Department of Mechanical Science and Engineering, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Department of Health and Kinesiology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Sensors (Basel). 2024 Aug 15;24(16):5297. doi: 10.3390/s24165297.
The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be beneficial for wheelchair users, but further work is needed to establish appropriate guidelines and requires assessment of the external loads. The goal of this research was to predict the six-degree-of-freedom external loads during handcycling from data similar to those which can be measured from inertial measurement units (segment accelerations and velocities) using machine learning. Five neural network models and two ensemble models were compared against a statistical model. A temporal convolutional network (TCN) yielded the best predictions. Predictions of forces and moments in-plane with the crank were the most accurate (r = 0.95-0.97). The TCN model could predict external loads during activities of different intensities, making it viable for different exercise protocols. The ability to predict the loads associated with forward propulsion using wearable-type data enables the development of informed exercise guidelines.
脊髓损伤患者心血管疾病风险增加，促使人们努力寻找改善健康结果而不会造成肌肉骨骼损伤风险的运动方式。手摇车是一种可能对轮椅使用者有益的运动方式，但需要进一步的工作来制定适当的指南，并需要评估外部负荷。本研究的目的是使用机器学习，从类似于惯性测量单元（节段加速度和速度）可测量的数据中，对手摇车的六自由度外部负荷进行预测。五种神经网络模型和两种集成模型与统计模型进行了比较。时间卷积网络（TCN）产生了最佳预测。与曲柄平面内的力和力矩的预测最为准确（r = 0.95-0.97）。TCN 模型可以预测不同强度活动中的外部负荷，使其适用于不同的运动方案。使用可穿戴式数据预测与向前推进相关的负荷的能力，为制定明智的运动指南提供了可能。
