13 accepted papers
The nonlinear dynamics required to model walking with multi-joint lower limb exoskeleton assistance results in high computational burden. To address this, we derive a Koopman-based linearized model of the human-exoskeleton system using electromyography and ultrasound-derived metrics of volitional mu
There is increasing evidence of the role of compromised mediolateral balance in falls and the need for rehabilitation specifically focused on mediolateral direction for various populations with motor deficits. To address this need, we have developed a neurorehabilitation platform by integrating a we…
Human-in-the-loop (HIL) optimization usually optimizes assistive torque of exoskeletons to minimize the human's energetic expenditure in walking, quantified by metabolic cost. This formulation can, however, result in altered gait pattern of the human joint from the natural pattern, which is undesire…
Existing robotic lower-limb prostheses use autonomous control to address cyclic, locomotive tasks, but are inadequate in adapting to variations in non-cyclic and unpredictable tasks. This study aims to address this challenge by designing a novel electromyography (EMG)-driven musculoskeletal model fo…
Fine manual control relies on intricate action-perception coupling to effectively interact with objects <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> Here, we evaluated how electrically evoked artificial tactile sensation can be integrated in
Powered exoskeletons are promising devices to improve the walking patterns of people with neurological impairments. Providing personalized external assistance though is challenging due to uncertainties and the time-varying nature of human-robot interaction. Recently, human-in-the-loop (HIL) optimiza
Quantitatively characterizing a locomotion performance objective for a human-robot system is an important consideration in the assistive wearable robot design towards human-robot symbiosis. This problem, however, has only been addressed sparsely in the literature. In this study, we propose a new inv
Robotic exoskeletons are exciting technologies for augmenting human mobility. However, designing such a device for seamless integration with the human user and to assist human movement still is a major challenge. This paper aims at developing a novel data-driven solution framework based on reinforce…
Ankle joints play key roles in everyday locomotion, such as walking, stair climbing, and sit-to-stand. Despite the achievement in designing powered prosthetic ankles, engineers still face challenges to duplicate the full mechanics of ankle joints, including high torque, large range of motion (ROM),…
The tuning process for a robotic prosthesis is a challenging and time-consuming task both for users and clinicians. An automatic tuning approach using reinforcement learning (RL) has been developed for a knee prosthesis to address the challenges of manual tuning methods. The algorithm tunes the opti…
Robotic prostheses provide new opportunities to better restore lost functions than passive prostheses for trans-femoral amputees. But controlling a prosthesis device automatically for individual users in different task environments is an unsolved problem. Reinforcement learning (RL) is a naturally p…
This paper aims to develop an optimal controller that can automatically provide personalized control of robotic knee prosthesis in order to best support gait of individual prosthesis wearers. We introduced a new reinforcement learning (RL) controller for this purpose based on the promising ability o…
In this paper, we presented a high-power, self-balancing, passively and software-controlled active compliant, and wearable hip exoskeleton to provide walking and balance assistance. The device features powered hip abduction/adduction (HAA) and hip flexion/extension (HFE) modules to provide assistanc…