Abstract：
Wheelchair propulsion interventions typically teach manual wheelchair users to perform wheelchair propulsion biomechanics as recommended by the Clinical Practice Guidelines (CPG). Outcome measures for these interventions are primarily laboratory based. Discrepancies remain between manual wheelchair propulsion (MWP) in laboratory-based examinations and propulsion in the real-world. Current developments in machine learning (ML) allow for monitoring of MWP in the real world. In this study, we collected data from participants enrolled in two wheelchair propulsion interventions, then built an ML algorithm to distinguish CPG recommended MWP patterns from non-CPG-recommended patterns. Eight primary manual wheelchair users did not initially follow CPG recommendations but learned and performed CPG propulsion after the interventions. Participants each wore two inertial measurement units as they propelled their wheelchairs on a roller system, indoors overground, and outdoors. ML models were trained to classify propulsion patterns as following the CPG or not following the CPG. Video recordings were used for reference. For indoor detection, we found that a subject-independent model was able to achieve 85% accuracy. For outdoor detection, we found that the subject-independent model achieved 75.4% accuracy. These results provide further evidence that CPG and non-CPG-recommended MWP patterns can be predicted with wearable sensors using an ML algorithm.
摘要：
轮椅推进干预措施通常教导手动轮椅使用者按照临床实践指南（CPG）的建议执行轮椅推进生物力学。这些干预措施的结果措施主要基于实验室。基于实验室的检查中的手动轮椅推进（MWP）与现实世界中的推进之间仍然存在差异。机器学习（ML）的当前发展允许在现实世界中监控MWP。在这项研究中,我们收集了两项轮椅推进干预措施的参与者的数据，然后构建了一个ML算法来区分CPG推荐的MWP模式和非CPG推荐的模式。八名主要的手动轮椅使用者最初没有遵循CPG建议，而是在干预后学习并执行了CPG推进。参与者在滚轮系统上推动轮椅时，每个人都穿着两个惯性测量单元，室内地上，和户外。训练ML模型以将推进模式分类为遵循CPG或不遵循CPG。视频记录用作参考。对于室内检测，我们发现独立于受试者的模型能够达到85%的准确率.对于室外检测，我们发现独立于受试者的模型达到了75.4%的准确率.这些结果提供了进一步的证据，表明可以使用ML算法通过可穿戴传感器预测CPG和非CPG推荐的MWP模式。
