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 253

Special Issue Editor

Graduate School of Computer Science and Engineering, The University of Aizu, Tsuruga, Ikki-machi, Aizuwakamatsu 965-8580, Japan
Interests: communication and network engineering; control and system engineering; electron device and electronic equipment; sports sciences; computer system; information network; perceptual information processing; human interface and interaction; intelligent informatics; soft computing; intelligent robotics; life health and medical informatics; biomedical engineering; real-time embedded computing systems; gesture recognition; network protocols
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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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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 (1 paper)

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Research

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 127
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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