Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor
This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of imp...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2018/5434897 |
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| _version_ | 1832554924026626048 |
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| author | Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit |
| author_facet | Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit |
| author_sort | Bunthit Watanapa |
| collection | DOAJ |
| description | This paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond. |
| format | Article |
| id | doaj-art-d04fc3e63a50401a925caadc85b56c38 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-d04fc3e63a50401a925caadc85b56c382025-02-03T05:50:08ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322018-01-01201810.1155/2018/54348975434897Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect SensorBunthit Watanapa0Orasa Patsadu1Piyapat Dajpratham2Chakarida Nukoolkit3School of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandFaculty of Science and Technology, Rajamangala University of Technology Krungthep, Bangkok, ThailandFaculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, ThailandSchool of Information Technology, King Mongkut’s University of Technology Thonburi, Bangkok, ThailandThis paper proposes a fall severity analytic and post-fall intelligence system with three interdependent modules. Module I is the analysis of fall severity based on factors extracted in the phases of during and after fall which include innovative measures of the sequence of body impact, level of impact, and duration of motionlessness. Module II is a timely autonomic notification to relevant persons with context-dependent fall severity alert via electronic communication channels (e.g., smartphone, tablet, or smart TV set). Lastly, Module III is the diagnostic support for caregivers and doctors to have information for making a well-informed decision of first aid or postcure with the chronologically traceable intelligence of information and knowledge found in Modules I and II. The system shall be beneficial to caregivers or doctors, in giving first aid/diagnosis/treatment to the subject, especially, in cases where the subject has lost consciousness and is unable to respond.http://dx.doi.org/10.1155/2018/5434897 |
| spellingShingle | Bunthit Watanapa Orasa Patsadu Piyapat Dajpratham Chakarida Nukoolkit Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor Applied Computational Intelligence and Soft Computing |
| title | Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
| title_full | Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
| title_fullStr | Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
| title_full_unstemmed | Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
| title_short | Post-Fall Intelligence Supporting Fall Severity Diagnosis Using Kinect Sensor |
| title_sort | post fall intelligence supporting fall severity diagnosis using kinect sensor |
| url | http://dx.doi.org/10.1155/2018/5434897 |
| work_keys_str_mv | AT bunthitwatanapa postfallintelligencesupportingfallseveritydiagnosisusingkinectsensor AT orasapatsadu postfallintelligencesupportingfallseveritydiagnosisusingkinectsensor AT piyapatdajpratham postfallintelligencesupportingfallseveritydiagnosisusingkinectsensor AT chakaridanukoolkit postfallintelligencesupportingfallseveritydiagnosisusingkinectsensor |