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Towards the development of a robust model for estimating wrist torque at different wrist angles

机译:致力于开发一种强大的模型来估算不同腕部角度下的腕部扭矩

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Several robotic devices have been developed to assist patients regain their autonomy. These robotic devices augment forces exerted by the device wearer. A majority of these devices are controlled by surface electromyography (SEMG) signals acquired from wearer's muscles. Several regression models are available for estimation of wrist torques using SEMG signals. Common issues related to torque estimation models are degradation of model accuracy when the limb posture is altered. This degradation renders these models unsuitable for practical applications. For this reason, a model is sought that is unaffected by varying arm postures and joint angles. Obtained results for overcoming this problem are presented.
机译:已经开发了几种机器人设备来帮助患者恢复自主权。这些机器人设备增加了设备佩戴者施加的力。这些设备中的大多数受从穿戴者的肌肉获取的表面肌电图(SEMG)信号控制。有几种回归模型可用于使用SEMG信号估算手腕扭矩。与扭矩估计模型相关的常见问题是当肢体姿势发生更改时模型准确性下降。这种降级使这些模型不适合实际应用。因此,寻求一种不受手臂姿势和关节角度变化影响的模型。提出了克服该问题的结果。

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