SUBJECT: M.S. Thesis Presentation
BY: Daniel Hochman
TIME: Thursday, April 15, 2021, 2:30 p.m.
PLACE:, n/a
TITLE: Measuring and Analyzing Joint Acoustic Emissions from the Wrist
COMMITTEE: Dr. Omer Inan, Co-Chair (ECE)
Dr. Aaron Young, Co-Chair (ME)
Dr. Woonhong Yeo (ME)


Joint acoustic emissions (JAEs) measured from the knee present promise as a method of noninvasive knee health quantification. This work adapts the methods developed for knee JAE measurements to the wrist - another joint commonly afflicted with injuries and degenerative disease. First, JAEs are measured using contact microphones at eight locations around the wrist during prescribed exercises (wrist flexion-extension and rotation) to find reliable and consistent wrist JAE measurement methods. The benchtop measurement setup established for knee JAE measurements is directly incorporated in this study. JAE signal strength is assessed using the signal-to-noise ratio (SNR). Then, nine features that have shown importance in JAE analysis are extracted, and the intraclass correlation coefficient (ICC) (model 3,k), coefficients of variability (CVs), and Jensen-Shannon (JS) divergence are used to assess the interrater repeatability, revealing both exercises produce JAEs and three locations demonstrated high JAE signal strength and repeatability. Second, a wrist wearable system is developed for high quality JAE sensing. Low-profile wide-band analog accelerometers to measure and analyze JAEs from two reliable locations on the wrist are sampled and saved on an SD card using a custom-designed printed circuit board (PCB). Using custom-developed casings, one accelerometer is clipped to the loop of a watchband for placement on the radius and the other is contained in a custom-developed grip for placement against the palm. Proper grip strength is reinforced real-time using LEDs. A flex sensor is secured along the volar side of the wrist for synchronously tracking wrist motion for improved cycle-by-cycle JAE analysis. This wrist wearable JAE sensing system is validated using SNR, ICC, CV, and JS divergence and compared to the benchtop setup. The developed wearable system is critical in moving toward monitoring wrist JAEs for at-home wrist joint health assessment.