Repetitive, individual exercises can improve the functional ability of stroke survivors over the long term. With the aim of providing extra motivation to adhere to repetitive, individual rehabilitation, this paper presents a robotic coach for stroke rehabilitation. Our system uses the Pepper robot and performs one of twelve data-driven coaching policies. The policies were learned from human-human observations of professional stroke physiotherapists and provide high-level personalisation based on user information and training context. A within subjects evaluation of the system was conducted in-person involving short interactions with 3 stroke survivors. The system was able to engage the target end users and there were indications that decreased workload could be possible when using the system compared to exercising alone.

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With the aim of providing extra motivation to adhere to repetitive, individual sports training, this paper presents an autonomous robotic squash coach capable of high-level personalisation. The system was evaluated in person with 16 participants each conducting three 15-minute solo practice sessions. We compared a baseline, non-coaching robotic condition to two conditions in which the robot executed one of 12 different coaching policies, each of which was based on human coaching data. In one of the coaching conditions, the policy was selected based on categories for personalisation and in the other it was selected randomly among policies. The coaching policy conditions were found to be more enjoyable, more socially competent, and perceived as a more effective coach than the baseline.@en

Adherence to repetitive rehabilitation exercises is important in motor recovery after stroke. Similarly, repetitive solo practice exercises can improve the skill level of sports players. In both of these scenarios, regular human coaching has benefits, but in practice, the required training is often carried out alone, resulting in lowered adherence. This work presents a mixed methodology approach, novel in the context of designing for HRI, towards informing the design of a personalised robotic coach for stroke rehabilitation and squash. Using observations of human-human interactions, we first obtained action sequences of behaviours exhibited by coaches and physiotherapists. We then clustered these action sequences into behaviour graphs, with each graph representing a coaching policy usable for robotic control. Next we obtained coaches' and physiotherapists' reflections on the graphs' applicability to the real world. Finally, we provide an explanation of how the policies visualised in these graphs could be used for robotic control.

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