UC Santa Barbara

The sense of touch arises from a complex interplay between biomechanical and neural processes that span large areas of skin. Little is understood about these processes and their interactions beyond the immediate area of touch contact due to experimental constraints on biomechanical and neural measurements. This Ph.D. dissertation addresses these challenges by developing data-driven computational methods to predict and analyze the widespread neuromechanical processes underlying manual touch. The research presented here seeks to answer the following questions: How does biomechanical transmission influence neural signals in the human tactile system, and what implications does that have for human tactile sensing in general? And how can we exploit biomechanical transmission for technology that interfaces with or takes inspiration from the human sense of touch?

This dissertation builds upon findings that manual touch interactions biomechanically transmit skin oscillations across the hand and arm (biomechanical transmission), exciting widespread mechanoreceptive sensory neurons (mechanoreceptors). Chapter 3 uses high-resolution optical vibrometry measurements of whole-hand skin oscillations to drive neural simulations of mechanoreceptor populations. The results demonstrate that the hand's biomechanics modifies skin oscillations in a frequency- and location-dependent manner that diversifies mechanoreceptor responses, enabling them to efficiently capture touch information. This research challenges existing characterizations of peripheral tactile sensing and has implications for how tactile information is processed by the brain. Critically, this chapter emphasizes the importance of considering the influence of biomechanics on neural signals both at and beyond the location of touch contact.

Motivated by research conveying the significance of studying neural circuitry in natural settings, Chapter 4 extends the data-driven methodology presented in Chapter 3 to investigate whole-hand tactile encoding of active, unconstrained touch interactions. The results indicate that information about these interactions is organized within the spatial structure of the population responses at the level of individual digits. Additionally, this work demonstrates that biomechanical transmission enables mechanoreceptors in areas far from locations of touch contact to capture significant tactile information.

This concept of remote tactile sensing underpins the wrist-worn device developed in Chapter 5, which utilizes accelerometers to measure skin oscillations elicited by tactile sign language (TSL) letters performed on the hand. By extracting various temporal, spectral, and spectrotemporal features from these measurements and passing them into simple classifiers, the device achieves a translation accuracy of 94%. This chapter presents the first digital input device for TSL users, enabling digital TSL transcription and communication by leveraging biomechanical transmission.

High-resolution measurements of skin oscillations, such as those employed in this dissertation, are often time- and resource-intensive. This presents an obstacle for touch research, given the demonstrated impact of biomechanical transmission on tactile sensing. To overcome these barriers, Chapter 6 introduces a free-to-use toolbox for predicting skin oscillations across the upper limb elicited by tactile stimuli applied at one or more locations on the hand. This toolbox enables the computational analysis of biomechanical transmission in the skin, reducing the need for physical measurements and supporting applications in neuroscience and haptics.