Publications
* indicates equal contribution. You can also check out my Google Scholar profile.2024
Volitional Control of the Paretic Hand Post-Stroke Increases Finger Stiffness and Resistance to Robot-Assisted Movement
Ava Chen*, Katelyn Lee*, Lauren Winterbottom, Jingxi Xu, Connor Lee, Grace Munger, Alexandra Deli-Ivanov, Dawn M. Nilsen, Joel Stein, and Matei CiocarlieSubmitted to IEEE RAS/EMBS Intl. Conference on Biomedical Robotics and Biomechatronics (BioRob), 2024
arXiv / abstract (+) abstract (-)
Increased effort during use of the paretic arm and hand can provoke involuntary abnormal synergy patterns and amplify stiffness effects of muscle tone for individuals after stroke, which can add difficulty for user-controlled devices to assist hand movement during functional tasks. We study how volitional effort, exerted in an attempt to open or close the hand, affects resistance to robot-assisted movement at the finger level. We perform experiments with three chronic stroke survivors to measure changes in stiffness when the user is actively exerting effort to activate ipsilateral EMG-controlled robot-assisted hand movements, compared with when the fingers are passively stretched, as well as overall effects from sustained active engagement and use. Our results suggest that active engagement of the upper extremity increases muscle tone in the finger to a much greater degree than through passive-stretch or sustained exertion over time. Potential design implications of this work suggest that developers should anticipate higher levels of finger stiffness when relying on user-driven ipsilateral control methods for assistive or rehabilitative devices for stroke.
Grasp Force Assistance via Throttle-based Wrist Angle Control on a Robotic Hand Orthosis for C6-C7 Spinal Cord Injury
Joaquin Palacios*, Alexandra Deli-Ivanov*, Ava Chen*, Lauren Winterbottom, Dawn M. Nilsen, Joel Stein, and Matei CiocarlieSubmitted to IEEE RAS/EMBS Intl. Conference on Biomedical Robotics and Biomechatronics (BioRob), 2024
arXiv / abstract (+) abstract (-)
Individuals with hand paralysis resulting from C6-C7 spinal cord injuries frequently rely on tenodesis for grasping. However, tenodesis generates limited grasping force and demands constant exertion to maintain a grasp, leading to fatigue and sometimes pain. We introduce the MyHand-SCI, a wearable robot that provides grasping assistance through motorized exotendons. Our user-driven device enables independent, ipsilateral operation via a novel Throttle-based Wrist Angle control method, which allows users to maintain grasps without continued wrist extension. A pilot case study with a person with C6 spinal cord injury shows an improvement in functional grasping and grasping force, as well as a preserved ability to modulate grasping force while using our device, thus improving their ability to manipulate everyday objects. This research is a step towards developing effective and intuitive wearable assistive devices for individuals with spinal cord injury.
Meta-Learning for Fast Adaptation in Intent Inferral on a Robotic Hand Orthosis for Stroke
Pedro Leandro La Rotta*, Jingxi Xu*, Ava Chen, Lauren Winterbottom, Wenxi Chen, Dawn M. Nilsen, Joel Stein, and Matei CiocarlieSubmitted to IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (IROS), 2024
arXiv / abstract (+) abstract (-)
We propose MetaEMG, a meta-learning approach for fast adaptation in intent inferral on a robotic hand orthosis for stroke. One key challenge in machine learning for assistive and rehabilitative robotics with disabled-bodied subjects is the difficulty of collecting labeled training data. Muscle tone and spasticity often vary significantly among stroke subjects, and hand function can even change across different use sessions of the device for the same subject. We investigate the use of metalearning to mitigate the burden of data collection needed to adapt high-capacity neural networks to a new session or subject. Our experiments on real clinical data collected from five stroke subjects show that MetaEMG can improve the intent inferral accuracy with a small session- or subject-specific dataset and very few fine-tuning epochs. To the best of our knowledge, we are the first to formulate intent inferral on stroke subjects as a meta-learning problem and demonstrate fast adaptation to a new session or subject for controlling a robotic hand orthosis with EMG signals.
