» Articles » PMID: 32901007

An Asian-centric Human Movement Database Capturing Activities of Daily Living

Overview
Journal Sci Data
Specialty Science
Date 2020 Sep 9
PMID 32901007
Citations 7
Authors
Affiliations
Soon will be listed here.
Abstract

Assessment of human movement performance in activities of daily living (ADL) is a key component in clinical and rehabilitation settings. Motion capture technology is an effective method for objective assessment of human movement. Existing databases capture human movement and ADL performance primarily in the Western population, and there are no Asian databases to date. This is despite the fact that Asian anthropometrics influence movement kinematics and kinetics. This paper details the protocol in the first phase of the largest Asian normative human movement database. Data collection has commenced, and this paper reports 10 healthy participants. Twelve tasks were performed and data was collected using Qualisys motion capture system, force plates and instrumented table and chair. In phase two, human movement of individuals with stroke and knee osteoarthritis will be captured. This can have great potential for benchmarking with the normative human movement captured in phase one and predicting recovery and progression of movement for patients. With individualised progression, it will offer the development of personalised therapy protocols in rehabilitation.

Citing Articles

A Personalized Multimodal BCI-Soft Robotics System for Rehabilitating Upper Limb Function in Chronic Stroke Patients.

Premchand B, Zhang Z, Ang K, Yu J, Tan I, Lam J Biomimetics (Basel). 2025; 10(2).

PMID: 39997117 PMC: 11852476. DOI: 10.3390/biomimetics10020094.


A Motion Capture Dataset on Human Sitting to Walking Transitions.

Kenneth Perera C, Hussain Z, Khant M, Gopalai A, Gouwanda D, Ahmad S Sci Data. 2024; 11(1):878.

PMID: 39138206 PMC: 11322156. DOI: 10.1038/s41597-024-03740-z.


Unraveling stroke gait deviations with movement analytics, more than meets the eye: a case control study.

Pan J, Sidarta A, Wu T, Kwong W, Ong P, Tay M Front Neurosci. 2024; 18:1425183.

PMID: 39104608 PMC: 11298395. DOI: 10.3389/fnins.2024.1425183.


Sit-to-walk strategy classification in healthy adults using hip and knee joint angles at gait initiation.

Kenneth Perera C, Gopalai A, Gouwanda D, Ahmad S, Salim M Sci Rep. 2023; 13(1):16640.

PMID: 37789077 PMC: 10547676. DOI: 10.1038/s41598-023-43148-0.


Quantitative Assessment of Upper Limb Movement in Post-Stroke Adults for Identification of Sensitive Measures in Reaching and Lifting Activities.

Blaszczyszyn M, Szczesna A, Konieczny M, Pakosz P, Balko S, Borysiuk Z J Clin Med. 2023; 12(9).

PMID: 37176773 PMC: 10179564. DOI: 10.3390/jcm12093333.


References
1.
Aboelnasr E, Hegazy F, Altalway H . Kinematic characteristics of reaching in children with hemiplegic cerebral palsy: A comparative study. Brain Inj. 2016; 31(1):83-89. DOI: 10.1080/02699052.2016.1210230. View

2.
Yozbatiran N, Der-Yeghiaian L, Cramer S . A standardized approach to performing the action research arm test. Neurorehabil Neural Repair. 2007; 22(1):78-90. DOI: 10.1177/1545968307305353. View

3.
Schwarz A, Kanzler C, Lambercy O, Luft A, Veerbeek J . Systematic Review on Kinematic Assessments of Upper Limb Movements After Stroke. Stroke. 2019; 50(3):718-727. DOI: 10.1161/STROKEAHA.118.023531. View

4.
Kontson K, Marcus I, Myklebust B, Civillico E . Targeted box and blocks test: Normative data and comparison to standard tests. PLoS One. 2017; 12(5):e0177965. PMC: 5438168. DOI: 10.1371/journal.pone.0177965. View

5.
Berg K, Williams J, Maki B . Measuring balance in the elderly: validation of an instrument. Can J Public Health. 1992; 83 Suppl 2:S7-11. View