# Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics
## 1 Introduction  
In recent years, special attention has been given to the development of mechatronic rehabilitation systems, which can provide high intensity and frequency of treatment for improved motor recovery [1], particularly when used in a tele-rehabilitation framework. For lower limb rehabilitation, robotic systems are now commercially available as an alternative to conventional therapy, and technological complement to Body-Weight Supported Treadmill Training (BWSTT) [2, 3]. Generally, robot-assisted lower limb therapy consists of the simulation of joint or foot path, such as gait trajectories produced by non-impaired people. The resulting movement may possibly integrate other technologies, such as Functional Electrical Stimulation [4], or additional levels of control, such as the ability to assist-as-needed [5, 6]. Mechanical support is often delivered to the patient using either an exoskeletal device [2], or an end-effector-based robot [3, 5]. While exoskeleton-based systems may lessen the strain on therapists, studies suggest that patient engagement on therapy may also be reduced [7], which may indicate that the neurological pathways required for rehabilitation are notproperly stimulated. In view of this issue, recent research has proposed new treadmill control schemes [8], as well as structurally innovative treadmills [9]. Nevertheless, in this work, we focus on end-effector systems. End-effector-based lower limb rehabilitation systems represent an alternative to these approaches. Some of those systems were designed for BWSTT [3; 5], while others have targeted ankle rehabilitation only [10; 11]. To the best of our knowledge, none of these systems provide BWSTT capability and independent six degree-of-freedom (DOF) movement of each foot. A system combining such features may not only be used for gait training, but also in balance assessment and exercises, an area that has also seen development of specific methods [12] and devices [13]. This study utilizes a gait simulator system based on two Stewart-Gough platforms used for lower limb rehabilitation. A thorough description of its mechanical design and implementation is reported in [14]. We propose in this paper novel control strategies that provide the system with additional tele-rehabilitation capabilities. In terms of control strategies, force control is often used in robot-aided rehabilitation and, more generally, in human-robot interaction. Despite the advantages of approaches based on force control, challenges exist, particularly in a tele-rehabilitation scenario [15], to apply these methods in our SGP-based system. Hence, the original version of the gait simulator employed position control, simply emulating standard gait trajectories, which remained fixed for the duration of the rehabilitation session. In order to improve overall safety (especially for use in tele-rehabilitation), provide a more intuitive therapist interface, and enable more active patient engagement, two main techniques are proposed in this paper. Firstly, we propose a robot walking velocity modulation based on patient motion (Self-Modulation Mode, SMM), which is similar to the idea proposed in [13]. However, instead of using a mechanical structure attached to the upper limb, we have used wearable sensors on the trunk, and velocity is updated based on the patient's posture. Secondly, the platforms may also be directly controlled using the therapist's mirrored motion (Therapist Modulation Mode, TMM). In our work, we have applied a similar approach as in [16], i.e., by using an intuitive interface based on wearable sensors, the therapist may control the slave robotic system intuitively and possibly from a remote location. Indeed, both control strategies proposed in this work may be further expanded by the use of tele-rehabilitation, in which the therapist is located remotely [17]. Within a session employing TMM, the therapist may control the system motion using his own body movement, while monitoring patient response.