A novel hidden Markov model-based adaptive dynamic time warping (HMDTW) gait analysis for identifying physically challenged persons

Internet of things plays vital role in real-time applications, and the research thrust towards implementing IoT in gait analysis increases day by day in order to obtain efficient gait recognition mechanism. IoT in gait analysis is used to monitor and communicate the observing gait, and also to transfer data to others is the current trend which is available. This research work provides an efficient gait recognition system with IoT using dynamic time wrapping and naive bays classifier as combination to obtain hybrid model. The objective of this research is identifying the patients or persons with walking disabilities in a crowded area and providing suitable alerts to them by monitoring the walking styles. So that the possibility of getting injured is avoided and the information related to the persons also alerted through IoT module. Also, IoT module is used to collect information from the sensors used in persons accessories and other places. Twenty-five males and 10 females are subjected to examine the proposed model in different locations and achieved the overall accuracy percentage of 92.15%.