Fall Detection Approaches for Monitoring Elderly HealthCare Using Kinect Technology: A Survey

Abstract

The severity of falls increases with age and reduced mobility. Falls are a frequent source of domestic accidents and accidental death on the part of fragile people. They produce anatomical injuries, reduce quality of life, cause dramatic psychological effects, and impose heavy financial burdens. A growing elderly population leads to a direct increase in health service costs, and indirectly to a deterioration of social life in the long term. Unsurprisingly, socioeconomic costs have triggered new scientific health research to detect falls in older people. One of the most appropriate solutions for monitoring the elderly and automatically detecting falls is computer vision. The Kinect camera plays a vital role in recognizing and detecting activities while ensuring seniors’ comfort, safety, and privacy preferences in the fall detection system. This research surveys several Kinect-based works in the literature that cover the approaches used in fall detection. In addition, we discuss the public fall benchmark based on Kinect technology. In general, the main objective of this survey is to provide a complete description of the modules making up the fall detectors and thereby guide researchers in developing fall approaches based on Kinect.

Classifying Motorcyclist Behaviour with XGBoost Based on IMU Data

“…However, there is also a large variety of algorithms that can be applied. Fayad et al [8], for example, published an extensive lists of Kinect-based fall detection approaches. Eye-trackers have also been investigated to monitor human behaviour, e.g., in the context of personnel selection for pilots and air traffic controllers [9].…”