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Title: Application of intelligent sensing and digital teaching mode in physical education teaching in colleges and universities

Authors: Min Wu; Xiaoling Song

Addresses: Physical Education Department, Dalian Ocean University, Dalian 116023, Liaoning, China ' Physical Education Department, Dalian Ocean University, Dalian 116023, Liaoning, China

Abstract: To address the limitations of traditional university physical education in time and space and its inability to meet individual student needs, this study proposes an intelligent sensing + digital teaching model. Centred on a smart sensor network, it collects real-time motion data via wearable devices, uses Kalman filtering for noise reduction, and applies CNNs for accurate movement recognition. K-means clustering analyses student profiles to generate personalised training programs. An interactive digital environment built on Moodle enables data visualisation, real-time feedback, and online guidance, forming a 'perception-analysis-feedback' closed-loop system. Experiments show improved data quality and recognition accuracy, with a 28.6% fitness improvement among low-level students, demonstrating the model's effectiveness in promoting scientific, precise physical education teachings.

Keywords: digital teaching; college physical education; personalised training; Kalman filter algorithm; convolutional neural networks.

DOI: 10.1504/IJCEELL.2025.149040

International Journal of Continuing Engineering Education and Life-Long Learning, 2025 Vol.35 No.8, pp.179 - 198

Received: 21 Jan 2025
Accepted: 18 Jul 2025
Published online: 10 Oct 2025 *