The key idea in iterative learning control is captured by the intuition of 'practice makes perfect'. The underlying learning is based on a gradient descent algorithm iteratively optimising an appropriate input-output measured criterion. How this paradigm is used to model quantitatively, at an input/output level, the learning that happens in the context of human motor skill learning is discussed in this note. Experimental studies of human motor learning, in robotically controlled environments, indicate that a model consisting of a classical (iterative) learning control augmented with an appropriate kinematic model of human motor motion fits the observed human learning behaviour well. In the context of the rehabilitation of motor skills, such models promise better humanmachine interfaces that extend the capability and capacity of rehabilitation clinicians by creating effective robot-patient-clinician feedback loops. The economic promise of robot-assisted rehabilitation is to greatly extend the intervention capacity above what presently can be achieved by rehabilitation systems: addressing the needs of more people, over longer periods of time and at a distance in the comfort of their own personal environment. Moreover, the robot platforms provide for a more rigorous and quantitative evaluation of the patient's motor skill across the entire personal rehabilitation trajectory, which opens up opportunities for improved, more individually tuned rehabilitation regimes.
BackgroundStroke is a leading cause of disability worldwide, with upper limb deficits affecting an estimated 30% to 60% of survivors. The effectiveness of upper limb rehabilitation relies on numerous factors, particularly patient compliance to home programs and exercises set by therapists. However, therapists lack objective information about their patients’ adherence to rehabilitation exercises as well as other uses of the affected arm and hand in everyday life outside the clinic. We developed a system that consists of wearable sensor technology to monitor a patient’s arm movement and a Web-based dashboard to visualize this information for therapists.ObjectiveThe aim of our study was to evaluate how therapists use upper limb movement information visualized on a dashboard to support the rehabilitation process.MethodsAn interactive dashboard prototype with simulated movement information was created and evaluated through a user-centered design process with therapists (N=8) at a rehabilitation clinic. Data were collected through observations of therapists interacting with an interactive dashboard prototype, think-aloud data, and interviews. Data were analyzed qualitatively through thematic analysis.ResultsTherapists use visualizations of upper limb information in the following ways: (1) to obtain objective data of patients’ activity levels, exercise, and neglect outside the clinic, (2) to engage patients in the rehabilitation process through education, motivation, and discussion of experiences with activities of daily living, and (3) to engage with other clinicians and researchers based on objective data. A major limitation is the lack of contextual data, which is needed by therapists to discern how movement data visualized on the dashboard relate to activities of daily living.ConclusionsUpper limb information captured through wearable devices provides novel insights for therapists and helps to engage patients and other clinicians in therapy. Consideration needs to be given to the collection and visualization of contextual information to provide meaningful insights into patient engagement in activities of daily living. These findings open the door for further work to develop a fully functioning system and to trial it with patients and clinicians during therapy.
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