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Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

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 algorit...

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Detalles Bibliográficos
Autores principales: Munoz-Organero, Mario, Lotfi, Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038742/
https://www.ncbi.nlm.nih.gov/pubmed/27618063
http://dx.doi.org/10.3390/s16091464
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author Munoz-Organero, Mario
Lotfi, Ahmad
author_facet Munoz-Organero, Mario
Lotfi, Ahmad
author_sort Munoz-Organero, Mario
collection PubMed
description 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.
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spelling pubmed-50387422016-09-29 Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data Munoz-Organero, Mario Lotfi, Ahmad Sensors (Basel) Article 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. MDPI 2016-09-09 /pmc/articles/PMC5038742/ /pubmed/27618063 http://dx.doi.org/10.3390/s16091464 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Munoz-Organero, Mario
Lotfi, Ahmad
Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title_full Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title_fullStr Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title_full_unstemmed Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title_short Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data
title_sort human movement recognition based on the stochastic characterisation of acceleration data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038742/
https://www.ncbi.nlm.nih.gov/pubmed/27618063
http://dx.doi.org/10.3390/s16091464
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