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Sensors 2016, 16(9), 1464; doi:10.3390/s16091464

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

1
Telematics Engineering Department, Universidad Carlos III de Madrid, Avda de la Universidad, 30, E-28911 Leganés, Madrid, Spain
2
School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 25 May 2016 / Revised: 31 August 2016 / Accepted: 7 September 2016 / Published: 9 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [1042 KB, uploaded 9 September 2016]   |  

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. View Full-Text
Keywords: human movement detection; activities; wearable sensors; fall detection human movement detection; activities; wearable sensors; fall detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Munoz-Organero, M.; Lotfi, A. Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data. Sensors 2016, 16, 1464.

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