MS Thesis

Abstract - Fall detection and prevention is an important area of research because of its significance in elderly health monitoring. The physical strength of bones and limbs deteriorates gradually with aging; hence, the risk of falls increases significantly in the elderly population. Fall-related injuries and hospitalization due to falls increase the load on national health systems. The objective in this thesis is to develop an intelligent system that can detect fall events in assisted living using wearable devices for the internet of health things environments. For such an intelligent system, deep learning model inspired from Inception-ResNet will be developed which will be trained & validated on pre-fall and/or post-fall events data collected with wearable sensors. The ultimate objective is to detect fall events in the near real-time.

• Supervisior: Dr. Qaiser Riaz
• Co-Supervisior: Dr. Hasan Ali Khattak