An optimized low computational algorithm for human fall detection from depth images based on Support Vector Machine classification
Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approa...
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| Vydáno v: | 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) s. 407 - 412 |
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| Jazyk: | angličtina |
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IEEE
01.09.2017
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| Abstract | Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches used include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on an algorithm using combination of machine learning and human activity measurements such as changes of human height and rate of change of the subject during any of the activity. Classification of human fall from other activities of daily life is accomplished using height, changes in velocity and acceleration of the subject extracted from the depth information. Finally position of the subject and SVM classification is used for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 97.39% with sensitivity of 100% and specificity of 96.61%. |
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| AbstractList | Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches used include some sort of wearable devices, ambient based devices or non-invasive vision based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on an algorithm using combination of machine learning and human activity measurements such as changes of human height and rate of change of the subject during any of the activity. Classification of human fall from other activities of daily life is accomplished using height, changes in velocity and acceleration of the subject extracted from the depth information. Finally position of the subject and SVM classification is used for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 97.39% with sensitivity of 100% and specificity of 96.61%. |
| Author | Suhaila, S. Jamil, M. Mahadi Abdul Nizam, Y. Mohd, Mohd Norzali Haji |
| Author_xml | – sequence: 1 givenname: Mohd Norzali Haji surname: Mohd fullname: Mohd, Mohd Norzali Haji email: norzali@uthm.edu.my organization: Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Batu Pahat, Malaysia – sequence: 2 givenname: Y. surname: Nizam fullname: Nizam, Y. organization: Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Batu Pahat, Malaysia – sequence: 3 givenname: S. surname: Suhaila fullname: Suhaila, S. organization: Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Batu Pahat, Malaysia – sequence: 4 givenname: M. Mahadi Abdul surname: Jamil fullname: Jamil, M. Mahadi Abdul organization: Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Batu Pahat, Malaysia |
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| Snippet | Systems developed to classify human activities to identify unintentional falls are highly demanding and play an important role in our daily life. Human falls... |
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| SubjectTerms | Acceleration Accelerometers Assitive Technology Classification algorithms Daily Activities Depth Images Floors Head Human Posture Robot sensing systems Support vector machines SVM |
| Title | An optimized low computational algorithm for human fall detection from depth images based on Support Vector Machine classification |
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