Adaptive Robotic Upper Limb Rehabilitation

This talk was given on 27th April 2016 by Azeemsha Thacham Poyil.

Adaptability to individual’s ability and effort level is an important aspect of rehabilitation robotics. Conventionally, this is the norm in physical therapy where the therapist adapts the therapy goal to individual needs, based on personal experience, skills and natural human-human interaction. Existing robotic therapies are designed without sufficient potentials for personalisation, e.g. to respond to pain or state of fatigue of the patient. Stroke patients may easily get tired due to their reduced muscle capabilities and reduced cognitive or motor capabilities. So, we aim to address the adaptability of rehabilitation training according to the level of individual contributions to the interaction, and based on the extent of their tiredness. We hypothesise that intensity of rehabilitation training can be altered according to the user’s fatigue assessed with the help of Electromyogram (EMG) signals. These, as well as kinematic data are studied to understand the current physical state and effort exerted by the patient during HRI sessions. Muscle fatigue can be detected from EMG signals using a range of signal processing algorithms and the corresponding EMG features can be potentially added to our kinematic benchmarks to alter the adaptive training exercises. Such an adaptive solution can also be used in a wide-range of human-machine interactions by tuning the interaction using accurate physiological and kinematic assessment. Benefiting from these, it is believed that more active contributions to the therapy results in a better recovery outcomes.

2012 © Adaptive Systems Research Group

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