Pre-Fall Detection by Openpose
| dc.contributor.author | Saha, Subhajit | |
| dc.date.accessioned | 2022-03-24T08:17:46Z | |
| dc.date.available | 2022-03-24T08:17:46Z | |
| dc.date.issued | 2021-07 | |
| dc.description | Dissertation under the supervision of Dr. Ashish Ghosh | en_US |
| dc.description.abstract | Due to the improvement of medical science people worldwide are living longer. WHO has already predicted that the number of persons with age more than 60, will increase to 1.4 billion by 2030 and 2.1 billion by 2050. Where in 2019 it was 1 billion. Fall accidents have become one of the main health threats elderly. Here a pre-fall detection model based on OpenPose, a human posture estimation algorithm has been proposed to distinguish the normal daily activities and accidental falls. Four handcrafted features are extracted from the virtual skeleton returned by OpenPose and using those feature classification algorithms can classify falling and non-falling situations. | en_US |
| dc.identifier.citation | 24p. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10263/7312 | |
| dc.language.iso | en | en_US |
| dc.publisher | Indian Statistical Institute, Kolkata. | en_US |
| dc.relation.ispartofseries | Dissertation;CS1933 | |
| dc.subject | Body Velocity | en_US |
| dc.subject | Change in vertical angle | en_US |
| dc.subject | Change in height | en_US |
| dc.subject | Variation of central line velocity | en_US |
| dc.subject | OpenPose | en_US |
| dc.subject | Video surveillance | en_US |
| dc.subject | Pre-Fall Detection | en_US |
| dc.title | Pre-Fall Detection by Openpose | en_US |
| dc.type | Other | en_US |
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