Development of a User-Adaptable Human Fall Detection Based on Fall Risk Levels Using Depth Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 7; p. 2260
• Main Authors: Nizam, Yoosuf, Mohd, Mohd Norzali Haji, Jamil, M Mahadi Abdul
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-07-2018; MDPI
• Subjects: Accidental Falls; Aged; Algorithms; assistive living; daily activities; Fall detection; fall risk level; Falls; Health risks; human fall; Humans; Monitoring, Ambulatory - instrumentation; Monitoring, Ambulatory - methods; Movement; Public health; Risk; Risk Assessment; Risk levels; Sensors; daily activities; fall risk level; falls; assistive living; human fall
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18072260

Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection.