On-body inertial sensing and signal processing for clinical assessment of tremor

Tremor, the most common form of movement disorder, is an often debilitating condition that adversely affects an individual's ability to maintain functional independence. Efforts to study, diagnose, and treat such movement disorders are complicated by a dearth of quantitative, precise, or accurate methods for motion data collection and assessment. To address this deficiency, this paper provides two contributions: 1) the design of a body-area inertial sensing system and 2) the evaluation of postcapture, on-body signal-processing algorithms that transform sensed inertial data into clinically significant information pertaining to tremor symmetry. For the former, we present our technology that meets requirements for wearability, fidelity, battery life, and interoperability. For the latter, we demonstrate the efficacy of using filter-bank analysis and cross correlation to interpret tremor frequency and energy. We extend the previous work by presenting a wireless body-area inertial sensing technology and a method to reduce, by up to 30 times, the computational demands of cross correlation on such a resource-constrained technology. These efforts lay the foundation for real-time, on-body assessment of tremor as well as more intelligent and energy-efficient data transmission and storage decisions.