PhD Candidate (5th Year), Teaching Fellow, University of California Los Angeles
General facts: Every year in the world, there are 15 million people suffering stroke, one of the leading causes of severe long-term disability. Depending on the position of stroke-induced brain trauma, post-stroke patients are often with various levels of arm weakness and musculoskeletal stiffness, thus limited participation in activities of daily living (ADLs). As many of these patients cannot afford the expense of hiring skillful physical therapists for post-stroke rehabilitation training, they miss the opportunity in regaining abilities to work and live independently. Due to the current large number of patients and the inevitable population aging trend, a strategic solution is needed for this society. Proposed solution: Post-stroke rehabilitation is mainly a repetitive training process, engineers and neurologists, therefore, work on robotic systems to reduce the labor cost and increase the training dosage. Among research groups in the world, UCLA Bionics Lab I am working at is one of the pioneers in upper limb robotic exoskeleton development. In one pilot study done at UCSF medical center, patients trained with our 3rd generation dual-arm exoskeleton EXO-UL7 exhibited promising recovery. Detailed challenges and my contribution: Control algorithms to an exoskeleton robot is like brains to a human being. Researchers would like to bring exoskeleton intelligence in 1) providing necessary assistance and challenges during rehabilitation; 2) understanding the level of disability of different stroke patients and making training decisions accordingly; 3) engaging patients in large dosage training and accelerating the recovery. Considering human’s uncertainty, human-in-a-loop research is traditionally more challenging than on a fully automated system. To achieve the intelligent performance, I have worked on physical human-robot interaction analysis, human motion understanding and virtual reality task design and implementation on our 4th generation dual-arm exoskeleton EXO-UL8. Part of the work has been recently accepted as a paper contribution (“Upper Limb Redundancy Resolution Under Gravitational Loading Conditions: Arm Postural Stability Index Based on Dynamic Manipulability Analysis”: Yang Shen, Brandon Po-Yun Hsiao, Ji Ma, and Jacob Rosen) to IEEE-RAS International Conference on Humanoid Robotics 2017. I am also invited to speak at “Exoskeleton Design Through Optimization and Adaptive Control” workshop which would be held on the first day of the conference.
Abstract: For a wearable robotic system which includes the same redundancy as the human arm, configuring the joint angles of the robotic arm in accordance with those of the operators arm is one of the crucial control mechanisms to minimize the energy exchange between human and robot. Thus it is important to understand the redundancy resolution mechanism of the human arm such that the inverse kinematics solution of these two coupled systems becomes identical. In this paper, the redundancy resolution of the human arm based on the wrist position and orientation is provided as a closed form solution for the practical robot control algorithm, which enables the robot to form the natural human arm configuration as the operator changes the position and orientation of the end effector. For this, the redundancy of the arm is expressed mathematically by defining the swivel angle. Then the swivel angle is expressed as a superposition of two components, which are reference swivel angle and the swivel angle offset, respectively. The reference swivel angle based on the wrist position is defined by the kinematic criterion that maximizes the manipulability along the vector connecting the wrist and the virtual target point on the head region as a cluster of important sensory organs. Then the wrist orientation change is mapped into a joint angle availablility function output and translated to the swivel angle offset with respect to the reference swivel angle. Based on the inverse kinematic formula the controller can transform the position and orientation of the end-effector into the joint torque which enables the robot to follow up the operator’s current joint configuration. The estimation performance was evaluated by utilizing a motion capture system and the results show that there is a high correlation between the estimated and calculated swivel angles.
Pub.: 18 Feb '15, Pinned: 14 Oct '17
Abstract: Survivors post stroke commonly have upper limb impairments. Patients can drive neural reorganization, brain recovery and return of function with task specific repetitive training (TSRT). Fifteen community independent stroke survivors (25-75 years, >6 months post stroke, Upper Limb Fugl Meyer [ULFM] scores 16-39) participated in this randomized feasibility study to compare outcomes of upper limb TSRT guided by a robotic orthosis (bilateral or unilateral) or a physical therapist. After 6 weeks of training (18 h), across all subjects, there were significant improvements in depression, flexibility, strength, tone, pain and voluntary movement (ULFM) (p < 0.05; effect sizes 0.49-3.53). Each training group significantly improved ULFM scores and range of motion without significant group differences. Virtual or actual TSRT performed with a robotic orthosis or a physical therapist significantly reduced arm impairments around the shoulder and elbow without significant gains in fine motor hand control, activities of daily living or independence.
Pub.: 06 Aug '13, Pinned: 14 Oct '17
Abstract: Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling - regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.
Pub.: 01 May '16, Pinned: 14 Oct '17