A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities
Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activiti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8470068/ |
| _version_ | 1818913267501760512 |
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| author | Diego Rivera Luis Cruz-Piris Susel Fernandez Bernardo Alarcos Antonio Garcia Juan R. Velasco |
| author_facet | Diego Rivera Luis Cruz-Piris Susel Fernandez Bernardo Alarcos Antonio Garcia Juan R. Velasco |
| author_sort | Diego Rivera |
| collection | DOAJ |
| description | Autonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements. |
| first_indexed | 2024-12-19T23:27:46Z |
| format | Article |
| id | doaj.art-1b0ed967233049759403131dca9eea66 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T23:27:46Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b0ed967233049759403131dca9eea662022-12-21T20:01:49ZengIEEEIEEE Access2169-35362018-01-016534095342510.1109/ACCESS.2018.28717328470068A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing ActivitiesDiego Rivera0https://orcid.org/0000-0002-7076-9048Luis Cruz-Piris1https://orcid.org/0000-0002-9570-2851Susel Fernandez2https://orcid.org/0000-0002-1576-4340Bernardo Alarcos3https://orcid.org/0000-0002-4455-5716Antonio Garcia4https://orcid.org/0000-0001-6194-5524Juan R. Velasco5https://orcid.org/0000-0003-0239-1116Departamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainDepartamento de Automática, Escuela Politécnica Superior, Universidad de Alcalá, Campus Universitario, Alcalá de Henares, SpainAutonomous devices able to evaluate diverse situations without external help have become especially relevant in recent years because they can be used as an important source of relevant information about the activities performed by people (daily habits, sports performance, and health-related activities). Specifically, the use of this kind of device in childhood games might help in the early detection of developmental problems in children. In this paper, we propose a method for the detection and classification of movements performed with an object, based on an acceleration signal. This method can automatically generate patterns associated with a given movement using a set of reference signals, analyze sequences of acceleration trends, and classify the sequences according to the previously established patterns. This method has been implemented, and a series of experiments has been carried out using the data from a sensor-embedded toy. For the validation of the obtained results, we have, in parallel, developed two other classification systems based on popular techniques, i.e., a similarity search based on Euclidean distances and machine-learning techniques, specifically a support vector machine model. When comparing the results of each method, we show that our proposed method achieves a higher number of successes and higher accuracy in the detection and classification of isolated movement signals as well as in sequences of movements.https://ieeexplore.ieee.org/document/8470068/Activity recognitionclassification algorithmsInternet of Thingspattern recognitionsensor systems and applications |
| spellingShingle | Diego Rivera Luis Cruz-Piris Susel Fernandez Bernardo Alarcos Antonio Garcia Juan R. Velasco A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities IEEE Access Activity recognition classification algorithms Internet of Things pattern recognition sensor systems and applications |
| title | A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities |
| title_full | A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities |
| title_fullStr | A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities |
| title_full_unstemmed | A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities |
| title_short | A Novel Method for Automatic Detection and Classification of Movement Patterns in Short Duration Playing Activities |
| title_sort | novel method for automatic detection and classification of movement patterns in short duration playing activities |
| topic | Activity recognition classification algorithms Internet of Things pattern recognition sensor systems and applications |
| url | https://ieeexplore.ieee.org/document/8470068/ |
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