A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance

Abstract

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.

Technological advances in lower-limb tele-rehabilitation: A review of literature

“…To address home-based rehabilitation programs, a variety of systems have been designed with different technologies such as vision including depth and RGB cameras, 26 – 49 and wearables, such as Inertial Measurement Units (IMU). 50 – 69 Some systems also used pressure-sensing technologies, such as pressure sensitive mats (with electronic textiles) and insole pressure sensors, 70 – 81 to measure the pressure of body limbs and its distribution on the respective areas. In each subsection, we begin with the outcomes of studies that performed joint angle measurement or estimation, followed by the studies that performed exercise recognition and/or exercise quality assessment.…”

“…Similar to the previous approaches, the reviewed studies either estimated joint angles, or gait parameters 71 , 75 , 79 or used ML for activity recognition. 70 , 73 , 74 , 76 , 77 , 80 , 81 However, it is worth noting that there is still a noticeable lack of work in the area of exercise assessment modules using this technology, as only one study was identified and categorized within this domain. 72 …”

“…Bennett et al aimed to analyze the trajectory of sit-to-stand-to-sit movements in individuals with and without stroke. 70 The researchers used a full body mocap suit with 17 IMUs to obtain the ground truth for the movements. They also used a balance board with four pressure sensors to measure the center of force and a pressure sensing mat to analyze weight distribution.…”