# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 4 Experiments and Results  
### Performance Evaluation for the Patient-Exoskeleton Interaction  
The exoskeleton adopts a high geared mechanism for guaranteeing enough torque output with a lightweight design; however, the passive character of the actuator's non-backdrivability limits the ability to generate variable impedance output. The non-backdrivability means that the mechanical system should have low impedance [39]. For our proposed therapist-in-the-loop platform, the actuation and control system should provide both robot-in-charge and patient-in-charge modes. When the patient can control some movements, the patient-in-charge mode is needed where the robot should not hinder the patient motion, but instead it should show near-zero impedance while assisting the user via the visually shared model. The near-zero impedance output is also a precondition for generating resistive force. Therefore, it is necessary to carry out an experiment to evaluate the near-zero impedance output of the control system.  
For the exoskeleton, a torque limiter mechanism was designed for the actuator; and by adjusting the clamp nut, the friction between the axle sleeve of the motor and the helical capstan shaft can be controlled [24]. In other words, we can release the clamp nut, so that the elbow motor will not provide the power to the robot frame. Two comparison experiments were carried out, and both muscle activities and the interactive force between forearm and device were used to evaluate the performance. In case one, the clamp nut was released, and the involved subject could freely flex his forearm. In case two, the clamp nut was screwed tightly. Thus, the control system will play a role in generating near-zero impedance, i.e., the exoskeleton can track the forearm motion freely. The subject also flexed his forearm as in case one. The muscle activities for biceps muscle were measured with a sEMG, because only the biceps muscle was involved for elbow flexion motion. The data acquisition and processing process are shown in Figure 5. The recorded sEMG signals were pre-processed by a commercial filter box (10-500 Hz band-pass, 60 Hz band-rejection, amplified 1000 times, Personal EMG, Oisaka Development Ltd., Fukushima, Japan) with differential amplification (gain: 1000) and common mode rejection (104 dB) at a sampling frequency of 3000 Hz and then sampled at 1000 Hz by a 16-bit A/D acquisition device (USB4716, 16-channel inputs, Advantech Co., Ltd., Taiwan). Other detailed information for signal acquisition can be found in our previous research [27]. Post-processed sEMG signals were then full wave rectified and low-pass filtered by a 1 st order Butterworth filter with a cut-off frequency of 10 Hz to acquire the envelope, and the interaction force was measured with a force sensor (FS03, Honeywell, Morristown, NJ, USA) attached between the belt and sampled with the A/D board simultaneously. The MTx sensor measures the flexion angle of the forearm simultaneously.  
The experimental result for case one is shown in Figure 6, where the biceps muscle activities and interaction force were recorded while the forearm flexed. The maximum value for the interaction force is 0.098 N, and the mean value for the sEMG signal is 0.0366 V. On the contrary, for case two, which is  
Figure 5: Data acquisition and data processing.  
shown in Figure 7, the maximum value for the interaction force is 0.2127 N while the mean value for the sEMG signal is 0.0376 V. By comparing these two experiments, it can be seen that the control system performs well; the proposed impedance controller effectively solves the backdrivability problem.