Development of a user-adaptable human fall detection based on fall risk levels using depth sensor
Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approac...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI
2018
|
| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2878/1/AJ%202019%20%2854%29.pdf |
| _version_ | 1796868732791816192 |
|---|---|
| author | Nizam, Yoosuf Haji Mohd, Mohd Norzali Abdul Jamil, M. Mahadi |
| author_facet | Nizam, Yoosuf Haji Mohd, Mohd Norzali Abdul Jamil, M. Mahadi |
| author_sort | Nizam, Yoosuf |
| collection | UTHM |
| description | Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection. |
| first_indexed | 2024-03-05T21:44:13Z |
| format | Article |
| id | uthm.eprints-2878 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:44:13Z |
| publishDate | 2018 |
| publisher | MDPI |
| record_format | dspace |
| spelling | uthm.eprints-28782021-11-16T03:49:32Z http://eprints.uthm.edu.my/2878/ Development of a user-adaptable human fall detection based on fall risk levels using depth sensor Nizam, Yoosuf Haji Mohd, Mohd Norzali Abdul Jamil, M. Mahadi TA164 Bioengineering TA166-167 Human engineering Unintentional falls are a major public health concern for many communities, especially with aging populations. There are various approaches used to classify human activities for fall detection. Related studies have employed wearable, non-invasive sensors, video cameras and depth sensor-based approaches to develop such monitoring systems. The proposed approach in this study uses a depth sensor and employs a unique procedure which identifies the fall risk levels to adapt the algorithm for different people with their physical strength to withstand falls. The inclusion of the fall risk level identification, further enhanced and improved the accuracy of the fall detection. The experimental results showed promising performance in adapting the algorithm for people with different fall risk levels for fall detection. MDPI 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/2878/1/AJ%202019%20%2854%29.pdf Nizam, Yoosuf and Haji Mohd, Mohd Norzali and Abdul Jamil, M. Mahadi (2018) Development of a user-adaptable human fall detection based on fall risk levels using depth sensor. Sensors, 18 (7). pp. 1-14. ISSN 1424-8220 https://doi.org/10.3390/s18072260 |
| spellingShingle | TA164 Bioengineering TA166-167 Human engineering Nizam, Yoosuf Haji Mohd, Mohd Norzali Abdul Jamil, M. Mahadi Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title | Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title_full | Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title_fullStr | Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title_full_unstemmed | Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title_short | Development of a user-adaptable human fall detection based on fall risk levels using depth sensor |
| title_sort | development of a user adaptable human fall detection based on fall risk levels using depth sensor |
| topic | TA164 Bioengineering TA166-167 Human engineering |
| url | http://eprints.uthm.edu.my/2878/1/AJ%202019%20%2854%29.pdf |
| work_keys_str_mv | AT nizamyoosuf developmentofauseradaptablehumanfalldetectionbasedonfallrisklevelsusingdepthsensor AT hajimohdmohdnorzali developmentofauseradaptablehumanfalldetectionbasedonfallrisklevelsusingdepthsensor AT abduljamilmmahadi developmentofauseradaptablehumanfalldetectionbasedonfallrisklevelsusingdepthsensor |