People with severe trunk impairments cannot maintain or control upright posture during sitting or reaching out with the upper body. Passive orthoses are clinically available to support the trunk and promote the use of upper extremities in this population. However, these orthoses only rigidly position the torso on a wheelchair but do not facilitate movement of the trunk. In this dissertation, we introduce a novel active-assistive torso brace system for upperbody movements by a subject while seated. We have named this system as Wheelchair Robot for Active Postural Support (WRAPS). We propose designs of two robots, one for the pelvis and the other for the trunk. Each of the two devices has a parallel chain architecture to accommodate the range of motion (ROM), respectively for the pelvic and thoracic segments. The first thoracic robot was designed for the upper trunk motion relative to the pelvis. It has a 2[RP]S-2UPS architecture which provides four degrees-of-freedom (DOFs) to the end-effector placed on the upper trunk. The second is a pelvic robot which is designed to orient the pelvic segment relative to the seat. It has a 3-DOF [RRR]U-2[RR]S architecture, coupled with translation to accommodate pelvic movements relative to the seat. These robot architectures are synthesized based on human movement data. WRAPS can modulate the displacement of both the pelvic and the thoracic segments. Additionally, the forces can be applied on the torso through the end-effectors of these robots. Each of the robot prototypes was evaluated with able-bodied subjects to assess the device wearability, kinematic performances, and control system.

In people with neuromotor deficits of trunk and lower extremities, maintaining and regaining balance is a difficult task. Many undergo rehabilitation to improve their movement capabilities, health, and overall interactions with their environment. Rehabilitation consists of a set of interventions designed to improve the individual’s mobility and independence. These strategies can be passive, active or task-specific and are dependent on the type of injury, how the individual progresses, and the intensity of the activity. Some of the common rehabilitation interventions to strengthen muscles and improve coordination are accomplished either by the manual assistance of a physical therapist, bodyweight suspension systems or through robotic-assisted training. There are several types of rehabilitation robotic systems and robotic control strategies.However, there are few robotic studies that compare their robotic device’s control strategy to common rehabilitation interventions. This dissertation introduces robotic strategies centered around rehabilitation ones and characterizes human motion in response to the robotic forces. Two cable-driven robotic systems are utilized to implement the robotic controllers for different tasks. Further details of the two cable-driven systems are discussed in Chapter 1. The validation and evaluation of these robotic strategies for standing rehabilitation is discussed in Chapter 2. A case study of a robotic training paradigm for individuals with spinal cord injury is presented in Chapter 3. Chapter 4 introduces a method to redistribute individuals’ weight using pelvic lateral forces. Chapter 5 and 6 characterizes how young and older groups respond to external perturbations during their sit-to-stand motion. This dissertation presents robotic strategies that can be implemented as rehabilitation interventions. It also presents how individuals’ biomechanics and muscle responses may change depending on the force control paradigm.These robotic strategies can be utilized by training individuals to improve their reactive and active balance control and thus reduce their risk of falling.

Dynamic postural control requires regulating body alignment to achieve postural stability and orientation during functional movements. This ability may be impaired in people with neuromotor disorders, challenging them in performing daily activities. Conventional training strategies, such as muscle strengthening, joint locking, and proprioceptive training, are known to improve posture control. However, providing sufficiently rich intervention and maintaining high training intensity can be labor-intensive and expensive. Therefore, novel technologies are being explored to overcome the challenges. Robot-assisted training is an emerging technology in posture rehabilitation. To maximize motor improvement, the assist-as-needed strategy is widely used in robotic platforms to provide adaptive assistance based on patients' functional ability. A prevailing paradigm employing the assist-as-needed strategy is the boundary-based assist-as-needed (BAAN) controller, which provides assistive forces when the center of mass moves beyond the stability boundary. This dissertation investigates the mechanisms underlying the efficacy of BAAN force fields and explores novel approaches to enhance the therapeutic effectiveness of BAAN robotic posture training protocols. In Chapter 1, we outline the research background and introduce the main content of the following chapters in this dissertation. We also describe two cable-driven robotic platforms with BAAN controllers: the Robotic Upright Stand Trainer (RobUST) for standing posture training and the Trunk Support Trainer (TruST) for sitting posture training. In Chapter 2, we present a study using the RobUST platform to investigate how the BAAN force field impacts muscle synergy in the lower limbs during standing posture training. This pilot study provides insights into understanding the neuromuscular basis of the BAAN robotic rehabilitation strategy and helps explain its effectiveness. In Chapter 3, we present a deep learning-based dynamic boundary design for the BAAN controller. We conducted a controlled experiment with 20 healthy subjects using the TruST platform to test the dynamic boundary's effectiveness. This study highlights the clinical potential of the dynamic boundary design in BAAN robotic training. Extended reality (XR) technology, including Virtual reality (VR) and augmented reality (AR), is gaining popularity in posture rehabilitation. XR has the potential to be combined with BAAN robotic training protocols to maximize postural control improvement. In Chapter 4, we conducted a randomized control experiment with sixty-three healthy subjects to compare the effectiveness of TruST intervention combined with VR or AR against TruST training alone. This study provides novel insights into the added value of XR to BAAN robot-assisted training and the differences between AR and VR when integrated into robotic training protocols. Motor skills acquired through BAAN robot-assisted training necessitate consistent follow-up practice for long-term maintenance. However, due to portability limitations, BAAN robot-assisted training faces challenges in providing follow-up training after high-intensity in-lab robotic interventions. In Chapter 5, we present a remote XR rehabilitation system with markerless motion tracking for sitting posture training. This remote XR framework holds promise as an adjunctive training approach to complement existing BAAN robot-assisted training methods, maximizing motor improvements.