Abstract:
Physical impairments, such as missing limbs as a result of illness, genetic disorders, or amputation, have a profoundly harmful impact on an individual's quality of life, welfare, and economic prospects in many nations. Functional prosthetics that are suitable for their intended use and are inexpensive, powerful, and reliable are required. They must be simple to make, maintain, and/or repair and suited for long-term usage. Gait analysis, which examines human locomotion holistically, is crucial in identifying patterns in such activity. Lower limb prosthesis users' physical and mental health are strongly impacted by their gait quality. Amputees of the lower extremities have mobility issues, which will lower their quality of life. To build an interactive control of the amputee prosthesis environment system, wearable sensors are widely utilized to analyze spatiotemporal, kinematic, and kinetic characteristics. Controlling lower limb prosthetic devices requires knowledge of gait events and the gait phase detection of an amputee's mobility. In order to restore mobility after amputation, the patient is fitted with a prosthetic device. However, a number of factors including the individual's health, the type of amputation, prosthetic device performance, and the fit of the prosthetic socket, affect how much the person recovers. One of the objectives of the World Health Organization's 2030 Agenda for Sustainable Development is that people will be able to get assistive products and services to enhance their quality of life, as well as inexpensive, high-quality health treatments. In this thesis, a web-based system that will help clinicians track gait problems and prosthesis deviation by providing realtime data during rehabilitation. The proposed system uses MPU9250 sensor that combines accelerometer, magnetometer and gyroscope to detect spatial temporal gait parameters such as step length, stance phase duration, and ankle flexion angle and send obtained information of the possibility of the specified improper gaits or prosthetic fit via wireless communication system to the web application and later sent to cloud database for future use. The data received from the sensor was analyzed by the Nodemcu micro-controller and sent to the web dashboard to be accessed by clinicians via WIFI. The gait parameter values were recorded, analyzed and used to indicate if the walk cycle of a person is normal or it has any abnormality. This device is a practical option for objective gait and prosthetic deviations tracking in clinical rehabilitation because it is simple and easy to put on and take off. The gait monitoring system was able to detect gait and prosthetic deviations and future study will investigate its use in home environment.
