preprints.org > computer science and mathematics > other > doi: 10.20944/preprints201610.0096.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 22 October 2016 / Approved: 22 October 2016 / Online: 22 October 2016 (11:20:53 CEST)

Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, that prevent authors to evenly compare their new proposals. Here, we present a dataset of falls and activities of daily living (ADL) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADL and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADL and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96~\% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where algorithms could be focused on. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages to develop new strategies with this new dataset as benchmark.

triaxial accelerometer; wearable devices; fall detection; mobile health-care; SisFall

Computer Science and Mathematics, Other

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