UC Irvine

The hand is a highly complex machine as evidenced by its mechanical structure and the large amount of cortical resources it requires for both sensation and motor control. Stroke is a pervasive, global problem that causes disability by damaging hand neural control systems. Movement practice can help drive the changes in neural connectivity needed to restore these systems, however, stroke patients typically undertake limited amounts of movement practice. The premise of this dissertation is that mechanical engineering techniques, and, specifically, the appropriate design of robotic therapy technologies based on an engineering-informed understanding of human hand mechanics and function, can improve the biomedical situation for individuals after a stroke.

Specifically, this dissertation addresses the question “How do we optimize the usage of wearable robotic technologies for hand rehabilitation after stroke?” Here we demonstrate progress in answering this question by considering three key areas: usership patterns of wearable hand sensing technology in real-world settings, sensory and motor control of the hand after stroke, and the mechanical design and intuitive control of wearable soft robotic technologies for the hand.

Regarding usership patterns, we studied a simple wearable sensor – the MusicGlove – in the home setting with individuals in the sub-acute phase of stroke. We found that only 14% of stroke patients have enough residual function in the hand for sensor-only rehabilitation, motivating us to work toward a device that can offer robotic assistance. Further, we demonstrated a connection between machine failure theory and usership via the functional form of the statistical distribution of the amount of use. Finally, we observed that -- when left to self-adjust the parameters of their worn device -- people make logical decisions relating to challenge, suggesting the strategy of building rehabilitation devices that allow individuals freedom by which to adapt their own control strategies.

In the area of sensory and motor control we address two specific questions: How does isometric grip force control compare to other aspects of hand function after stroke, and how do sensory deficits measured robotically correlate to motor function after stroke? Through a series of experiments conducted with chronic stroke survivors we showed that isometric grip force control is not only a well preserved control signal after stroke, but is also more preserved than strength or manual dexterity. This provided the conceptual basis for a novel exoskeleton control strategy -- residual force control – in which isometric grip control by some fingers drivers full movement control of other fingers. Additionally, we showed sensory deficits, and, specifically, finger position sensing versus tactile deficits, are correlated with hand function after a stroke, suggesting the importance of developing devices that can retrain, promote, and challenge finger position sensing.

In the last area -- mechanical design and control – we integrated the above findings as follows. First, we developed a novel, compact, soft actuator capable of providing the biologically-scaled force and impedance that the large fraction of stroke survivors we identified needed to assist their finger movement practice. Second, we integrated this actuator into a form-fitting, minimalistic exoskeleton -- the IGRIP exoskeleton – that facilitates active sensory-based control of pinch grip using the residual force control strategy. Third, we tested the IGRIP exoskeleton with ten unimpaired individuals by substituting it for their index finger in a prosthesis-like mode. We found that these individuals were able to learn to incorporate finger sensory input in order to take advantage of the residual force control strategy, thereby improving their performance at a manual lifting task beyond levels achievable without active, sensory-based control. These advances define a potential path forward toward user-accepted, worn, therapeutic, assistive robotics for the hand after stroke.