Visual Fall Detection Analysis Through Computer Vision and Deep Learning – Technology Proposition

Abstract

Advances in modern medicine has increased humans’ life span. Orderly adults face mobility problems while aging. They feel less fit to continue any activity for short intervals too. This is due to declining fitness levels or muscle strength, diminished dexterity, and loss of balance. These symptoms lead to the fall of the individual and sometimes fatal too, if immediately not attended to. It’s an alarming issue for people staying alone. They may pose significant health risks and need immediate assistance. Fall detection technologies are majorly categorised as wearable sensors and ambient sensors. Fall detection wearable devices like pendant necklaces, watches and wristband devices, and clip-on medical alerts use accelerometers to detect rapid downward movements that can indicate a fall. They often also include manual alert buttons, for an increased accuracy. This requires technology comfort and awareness for usage. Ambient home sensors use video cameras to monitor the user’s movement and detect falls. When the fall is transmitted to a monitoring center, a representative typically will call the user to check on them before notifying contacts or calling for emergency services, but this can depend on the user’s preferences and risk factors. In this paper we propose a technology, using security cameras to record videos and create a video-based fall detection system. The system uses computer vision and deep learning algorithms to accurately recognize fall-related movements and distinguish them from regular activities. This system can be integrated to prompt alerts to emergency contacts, thus assisting in providing immediate aid to individuals who have experienced a fall. For higher accuracy, multiple-angle videos and multi-person tracking is integrated in this system to estimate the intensity of the fall for immediate attention. Thus, this fall detection system can contribute to the safety, well-being and independence of individuals at risk of falling.