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UBC Theses and Dissertations

The use of physiological signals and motor performance metrics in task difficulty adaptation : improving engagement in robot-assisted movement therapy Shirzad, Navid


Before robot-assisted therapy regimens can be included in clinical practice, one of the major challenges to overcome is maintaining the patient’s engagement in the therapy during the lengthy functional recovery period. Game designers and psychologists have theorized the mechanics of sustaining an individual’s engagement in a task. In a motor learning context, to maintain motivation to continue an exercise, one must be kept exercising at one’s desirable difficulty by manipulation of the task challenge over the course of treatment. Thus, this work was aimed to design a robotic therapy regimen that can automatically adjust the difficulty to motivate users to continue with the exercise. The main contributions of this thesis are to develop a method to predict the user’s desirable difficulty and validate the effects of adaptively adjusting a robotic exercise on the user’s perception of the task. The theory of desirable difficulty relies on three main factors: meaningful levels of difficulties, knowledge of the user’s challenge preference, and positive effects of exercising a task under the desirable difficulty conditions. Studies to develop implementations of the first two factors in the context of an upper-limb reaching task were conducted, and investigated the effects of practicing this task under the desirable difficulty conditions. The first study implemented five error amplification (EA) methods for a reaching task and validated that users perceive each with a different challenge level. In the second study, users’ physiological and motor performance metrics were collected, as well as self-reports of the user’s challenge preference after exercising with each of the EAs. The efficiency of different machine learning methods in predicting a user’s challenge preference based on different combinations of physiological and motor performance attributes were analyzed. In the third study, the control group received EAs in predefined random order while the experimental group received EAs based on the predictions of the trained machine learning algorithm. The experimental group reported statistically significant higher scores on the metrics that assessed satisfaction, attentiveness, and willingness to continue the task. These results support the approach of designing a robotic system capable of adjusting exercises to prolong individuals’ engagement in stroke therapy.

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