Bristol Robotics Laboratory, Faculty of Environment and Technology, University of the West of England, Bristol BS16 1QY, UK.
Faculty of Health and Applied Sciences, University of the West of England, Bristol BS16 1DD, UK.
Sensors (Basel). 2022 Jun 24;22(13):4789. doi: 10.3390/s22134789.
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 -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 = 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.
坐站和站坐转移是基本的日常动作，使所有其他类型的移动和步态成为可能。然而，由于不同的因素，如中风的发生，这些动作的能力可能会受到严重损害，限制了参与其他日常活动的能力。本研究记录和分析了一组全面的全身生物力学和力数据数据库，这些数据是在经历过和未经历过中风的受试者进行坐站-站坐运动时捕获的。然后，这些数据与简单的机器学习算法结合使用，以预测垂直运动轨迹，这些轨迹可进一步用于辅助机器人的控制。共有 30 人（包括 6 名中风患者）在两种不同的座椅高度下各进行了 20 次坐站-站坐动作，从中创建了平均轨迹。然后，使用两种不同的关键参与者参数集（身高和体重，以及 BMI 和年龄），对加权最近邻和线性回归模型进行了使用，以生成预测轨迹。对于非中风患者，使用身高和体重参数时，产生的轨迹与真实轨迹匹配，平均 R2 评分为 0.864±0.134， = 3 和 100%的座椅高度。即使在中风患者的小样本中，也注意到了平衡和运动趋势，以及较大的类内变异性，表明需要进行更大规模的试验才能获得显著结果。每个用户的坐站-站坐动作的完整数据集都可供进一步研究公开使用。
