# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 1 Introduction  
Activities of daily living can be impacted by many events such as new or recurrent strokes, which can impair the motor control of the upper limbs. Stroke has also become a leading cause of severe long-term disability and stroke patients are predicted to increase by 4 million by 2030 in the USA [1]. Compared with manual therapy, robot-aided therapy has been certified as more effective and suitable for rehabilitation without fatigue for long-time therapy [2]. Many rehabilitation systems have been developed for performing the training and can be generally divided into endpoint types [3; 4] and exoskeleton types [5; 6]. Between them, endpoint types are more straightforward to adjust to accommodate different patients and attach to the distal end of patients' limbs and they do not suffer from joint alignment problems [7]. The drawback is that end-effector type rehabilitation robots have to constrain the redundancy of the patient's arm and this restriction limits the training in hand and wrist function. Contrarily, although exoskeleton devices have more complicated system dynamics and mechanical structure, they can provide the training and obtain the feedback for each joint independently. Moreover, some devices use cable mechanisms to transmit the power of motors mounted on thebackboard to the corresponding joints and these devices are compact, lightweight and comfortable for patients [8; 9; 10]. In addition, some research has also proved that robotic therapy could be less effective than conventional manual therapy in some situations [11]. Rehabilitation robots need to adjust the training strategy and intensity to the specific requirements and abilities of different individuals. Some adaptive methods have been proposed for specific training. For instance, [PERSON] et al. designed a minimal assist-as-needed (mAAN) controller, which uses a model-based sensorless force estimation method to determine subject capability and provide required minimal assist-as-needed rehabilitation [12]. [PERSON] et al. designed the partially observable Markov decision process (POMDP), which allows an automated rehabilitation system to autonomously adjust different exercise parameters according to each individual's needs [13]. Artificial intelligence (AI) methods such as artificial neural networks [14] have also been employed for improving the intelligence when performing active assistance to a user's affected arm. However, although these adaptive methods can improve the performance of training, the experience and knowledge of a skilled therapist can still not be matched well due to the lack of flexibility in tuning the control parameters compared to conventional therapy. Therefore, patient-specific training involving therapists can be adjusted to the training strategy and assistive/resistive therapeutic force fields for different individuals can be applied by the therapists, according to their abundant experience [15; 16]. For instance, [PERSON] et al. proposed two new frameworks which are haptics-enabled teleoperated supervised training and electromyography-based indirect supervised training for tuning the strategy and intensity of training in a patient-specific manner [15]. Because of the inconvenience of regularly attending rehabilitation centers, home-based tele-rehabilitation systems are likely to become a trend. However, the absence of the supervision of a skilled therapist will become an issue. Some researchers have applied a motion capture device, like Kinect developed by the company of Microsoft, to assist with guidance, correction, management and assessment of patients' movements [17; 18; 19; 20; 21; 22]. [PERSON] et al. proposed a system with Kinect and a haptic device which can simulate the pressure the therapist applies to training [24]. However, the force feedback is absent in the majority of tele-rehabilitation systems. In this study, a therapist-in-the-loop training platform is proposed for elbow joint motor function recovery.