In upper limb rehabilitation training by exploiting robotic devices, the qualitative or quantitative assessment of human active effort is conducive to altering the robot control parameters to offer the patients appropriate assistance, which is considered an effective rehabilitation strategy termed as assist-as-needed. Since active effort of a patient is changeable for the conscious or unconscious behavior, it is considered to be more feasible to determine the distributions of the passive resistance of the patient’s joints versus the joint angle in advance, which can be adopted to assess the active behavior of patients combined with the measurement of robotic sensors. However, the overintensive measurements can impose a burden on patients. Accordingly, a prediction method of shoulder joint passive torque based on a Backpropagation neural network (BPANN) was proposed in the present study to expand the passive torque distribution of the shoulder joint of a patient with less measurement data. The experiments recruiting three adult male subjects were conducted, and the results revealed that the BPANN exhibits high prediction accurate for each direction shoulder passive torque. The results revealed that the BPANN can learn the nonlinear relationship between the passive torque and the position of the shoulder joint and can make an accurate prediction without the need to build a force distribution function in advance, making it possible to draw up an assist-as-needed strategy with high accuracy while reducing the measurement burden of patients and physiotherapists.

中文翻译：

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基于BPANN的人体肩关节被动扭矩预测

在通过利用机器人设备进行上肢康复训练中，对人的积极努力进行定性或定量评估有助于改变机器人控制参数，为患者提供适当的帮助，这被认为是一种有效的康复策略，被称为“按需协助”。由于患者的主动努力对于有意识或无意识的行为是可变的，因此认为预先确定患者关节的被动阻力相对于关节角度的分布是更可行的，可以用来评估主动行为与机器人传感器的测量相结合。但是，过度密集的测量会给患者带来负担。因此，提出了一种基于反向传播神经网络（BPANN）的肩关节被动扭矩预测方法，以较少的测量数据扩展患者肩关节被动扭矩分布。进行了招募三名成年男性受试者的实验，结果表明BPANN对每个方向的肩部被动扭矩均具有较高的预测准确度。结果表明，BPANN可以学习被动扭矩与肩关节位置之间的非线性关系，并且无需预先建立力分布函数就可以做出准确的预测，从而可以绘制出辅助力。高精度的策略，同时减轻了患者和理疗师的测量负担。