Personalised sports rehabilitation analysis using a fitness enhanced model based on big data and deep learning

Health rehabilitation plays a vital role in contemporary healthcare in ensuring post-illness, post-injury, and post chronic condition recovery, and serving on maintaining or improving physical functions and overall quality of life and preventing long-term complications and conditions. Conventional approaches to rehabilitation have tended to involve off-the-shelf procedures and frequent clinical assessments. Purpose of this study is to establish a modern health rehabilitation management model. This is through implementing the embedded technology and big data analytic in Fitness Qigong. The system incorporates wearable devices and sensors in the environment. Physiologically, results indicated critical advances in physiological functioning following international training periods of Qigong training: increasing Heart Rate Variability (HRV) by 29.7%, decreasing breathing rate by 17.8%, improving movements efficiency by 20.3% and dropping the level of stress by 46.2% (all p < 0.001). Correlation analysis showed that improving HRV and moving with efficiency were strongly related to the intensity of practice (r = +0.84 and r = +0.90, respectively), whereas breathing rate showed a considerable negative relationship with practice intensity (r = -0.72). Comparison with Traditional Model In addition to that, the technology-enabled model exhibited further significant improvement, exceeding the traditional model increase by more than 200% HRV (+34.2% vs. +14.%) and breathing efficiency as well as adherence rates (91.5% vs. 65.8%). The implications of these findings emphasise the potential of technology-integrated Qigong practice to reliably, scalable and data-informed health management.