Abstract
Muscle weakness is the primary impairment causing mobility difficulty among stroke survivors. Millions of people are unable to live normally because of mobility difficulty every year. Strength training is an effective method to improve lower extremity ability but is limited by the shortage of medical staff. Thus, this paper proposes a robot-assisted active training (RAAT) by an adaptive admittance control scheme with virtual reality interaction (AACVRI). AACVRI consists of a stiffness variable admittance controller, an adaptive controller, and virtual reality (VR) interactions. In order to provide human-robot reality interactions corresponding to virtual scenes, an admittance control law with variable stiffness term was developed to define the mechanics property of the end effector. The adaptive controller improves tracking performances by compensating interaction forces and dynamics model deviations. A virtual training environment including action following, event feedback, and competition mechanism is utilized for improving boring training experience and engaging users to maintain active state in cycling training. To verify controller performances and the feasibility of RAAT, experiments were conducted with eight subjects. Admittance control provides desired variable interactions along the trajectory. The robot responds to different virtual events by changing admittance parameters according to trigger feedbacks. Adaptive control ensures tracking errors at a low level. Subjects were maintained in active state during this strength training. Their physiological signals significantly increased, and interaction forces were at a high level. RAAT is a feasible approach for lower limb strength training, and users can independently complete high-quality active strength training under RAAT.
Highlights
There are nearly 10 million new incident stroke cases every year in the world [1,2]
The experiment results suggest that: the variable admittance controller can enable the end effector to move as a virtual bike pedal whose mechanics property is designed; LLR-II can respond to different virtual events by changing admittance parameters; the adaptive controller can compensate deviations caused by dynamics parameter variations and human-robot interaction forces; robot-assisted active training (RAAT) can engage users to maintain active state in strength training
We made a meaningful attempt that designing the stiffness term of the admittance law to define mechanics property of the end effector, and the effector will move as a virtual pedal to cooperate virtual scenes
Summary
As a leading cause of mortality and disability worldwide, stroke seriously influences survivors’ quality of life. The treatment and caring economic costs are substantial for stroke survivors’ families. The feasibility of the taskspecific training has been proven in clinical practice, and its effect can transfer to other functional motor tasks not directly practiced in therapy [5,6], whereas the mounting shortage of therapists and caregivers would become a serious problem in the near future [7]. Rehabilitation robots can effectively deliver task-specific training, which are developed as an alternative solution to meet the great potential demand for rehabilitation therapy. The clinical effect of robot-assisted therapy has been validated by many researchers [8,9,10]
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