A motion capture dataset on human sitting to walking transitions
Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Research
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/120102/ http://psasir.upm.edu.my/id/eprint/120102/1/120102.pdf |
| _version_ | 1848868113385259008 |
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| author | Perera, Chamalka Kenneth Hussain, Zakia Khant, Min Gopalai, Alpha Agape Gouwanda, Darwin Ahmad, Siti Anom |
| author_facet | Perera, Chamalka Kenneth Hussain, Zakia Khant, Min Gopalai, Alpha Agape Gouwanda, Darwin Ahmad, Siti Anom |
| author_sort | Perera, Chamalka Kenneth |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age groups – young (19–35 years), middle (36–55 years) and older (above 56 years). The dataset contains lower body motion capture, ground reaction force, surface electromyography, inertial measurement unit data, and responses for the knee injury and osteoarthritis outcome score survey. For validation, the within subjects intraclass correlation coefficients for the maximum and minimum lower body joint angles were calculated with values greater than 0.74, indicating good test-retest reliability. The joint angle trajectories and maximum voluntary contractions are comparable with existing literature, matching in overall trends and range. Accordingly, this dataset allows STW biomechanics, executions, and characteristics to be studied across age groups. Biomechanical trajectories of healthy adults serve as a benchmark in assessing neuromusculoskeletal impairments and when designing assistive technology for treatment or rehabilitation. |
| first_indexed | 2025-11-15T14:47:14Z |
| format | Article |
| id | upm-120102 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:47:14Z |
| publishDate | 2024 |
| publisher | Nature Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1201022025-09-23T04:31:52Z http://psasir.upm.edu.my/id/eprint/120102/ A motion capture dataset on human sitting to walking transitions Perera, Chamalka Kenneth Hussain, Zakia Khant, Min Gopalai, Alpha Agape Gouwanda, Darwin Ahmad, Siti Anom Sit-to-walk (STW) is a crucial daily task that impacts mobility, independence, and thus quality of life. Existing repositories have limited STW data with small sample sizes (n = 10). Hence, this study presents a STW dataset obtained via the time-up-and-go test, for 65 healthy adults across three age groups – young (19–35 years), middle (36–55 years) and older (above 56 years). The dataset contains lower body motion capture, ground reaction force, surface electromyography, inertial measurement unit data, and responses for the knee injury and osteoarthritis outcome score survey. For validation, the within subjects intraclass correlation coefficients for the maximum and minimum lower body joint angles were calculated with values greater than 0.74, indicating good test-retest reliability. The joint angle trajectories and maximum voluntary contractions are comparable with existing literature, matching in overall trends and range. Accordingly, this dataset allows STW biomechanics, executions, and characteristics to be studied across age groups. Biomechanical trajectories of healthy adults serve as a benchmark in assessing neuromusculoskeletal impairments and when designing assistive technology for treatment or rehabilitation. Nature Research 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/120102/1/120102.pdf Perera, Chamalka Kenneth and Hussain, Zakia and Khant, Min and Gopalai, Alpha Agape and Gouwanda, Darwin and Ahmad, Siti Anom (2024) A motion capture dataset on human sitting to walking transitions. Scientific Data, 11 (1). art. no. 878. pp. 1-10. ISSN 2052-4463 https://www.nature.com/articles/s41597-024-03740-z?error=cookies_not_supported&code=789f112e-bcf0-4987-8f4e-fd4b986f3b6c 10.1038/s41597-024-03740-z |
| spellingShingle | Perera, Chamalka Kenneth Hussain, Zakia Khant, Min Gopalai, Alpha Agape Gouwanda, Darwin Ahmad, Siti Anom A motion capture dataset on human sitting to walking transitions |
| title | A motion capture dataset on human sitting to walking transitions |
| title_full | A motion capture dataset on human sitting to walking transitions |
| title_fullStr | A motion capture dataset on human sitting to walking transitions |
| title_full_unstemmed | A motion capture dataset on human sitting to walking transitions |
| title_short | A motion capture dataset on human sitting to walking transitions |
| title_sort | motion capture dataset on human sitting to walking transitions |
| url | http://psasir.upm.edu.my/id/eprint/120102/ http://psasir.upm.edu.my/id/eprint/120102/ http://psasir.upm.edu.my/id/eprint/120102/ http://psasir.upm.edu.my/id/eprint/120102/1/120102.pdf |