ArticleSensors (Basel, Switzerland)2017
Trunk Motion System (TMS) Using Printed Body Worn Sensor (BWS) via Data Fusion Approach.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Wearable SIMO Inductive Resonant Link for Posture Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Exploring the electrical robustness of conductive textile fasteners for wearable devices in different human motion conditions.Scientific reports · 2024Article
- Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device.Sensors (Basel, Switzerland) · 2021Article
- Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning.PeerJ. Computer science · 2021Article
- Automatic Recognition of Human Interaction via Hybrid Descriptors and Maximum Entropy Markov Model Using Depth Sensors.Entropy (Basel, Switzerland) · 2020Article
- Wearable Device to Monitor Back Movements Using an Inductive Textile Sensor.Sensors (Basel, Switzerland) · 2020Article
- Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2019Article
- Application-Based Production and Testing of a Core-Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles.Sensors (Basel, Switzerland) · 2019Article
- Classifying Diverse Physical Activities Using "Smart Garments".Sensors (Basel, Switzerland) · 2019Article
- Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
7 authors.
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Abstract
Human movement analysis is an important part of biomechanics and rehabilitation, for which many measurement systems are introduced. Among these, wearable devices have substantial biomedical applications, primarily since they can be implemented both in indoor and outdoor applications. In this study, a Trunk Motion System (TMS) using printed Body-Worn Sensors (BWS) is designed and developed. TMS can measure three-dimensional (3D) trunk motions, is lightweight, and is a portable and non-invasive system. After the recognition of sensor locations, twelve BWSs were printed on stretchable clothing with the purpose of measuring the 3D trunk movements. To integrate BWSs data, a neural network data fusion algorithm was used. The outcome of this algorithm along with the actual 3D anatomical movements (obtained by Qualisys system) were used to calibrate the TMS. Three healthy participants with different physical characteristics participated in the calibration tests. Seven different tasks (each repeated three times) were performed, involving five planar, and two multiplanar movements. Results showed that the accuracy of TMS system was less than 1.0°, 0.8°, 0.6°, 0.8°, 0.9°, and 1.3° for flexion/extension, left/right lateral bending, left/right axial rotation, and multi-planar motions, respectively. In addition, the accuracy of TMS for the identified movement was less than 2.7°. TMS, developed to monitor and measure the trunk orientations, can have diverse applications in clinical, biomechanical, and ergonomic studies to prevent musculoskeletal injuries, and to determine the impact of interventions.
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Registered trials
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