Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

Munoz-Organero, M and Lotfi, A ORCID: https://orcid.org/0000-0002-5139-6565, 2016. Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data. Sensors, 16 (9), p. 1464. ISSN 1424-8220

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Abstract

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Munoz-Organero, M. and Lotfi, A.
Publisher:	MDPI
Date:	9 September 2016
Volume:	16
Number:	9
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s16091464 DOI
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	13 Sep 2016 09:35
Last Modified:	09 Jun 2017 14:05
URI:	https://irep.ntu.ac.uk/id/eprint/28475

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