Liliana Barrios
ETH Zurich, Zürich, Switzerland
Fatigue is a common debilitating symptom of many autoimmune diseases, including multiple sclerosis. It negatively impacts patients' every-day life and productivity. Despite its prevalence, fatigue is still poorly understood. Its subjective nature makes quantification challenging and it is mainly assessed by questionnaires, which capture the magnitude of fatigue insufficiently. Motor fatigability, the objective decline of performance during a motor task, is an underrated aspect in this regard. Currently, motor fatigability is assessed using a handgrip dynamometer. This approach has been proven valid and accurate but requires special equipment and trained personnel. We propose a technique to objectively quantify motor fatigability using a commodity smartphone. The method comprises a simple exertion task requiring rapid alternating tapping. Our study with 20 multiple sclerosis patients and 35 healthy participants showed a correlation of rho = 0.8 with the baseline handgrip method. This smartphone-based approach is a first step towards ubiquitous, more frequent, and remote monitoring of fatigability and disease progression.
https://doi.org/10.1145/3313831.3376588
The ACM CHI Conference on Human Factors in Computing Systems (https://chi2020.acm.org/)