ArticleSensors (Basel, Switzerland)2021
Paddle Stroke Analysis for Kayakers Using Wearable Technologies.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Paddle Stroke Classification and Wireless Monitoring in Open Water Using Wearable Inertial Nodes.Sensors (Basel, Switzerland) · 2025Article
- Development of a video camera-type kayak motion capture system to measure water kayaking.PeerJ · 2023Article
- Special Issue "Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures".Sensors (Basel, Switzerland) · 2021Article
- An Ergonomic Assessment of Different Postures and Children Risk during Evacuations.International journal of environmental research and public health · 2021Article
- Custom IMU-Based Wearable System for Robust 2.4 GHz Wireless Human Body Parts Orientation Tracking and 3D Movement Visualization on an Avatar.Sensors (Basel, Switzerland) · 2021Article
- Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proper stroke posture and rhythm are crucial for kayakers to achieve perfect performance and avoid the occurrence of sport injuries. The traditional video-based analysis method has numerous limitations (e.g., site and occlusion). In this study, we propose a systematic approach for evaluating the training performance of kayakers based on the multiple sensors fusion technology. Kayakers' motion information is collected by miniature inertial sensor nodes attached on the body. The extend Kalman filter (EKF) method is used for data fusion and updating human posture. After sensor calibration, the kayakers' actions are reconstructed by rigid-body model. The quantitative kinematic analysis is carried out based on joint angles. Machine learning algorithms are used for differentiating the stroke cycle into different phases, including entry, pull, exit and recovery. The experiment shows that our method can provide comprehensive motion evaluation information under real on-water scenario, and the phase identification of kayaker's motions is up to 98% validated by videography method. The proposed approach can provide quantitative information for coaches and athletes, which can be used to improve the training effects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.