Advanced Gait Analysis for Elder Wellbeing Monitoring

As the elderly population grows, fall prediction and prevention becomes a crucial subject for investigation. This work explores a novel approach using unobtrusive sensor such as pressure mats and machine learning (ML) algorithms to continuously monitor gait patterns and predict fall risks in older adults. Sensing pressure mat was denoised and used to acquire movement datasets from an individual ranging from normal walking to performance of various activities such as falls, jumps and the application of uneven foot pressure while walking or exercising in a lab environment that mimics a home environment. Data gleaned from the sensor were cleaned, visualised, analysed and used to design a fall prediction model using a decision tree algorithm. Experimental results indicated a balanced performance (overall accuracy of 80% and F1 score of 88.64%). This paves the way for innovative fall prediction and prevention strategies using sensor mats and ML, ultimately benefiting the wellbeing of the aging population. Future research will utilise real-world data from unobtrusive sensing solutions such as pressure mats and more advanced ML algorithms to model a fall prediction system.