An eight-camera fall detection system using human fall pattern recognition via machine learning by a low-cost android box

Abstract

Falls are a leading cause of unintentional injuries and can result in devastating disabilities and fatalities when left undetected and not treated in time. Current detection methods have one or more of the following problems: frequent battery replacements, wearer discomfort, high costs, complicated setup, furniture occlusion, and intensive computation. In fact, all non-wearable methods fail to detect falls beyond ten meters. Here, we design a house-wide fall detection system capable of detecting stumbling, slipping, fainting, and various other types of falls at 60 m and beyond, including through transparent glasses, screens, and rain. By analyzing the fall pattern using machine learning and crafted rules via a local, low-cost single-board computer, true falls can be differentiated from daily activities and monitored through conventionally available surveillance systems. Either a multi-camera setup in one room or single cameras installed at high altitudes can avoid occlusion. This system’s flexibility enables a wide-coverage set-up, ensuring safety in senior homes, rehab centers, and nursing facilities. It can also be configured into high-precision and high-recall application to capture every single fall in high-risk zones.

Sensor-based fall risk assessment in older adults with or without cognitive impairment: a systematic review

“…A recent review on single and multiple sensorbased fall detection concluded wearable sensor-based solutions to be of accuracy to detect fall-events in older adults [25]. Nevertheless, fall detection systems using multiple input sources may lead to high costs and their use is often restricted to indoor locations [25,26]. Furthermore, fall detection systems may help to identify external fall risk (e.g., uneven ground) but they are limited in providing information about internal fall risk factors, e.g., dysfunctional patterns of gait or required motor tasks that are of interest to conceptualize fall prevention strategies.…”

Acceptance and Preferences of Using Ambient Sensor-Based Lifelogging Technologies in Home Environments

“…In fact, lifelogging can help to raise awareness about one's own quality of life, thanks to the use of wearable trackers, ambient sensors, and even manual entry to collect suitable indicators [3]. The spectrum of the technological developments is extremely broad, reaching from video-based approaches [1,4,5], including those exploiting multiple cameras and suitable artificial intelligence-based processing aimed at fall detection, such as [6][7][8], over ambient systems based on different sensors and technologies (such as microphones, pressure or vibration sensors) [9,10] to wearable technologies using acceleration and rotation rate sensors [11][12][13]. All these technologies and systems have in common that they can be used to support people in their everyday life, e.g., by detecting emergency situations, by identifying typical movement and behavior patterns as well as anomalies, or by remembering functions [2,14].…”

A Sensor-Based mHealth Platform for Remote Monitoring and Intervention of Frailty Patients at Home

“…These technologies can be classified into two types: (1) non-portable systems; (2) systems based on sensors and portable devices [ 32 ]. Non-portable systems use environmental sensors located in the monitoring area, typically artificial vision systems based on cameras [ 33 ] or floor sensors systems (pressure, vibration, capacitive, etc.) [ 34 ].…”