Collaboration between Occupational Therapists, Engineers, and People with Neurological Conditions in the Development of Wearable Robotic Devices
Lauren Winterbottom, Dawn M. Nilsen, Rochelle Mendonca, Ava Chen, Sara Lin, Kevin Carroll, Jingxi Xu, Matei Ciocarlie, and Joel SteinAmerican Occupational Therapy Association (AOTA) INSPIRE 2024 Conference
poster / abstract (+) abstract (-)
Wearable robotic devices for the upper limb are currently being developed to enhance arm and hand function for individuals with neurological conditions such as stroke and spinal cord injury (Xu et al., 2022; Yurkewich et al., 2022). Historically, upper limb orthotic devices have often been developed with minimal input from potential consumers and are abandoned at high rates (Sugawara et al., 2018). Incorporating the needs and values of consumers during the development of such devices is crucial and can help ensure these devices are useful and acceptable (Torricelli et al., 2020). Occupational therapists can contribute to this process through collaboration with engineering teams and people with disabilities by centering stakeholder voices and driving device design to support end-user goals for pragmatic application. In this presentation, we will discuss our experiences working with engineering teams in the development of robotic devices for neurological conditions and highlight the importance of multidisciplinary collaboration. We will present data from interviews and focus groups with individuals with stroke and spinal cord injury on their priorities for a robotic device for the hand. We will then discuss challenges and opportunities for occupational therapists when collaborating on the development of rehabilitation technologies that incorporate stakeholder priorities. We will engage participants in discussion on ways to advance the involvement of occupational therapists in technology development that supports the needs of individuals living with neurological conditions.
2023
Towards Tenodesis-Modulated Control of an Assistive Hand Exoskeleton for SCI
Joaquin Palacios*, Alexandra Deli-Ivanov*, Ava Chen, Lauren Winterbottom, Dawn M. Nilsen, Joel Stein, and Matei CiocarlieIEEE/RSJ 2023 Intl. Conference on Intelligent Robots and Systems (IROS) Workshop: Assistive Robotics for Citizens
workshop paper / arXiv / poster
2022
Thumb Stabilization and Assistance in a Robotic Hand Orthosis for Post-Stroke Hemiparesis
Ava Chen, Lauren Winterbottom, Sangwoo Park, Jingxi Xu, Dawn Nilsen, Joel Stein, and Matei CiocarlieIEEE Robotics and Automation Letters (RAL), 2022; presented in IEEE RAS/EMBS 2022 Intl. Conference on Biomedical Robotics and Biomechatronics (BioRob)
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 Subjects
Ava Chen, Lauren Winterbottom, Katherine O'Reilly, Sangwoo Park, Dawn Nilsen, Joel Stein, and Matei CiocarlieIEEE 2022 Intl. Conferance on Rehabilitation Robotics (ICORR)
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 Stroke
Jingxi Xu, Cassie Meeker, Ava Chen, Lauren Winterbottom, Michaela Fraser, Sangwoo Park, Lynne M. Weber, Mitchell Miya, Dawn Nilsen, Joel Stein, and Matei CiocarlieIEEE 2022 Intl. Conference on Robotics and Automation (ICRA)
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.
2021
Rapid mid-jump production of high-performance silk by jumping spiders
Ava Chen, Kris Kim, and Paul ShambleCurrent Biology, 2021, vol. 31, no. 21, R1422-R1423
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 link2018
A device for quantitative analysis of the thumb ulnar collateral ligament
Thomas Cervantes, Woojeong E. Byun*, Ava Chen*, Kris Kim*, Kaitlyn Nealon*, Jay Connor, and Alexander SlocumASME 2018 Design of Medical Devices Conference
A device to quantitatively assess the ulnar collateral ligament of the thumb was developed to facilitate rapid and accurate diagnosis of the ligamentous injury known as Skier’s thumb.