I work towards robots that are more intuitive to put on, customize, and operate so that their users are incentivized to use their impaired hands more often and more broadly. My main focus is electromechanical design and robotic adaptation to stroke-specific biomechanics. I am also involved in the integration of adaptive intent detection algorithms that make use of multimodal sensing, as well as with conducting need-finding and usability case studies to have conversations with the stroke survivor community about how to make devices that meet their real-world needs.
Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke HemiparesisAva Chen, Lauren Winterbottom, Sangwoo Park, Jingxi Xu, Dawn Nilsen, Joel Stein, and Matei Ciocarlie
In IEEE Robotics and Automation Letters (RAL); presented in IEEE RAS/EMBS Intl. Conference on Biomedical Robotics and Biomechatronics (BioRob), 2022.
We propose a dual-cable method of stabilizing the thumb in the context of a hand orthosis designed for individuals with upper extremity hemiparesis after stroke. This cable network adds opposition/reposition capabilities to the thumb, and increases the likelihood of forming a hand pose that can successfully manipulate objects. In addition to a passive-thumb version (where both cables are of fixed length), our approach also allows for a single-actuator active-thumb version (where the extension cable is actuated while the abductor remains passive), which allows a range of motion intended to facilitate creating and maintaining grasps. We performed experiments with five chronic stroke survivors consisting of unimanual resistive-pull tasks and bimanual twisting tasks with simulated real-world objects; these explored the effects of thumb assistance on grasp stability and functional range of motion. Our results show that both active- and passive-thumb versions achieved similar performance in terms of improving grasp force generation over a no-device baseline, but active thumb stabilization enabled users to maintain grasps for longer durations.
Design of Spiral-Cable Forearm Exoskeleton to Provide Supination Adjustment for Hemiparetic Stroke SubjectsAva Chen, Lauren Winterbottom, Katherine O'Reilly, Sangwoo Park, Dawn Nilsen, Joel Stein, and Matei Ciocarlie
In IEEE Intl. Conferance on Rehabilitation Robotics (ICORR), 2022.
We present the development of a cable-based passive forearm exoskeleton that is designed to assist supination for hemiparetic stroke survivors. Our device uniquely provides torque sufficient for counteracting spasticity within a below-elbow apparatus. The mechanism consists of a spiral single-tendon routing embedded in a rigid forearm brace and terminated at the hand and upper-forearm. A spool with an internal releasable-ratchet mechanism allows the user to manually retract the tendon and rotate the hand to counteract involuntary pronation synergies due to stroke. We characterize the mechanism with benchtop testing and five healthy subjects, and perform a preliminary assessment of the exoskeleton with a single chronic stroke subject having minimal supination ability. The mechanism can be integrated into an existing active hand-opening orthosis to enable supination support during grasping tasks, and also allows for a future actuated supination strategy.
Adaptive Semi-Supervised Intent Inferral to Control a Powered Hand Orthosis for StrokeJingxi Xu, Cassie Meeker, Ava Chen, Lauren Winterbottom, Michaela Fraser, Sangwoo Park, Lynne M Weber, Mitchell Miya, Dawn Nilsen, Joel Stein, and Matei Ciocarlie
In IEEE Intl. Conference on Robotics and Automation (ICRA), 2022.
In order to provide therapy in a functional context, controls for wearable orthoses need to be robust and intuitive. We have previously introduced an intuitive, user-driven, EMG based method to operate a robotic hand orthosis, but the process of training a control that is robust to concept drift (changes in the input signal) places a substantial burden on the user. In this paper, we explore semi-supervised learning as a paradigm for controlling a powered hand orthosis for stroke subjects. To the best of our knowledge, this is the first use of semi-supervised learning for an orthotic application. Specifically, we propose a disagreement-based semi-supervision algorithm for handling intrasession concept drift based on multimodal ipsilateral sensing. We evaluate the performance of our algorithm on data collected from five stroke subjects. Our results show that the proposed algorithm helps the device adapt to intrasession drift using unlabeled data and reduces the training burden placed on the user. We also validate the feasibility of our proposed algorithm with a functional task; in these experiments, two subjects successfully completed multiple instances of a pick-and-handover task.
Previous Work ( * — indicates equal contribution )
Rapid mid-jump production of high-performance silk by jumping spidersAva Chen, Kris Kim, and Paul Shamble
In Current Biology, DOI: 10.1016/j.cub.2021.09.053
Jumping spiders (Salticidae) do not rely on webs to capture their prey, but they do spin a silk dragline behind them as they move through their habitat. They also spin this dragline during jumps, continuously connecting them with the surface they leapt from. Since spiders cannot spin silk in advance, this silk must be spun at the same speed as the spider jumps—in effect, requiring spin speeds over ten times faster than typical. Past work in other spider species has found that silk spinning rates in excess of walking and web-building speeds (~2-20mm/s) result in lower quality silk and even dragline failure. Surprisingly, here we found that despite being spun at high speeds (~500-700mm/s; 100-140 body lengths/s), jump-spun silk showed consistent, uniform structure as well as the high-performance qualities characteristic of silk spun by other spiders, including orb-weaving species, at low speeds. Toughness of this jump-spun silk (mean = 281.9MJ/m3) even surpassed reported values for all but the toughest orb-weaver draglines. This provides the first evidence that salticids are capable of spinning high-performance silk and are able to do so extremely rapidly under natural conditions.————— supplemental files download link
A device for quantitative analysis of the thumb ulnar collateral ligamentThomas Cervantes, Woojeong E Byun*, Ava Chen*, Kris Kim*, Kaitlyn Nealon*, Jay Connor, and Alexander Slocum
In ASME Design of Medical Devices Conference, 2018.