A Novel Martingale Based Model Using a Smartphone to Detect Gait Bout in Human Activity Recognition

Abstract

Gait bout is when an individual performs certain physical activities such as walking or running. In the last few decades, the study of gait bout has led to substantial progress in treating gait impairment (neuropathic, myopathic, and parkinsonian) in a person. Recently, gait bout study has been improved by advancing smartphone technology. To perform gait bout tasks, two different human activity scenarios, such as walking upstairs and standing, are obtained using the axis orientation of a smartphone accelerometer. To capture the pattern of walking upstairs and standing, we utilize a smartphone device attached to the waist of 30 subjects within the age group from 19 to 48 years old. We propose a human activity recognition model known as the multivariate triple exponential weighted moving average of the martingale sequence using particle swarm optimization (MTMS(PSO)) in the experimental setup. MTMS(PSO) utilizes the martingale framework to capture gait bout in human activity recognition data. Firstly, MTMS(PSO) is an unsupervised learning method that uses smoothing techniques such as triple exponential smoothing to remove high-frequency noise from the processed activity times series, making the patterns more visible. Secondly, the activity recognition model involves computing a threshold for identifying gait bout. Thirdly, MTMS(PSO) uses logical precedent and particle swarm optimization to enhance accuracy and precision. As a result, the overall MTMS(PSO) accuracy and G-mean are $95.4\%$ and $96.1\%$ , respectively. In addition, MTMS(PSO) technique independently outperforms other traditional methods such as MRPM(PSO), MGM(PSO), and ELM.