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A machine learning model for predicting sit-to-stand trajectories of people with and without stroke: towards adaptive robotic assistance

Bennett, Thomas; Kumar, Praveen; Ruiz Garate, Virginia Ruiz


Thomas Bennett

Virginia Ruiz Ruiz Garate


Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke, limiting the ability to engage in other daily activities. This study presents the recording and analysis of a comprehensive database of full body biomechanics and force data captured during sit-to-stand-to-sit movements in subjects who have and have not experienced stroke. These data were then used in conjunction with simple machine learning algorithms to predict vertical motion trajectories that could be further employed for the control of an assistive robot. A total of 30 people (including 6 with stroke) each performed 20 sit-to-stand-to-sit actions at two different seat heights, from which average trajectories were created. Weighted k-nearest neighbours and linear regression models were then used on two different sets of key participant parameters (height and weight, and BMI and age), to produce a predicted trajectory. Resulting trajectories matched the true ones for non-stroke subjects with an average R2 score of 0.864 ± 0.134 using k = 3 and 100% seat height when using height and weight parameters. Even among a small sample of stroke patients, balance and motion trends were noticed along with a large within-class variation, showing that larger scale trials need to be run to obtain significant results. The full dataset of sit-to-stand-to-sit actions for each user is made publicly available for further research.


Bennett, T., Kumar, P., & Ruiz Garate, V. R. (2022). A machine learning model for predicting sit-to-stand trajectories of people with and without stroke: towards adaptive robotic assistance. Sensors, 22(13),

Journal Article Type Article
Acceptance Date Jun 22, 2022
Online Publication Date Jun 24, 2022
Publication Date Jun 24, 2022
Deposit Date Jun 29, 2022
Publicly Available Date Jun 29, 2022
Journal Sensors
Electronic ISSN 1424-8220
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 22
Issue 13
Series Title Sensing, Estimating, and Analyzing Human Movements for Human–Robot Interaction
Keywords Movement prediciton, machine learning, stroke, assistive robots
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