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A skeleton-free fall detection system from depth images using random decision forest

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Version 2 2024-06-04, 02:20
Version 1 2018-07-12, 09:58
journal contribution
posted on 2024-06-04, 02:20 authored by A Abobakr, M Hossny, S Nahavandi
Interest in enhancing medical services and healthcare is emerging exploiting recent technological capabilities. An integrable fall detection sensor is an essential component toward achieving smart healthcare solutions. Traditional vision-based methods rely on tracking a skeleton and estimating the change in height of key body parts such as head, hips, and shoulders. These methods are often challenged by occluded body parts and abrupt posture changes. This paper presents a fall detection system consisting of a novel skeleton-free posture recognition method and an activity recognition stage. The posture recognition method analyzes local variations in depth pixels to identify the adopted posture. An input depth frame acquired using a Kinect-like sensor is densely represented using a depth comparison feature and fed to a random decision forest to discriminate among standing, sitting, and fallen postures. The proposed approach simplifies the posture recognition into a simple pixel labeling problem, after which determining the posture is as simple as counting votes from all labeled pixels. The falling event is recognized using a support vector machine. The proposed approach records a sensitivity rate of 99% on synthetic and live datasets as well as a specificity rate of 99% on synthetic datasets and 96% on popular live datasets without invasive accelerometer support.

History

Journal

IEEE systems journal

Volume

12

Pagination

2994-3005

Location

Piscataway, N.J.

ISSN

1932-8184

eISSN

1937-9234

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2017, IEEE

Issue

3

Publisher

Institute of Electrical and Electroncs Engineers