Digital-Health Monitoring System for Healthy Aging Using Gait Biomarkers

Healthy aging is crucial for socioeconomic independence and quality living. Nevertheless, age-related gait variations result in mobility issues influencing human walk. Hence, studying lower-limb functionality with aging aids in sustaining good mobility. This work analyzed collected gait and ankle joint data of 80 healthy participants (21–60 years) using a developed G-Eva (system for gait evaluation) system during dynamic walking. Thereafter, data are preprocessed followed by feature extraction and selection using statistical, correlation, and relative importance analysis. With aging, gait variability is observed in walking speed, thus, a weighted majority-voting ensemble model (MVens) is proposed for age and gender prediction of middle-aged individuals to overcome imbalance influence. MVens model is able to achieve an accuracy of 90% for predicting age-related gait deterioration and 96.25% for gender estimation. It has also been demonstrated that among middle-aged adults, spatiotemporal parameters show significant changes followed by symmetry and variability analysis with aging as opposed to older adults. However, other significantly contributing features include differences in spatiotemporal and gait variability parameters followed by symmetry for gender prediction. Thus, the developed system with the proposed MVens model can be used for early diagnosis of mobility and joint-health disorders for initiation of timely intervention and rehabilitation.