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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2016-09-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/16/9/1464 |
| _version_ | 1811213423738880000 |
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| author | Mario Munoz-Organero Ahmad Lotfi |
| author_facet | Mario Munoz-Organero Ahmad Lotfi |
| author_sort | Mario Munoz-Organero |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T05:46:38Z |
| format | Article |
| id | doaj.art-a4a354ba551c40fd8763f36933db2293 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T05:46:38Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a4a354ba551c40fd8763f36933db22932022-12-22T03:45:27ZengMDPI AGSensors1424-82202016-09-01169146410.3390/s16091464s16091464Human Movement Recognition Based on the Stochastic Characterisation of Acceleration DataMario Munoz-Organero0Ahmad Lotfi1Telematics Engineering Department, Universidad Carlos III de Madrid, Avda de la Universidad, 30, E-28911 Leganés, Madrid, SpainSchool of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UKHuman 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.http://www.mdpi.com/1424-8220/16/9/1464human movement detectionactivitieswearable sensorsfall detection |
| spellingShingle | Mario Munoz-Organero Ahmad Lotfi Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data Sensors human movement detection activities wearable sensors fall detection |
| 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 | human movement detection activities wearable sensors fall detection |
| url | http://www.mdpi.com/1424-8220/16/9/1464 |
| work_keys_str_mv | AT mariomunozorganero humanmovementrecognitionbasedonthestochasticcharacterisationofaccelerationdata AT ahmadlotfi humanmovementrecognitionbasedonthestochasticcharacterisationofaccelerationdata |