Abstract
Lower limb muscle strength is an important indicator of the effectiveness of rehabilitation training for patients with stroke and hemiplegia. When patients are trained with a lower limb rehabilitation robot, real-time human–machine interaction force acquisition can not only grasp the muscle strength of patients' lower limbs in time but also serve as an important reference for active/passive training mode. To accurately collect the human–machine interaction force to identify the patient's movement intention and determine the active/passive mode, a method is proposed to collect the human–machine interaction force based on a resistive strain gauge leg pressure sensor. The method can be applied to a variety of rehabilitation platforms to collect human–machine interaction force quickly and accurately and to identify and classify behaviors through probabilistic neural networks to determine patients' active/passive training patterns. By analyzing the active/passive walking of rehabilitation patients, the motor status of patients in that period is derived. The validity and correctness of the designed sensor and determination method were verified.
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