UC Irvine

Stroke is a leading cause of disability, with over 80% of patients experiencing chronic upper extremity (UE) impairments that impede daily activities and degrade quality of life. While wearable sensors in the form of step counters have foundapplication in stroke rehabilitation, effective home rehabilitation of the UE using wearable sensors remains an open question. This dissertation focuses on three research questions in UE rehabilitation using a wrist-worn wearable inertial measurement unit (IMU): (1) identifying hand movements, (2) assessing the quality of movement experience (QOME) in daily life, and (3) identifying ways to improve UE QOME with wearable feedback.

We begin by reviewing existing wearable sensor technologies for at-home rehabilitation, setting the stage for our exploration of hand movement identification and arm movement quality assessment. We then introduce a spectrogram-basedconvolutional neural network (CNN) algorithm for hand movement recognition using a single wrist-worn inertial measurement unit (IMU). Our working hypothesis was that we could use machine learning to identify active flexion and extension of the fingers/wrist based on the vibrational patterns produced at the distal end of the forearm. Using wristworn IMU recordings from 22 individuals with a stroke, we found we could identify the occurrence of finger/wrist movements with approximately 75% accuracy. Thus, ringless sensing of finger/wrist movement occurrence is feasible using wrist-worn IMUs, opening up new avenues for hand-related healthcare applications.

Subsequently, we posited that it may be more effective to encourage an increase in beneficial patterns of movement (i.e. QOME), rather than simply the overall amount of movement. As a first step toward this goal, we sought to identify statisticalcharacteristics of daily arm movements that become more prominent as arm impairment decreases. Using the same data set as for the hand movement study, we identified several measures that increased as UE Fugl-Meyer (UEFM) score increased: forearm speed, forearm postural diversity (quantified by kurtosis of the tilt-angle), and forearm postural complexity (quantified by sample entropy of tilt angle). Dividing participants into severe, moderate, and mild impairment groups, we found that forearm postural diversity and complexity were best able to distinguish the groups (Cohen’s D = 1.1, and 0.99, respectively) and were also the best subset of predictors for UEFM score. Based on these findings and a large body of research in motor learning that indicates the importance of challenging and variable movement practice, we posit that encouraging people to achieve more forearm postural diversity and complexity will improve QOME and therefore will be therapeutically beneficial.

Finally, we sought to identify practical ways to improve QOME with wearable feedback. In an experiment with unimpaired individuals, we examined specific exercises, chosen from a candidate list of common exercise activities, to find whichexercises create a high level of movement diversity and complexity. Engaging in conventional rehabilitation therapy exercises created high values for forearm postural diversity but not complexity, as measured with a wrist-worn IMU. Playing the card game Speed and exercising with a commercial, gamified, home exercise sensor system produced the highest values for both postural diversity and complexity. Then, we designed an implementation for providing real-time, wearable feedback based on diversity and complexity, working toward a randomized controlled trial to assess the efficacy of QOME feedback compared to quantity feedback alone. We introduced the concept of "Quality Time" to measure the amount of time users perform complex, diverse movements. We propose an adaptive goal-setting strategy based on clinical scores of UE impairment and the statistics of complexity and diversity measured across a wide range of persons with stroke.