America’s housing affordability crisis has had various indirect costs on health and safety among people living with disability. The skyrocketing housing prices have exponentially increased with the onset of the COVID-19 pandemic leaving many people at risk for eviction after federal and local moratoriums providing protection during the pandemic expire. Americans with disabilities have been particularly affected by the affordability crisis and it is expected that this major public health problem will only grow as government-provided protections and supports wane. It is critical that both government and various housing organizations consider ways to support affordability, quality, and accessibility in this particularly hard-hit population.

We describe a learning-from-demonstration technique that enables a general-purpose humanoid robot to lead a user through object-mediated upper-limb exercises. It needs only tens of seconds of training data from a therapist teleoperating the robot to do the task with the user. We model the robot behavior as a regression problem, inferring the desired robot effort using the end-effector's state (position and velocity). Compared to the conventional approach of learning time-based trajectories, our strategy produces customized robot behavior and eliminates the need to tune gains to adapt to the user's motor ability. In our study, one occupational therapist and six people with stroke trained a Willow Garage PR2 on three example tasks (periodic 1D and 2D motions plus episodic pick-and-place). They then repeatedly did the tasks with the robot and blindly compared the state- and time-based controllers learned from the training data. Our results show that working models were reliably obtained to allow the robot to do the exercise with the user; that our state-based approach enabled users to be more actively involved, allowed larger excursion, and generated power outputs more similar to the therapist demonstrations; and that the therapist found our strategy more agreeable than the traditional time-based approach.

Wearable robotic devices are currently being developed to improve upper limb function for individuals with hemiparesis after stroke. Incorporating the views of clinicians during the development of new technologies can help ensure that end products meet clinical needs and can be adopted for patient care. In this cross-sectional mixed-methods study, an anonymous online survey was used to gather clinicians' perceptions of a wearable robotic hand orthosis for post-stroke hemiparesis. Participants were asked about their clinical experience and provided feedback on the prototype device after viewing a video. 154 participants completed the survey. Only 18.8% had previous experience with robotic technology. The majority of participants (64.9%) reported that they would use the device for both rehabilitative and assistive purposes. Participants perceived that the device could be used in supervised clinical settings with all phases of stroke. Participants also indicated a need for insurance coverage and quick setup time. Engaging clinicians early in the design process can help guide the development of wearable robotic devices. Both rehabilitative and assistive functions are valued by clinicians and should be considered during device development. Future research is needed to understand a broader set of stakeholders' perspectives on utility and design.