ArticleBMC medical informatics and decision making2025
A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Emerging Integrating Approach to Sensors, Digital Signal Processing, Communication Systems, and Artificial Intelligence.Sensors (Basel, Switzerland) · 2026Review
- The fatigue status feature of bicycle movement based on deep learning and signal processing technology.Scientific reports · 2025Article
- Estimation of lower limb torque: a novel hybrid method based on continuous wavelet transform and deep learning approach.PeerJ. Computer science · 2025Article
- Optimization of physical energy and velocity allocation for cyclists in road cycling individual time trial using genetic algorithm.Frontiers in physiology · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
This study proposes a deep learning-based motion assessment method that integrates the pose estimation algorithm (Keypoint RCNN) with signal processing techniques, demonstrating its reliability and effectiveness.The reliability and validity of this method were also verified.Twenty college students were recruited to pedal a stationary bike. Inertial sensors and a smartphone simultaneously recorded the participants' cycling movement. Keypoint RCNN(KR) algorithm was used to acquire 2D coordinates of the participants' skeletal keypoints from the recorded movement video. Spearman's rank correlation analysis, intraclass correlation coefficient (ICC), error analysis, and t-test were conducted to compare the consistency of data obtained from the two movement capture systems, including the peak frequency of acceleration, transition time point between movement statuses, and the complexity index average (CIA) of the movement status based on multiscale entropy analysis.The KR algorithm showed excellent consistency (ICC
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Registered trials
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