing
  • machine learning in motion tracking
  • position tracking
  • IMU
  • gait analysis
  • biomechanics
  • real-time tracking system
  • body area networks
  • smart clothing
  • signal processing for wearables

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Published Papers (5 papers)

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Research

27 pages, 3511 KiB  
Article
A Distributed Wearable Computing Framework for Human Activity Classification
by Jhonathan L. Rivas-Caicedo, Kevin Niño-Tejada, Laura Saldaña-Aristizabal and Juan F. Patarroyo-Montenegro
Electronics 2025, 14(16), 3203; https://doi.org/10.3390/electronics14163203 - 12 Aug 2025
Viewed by 32
Abstract
Human Activity Recognition (HAR) using wearable sensors plays a critical role in applications such as healthcare, sports monitoring, and rehabilitation. Traditional approaches typically rely on centralized models that aggregate and process data from multiple sensors simultaneously. However, such architecture often suffers from high [...] Read more.
Human Activity Recognition (HAR) using wearable sensors plays a critical role in applications such as healthcare, sports monitoring, and rehabilitation. Traditional approaches typically rely on centralized models that aggregate and process data from multiple sensors simultaneously. However, such architecture often suffers from high latency, increased communication overhead, limited scalability, and reduced robustness, particularly in dynamic environments where wearable systems operate under resource constraints. This paper proposes a distributed neural network framework for HAR, where each wearable sensor independently processes its data using a lightweight neural model and transmits high-level features or predictions to a central neural network for final classification. This strategy alleviates the computational load on the central node, reduces data transmission across the network, and enhances user privacy. We evaluated the proposed distributed framework using our publicly available multi-sensor HAR dataset and compared its performance against a centralized neural network trained on the same data. The results demonstrate that the distributed approach achieves comparable or superior classification accuracy while significantly lowering inference latency and energy consumption. These findings underscore the promise of distributed intelligence in wearable systems for real-time and energy-efficient human activity monitoring. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)
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18 pages, 3318 KiB  
Article
Indirect AI-Based Estimation of Cardiorespiratory Fitness from Daily Activities Using Wearables
by Laura Saldaña-Aristizábal, Jhonathan L. Rivas-Caicedo, Kevin Niño-Tejada and Juan F. Patarroyo-Montenegro
Electronics 2025, 14(15), 3081; https://doi.org/10.3390/electronics14153081 - 1 Aug 2025
Viewed by 348
Abstract
Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised [...] Read more.
Cardiorespiratory fitness is a predictor of long-term health, traditionally assessed through structured exercise protocols that require maximal effort and controlled laboratory conditions. These protocols, while clinically validated, are often inaccessible, physically demanding, and unsuitable for unsupervised monitoring. This study proposes a non-invasive, unsupervised alternative—predicting the heart rate a person would reach after completing the step test, using wearable data collected during natural daily activities. Ground truth post-exercise heart rate was obtained through the Queens College Step Test, which is a submaximal protocol widely used in fitness settings. Separately, wearable sensors recorded heart rate (HR), blood oxygen saturation, and motion data during a protocol of lifestyle tasks spanning a range of intensities. Two machine learning models were developed—a Human Activity Recognition (HAR) model that classified daily activities from inertial data with 96.93% accuracy, and a regression model that estimated post step test HR using motion features, physiological trends, and demographic context. The regression model achieved an average root mean squared error (RMSE) of 5.13 beats per minute (bpm) and a mean absolute error (MAE) of 4.37 bpm. These findings demonstrate the potential of test-free methods to estimate standardized test outcomes from daily activity data, offering an accessible pathway to infer cardiorespiratory fitness. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)
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18 pages, 4452 KiB  
Article
Upper Limb Joint Angle Estimation Using a Reduced Number of IMU Sensors and Recurrent Neural Networks
by Kevin Niño-Tejada, Laura Saldaña-Aristizábal, Jhonathan L. Rivas-Caicedo and Juan F. Patarroyo-Montenegro
Electronics 2025, 14(15), 3039; https://doi.org/10.3390/electronics14153039 - 30 Jul 2025
Viewed by 370
Abstract
Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide [...] Read more.
Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)
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16 pages, 2030 KiB  
Article
Study on Comb-Drive MEMS Acceleration Sensor Used for Medical Purposes: Monitoring of Balance Disorders
by Michał Szermer and Jacek Nazdrowicz
Electronics 2025, 14(15), 3033; https://doi.org/10.3390/electronics14153033 - 30 Jul 2025
Viewed by 353
Abstract
This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a [...] Read more.
This article presents a comprehensive modeling and simulation framework for a capacitive MEMS accelerometer integrated with a sigma-delta analog-to-digital converter (ADC), with a focus on applications in wearable health and motion monitoring devices. The accelerometer used in the system is connected to a smartphone equipped with dedicated software and will be used to assess the risk of falling, which is crucial for patients with balance disorders. The authors designed the accelerometer with special attention paid to the specification required in a system, where the acceleration is ±2 g and the frequency is 100 Hz. They investigated the sensor’s behavior in the DC, AC, and time domains, capturing both the mechanical response of the proof mass and the resulting changes in output capacitance due to external acceleration. A key component of the simulation is the implementation of a second-order sigma-delta modulator designed to digitize the small capacitance variations generated by the sensor. The Simulink model includes the complete signal path from analog input to quantization, filtering, decimation, and digital-to-analog reconstruction. By combining MEMS+ modeling with MATLAB-based system-level simulations, the workflow offers a fast and flexible alternative to traditional finite element methods and facilitates early-stage design optimization for MEMS sensor systems intended for real-world deployment. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)
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17 pages, 1345 KiB  
Article
Wearable Sensor-Based Analysis of Human Biomechanics in Manual and Cobot-Assisted Agricultural Transplanting
by Yuetong Wu, Xiangrui Wang and Boyi Hu
Electronics 2025, 14(10), 2043; https://doi.org/10.3390/electronics14102043 - 17 May 2025
Viewed by 552
Abstract
Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative [...] Read more.
Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article
(This article belongs to the Special Issue Wearable Sensors for Human Position, Attitude and Motion Tracking)
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