An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment
Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Sp...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8805313/ |
| _version_ | 1819133528915312640 |
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| author | Fouzi Harrou Nabil Zerrouki Ying Sun Amrane Houacine |
| author_facet | Fouzi Harrou Nabil Zerrouki Ying Sun Amrane Houacine |
| author_sort | Fouzi Harrou |
| collection | DOAJ |
| description | Falls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance. |
| first_indexed | 2024-12-22T09:48:44Z |
| format | Article |
| id | doaj.art-7cecefc4280c44bd9b6908769a3584ed |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:48:44Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7cecefc4280c44bd9b6908769a3584ed2022-12-21T18:30:28ZengIEEEIEEE Access2169-35362019-01-01711496611497410.1109/ACCESS.2019.29363208805313An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home EnvironmentFouzi Harrou0https://orcid.org/0000-0002-2138-319XNabil Zerrouki1Ying Sun2Amrane Houacine3LCPTS, Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne (USTHB), Algiers, AlgeriaComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), huwal, Saudi ArabiaLCPTS, Faculty of Electronics and Computer Science, University of Sciences and Technology Houari Boumédienne (USTHB), Algiers, AlgeriaComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), huwal, Saudi ArabiaFalls are an important healthcare problem for vulnerable persons like seniors. Response to potential emergencies can be fastened timely detection and classification of falls. This paper addresses the detection of human falls using relevant pixel-based features reflecting variations in body shape. Specifically, the human body is divided into five partitions that correspond to five partial occupancy areas. For each frame, area ratios are calculated and used as input data for fall detection and classification. First, the detection of falls is addressed from a statistical point of view as an anomaly detection problem. Towards this end, an integrated approach merging a detection step with a classification step is proposed for enabling efficient human fall detection in a home environment. In this regard, an effective fall detection approach using generalized likelihood ratio (GLR) scheme is designed. However, a GLR scheme cannot discriminate between true falls and like-fall events, such as lying down. To mitigate this limitation, the support vector machine algorithm has been successfully applied on features of the detected fall to recognize the type of fall. Tests on two publicly available datasets show the effectiveness of the proposed approach to appropriately detecting and identifying falls. Compared with the neural network, k -nearest neighbor, decision tree and naïve Bayes procedures, the two steps approach achieved better detection performance.https://ieeexplore.ieee.org/document/8805313/Smart homehuman fallfall detectionclassificationmachine learning algorithms |
| spellingShingle | Fouzi Harrou Nabil Zerrouki Ying Sun Amrane Houacine An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment IEEE Access Smart home human fall fall detection classification machine learning algorithms |
| title | An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment |
| title_full | An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment |
| title_fullStr | An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment |
| title_full_unstemmed | An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment |
| title_short | An Integrated Vision-Based Approach for Efficient Human Fall Detection in a Home Environment |
| title_sort | integrated vision based approach for efficient human fall detection in a home environment |
| topic | Smart home human fall fall detection classification machine learning algorithms |
| url | https://ieeexplore.ieee.org/document/8805313/ |
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