Validation Of A Nonintrusive Wearable Device For Distress Estimation During Robotic Roller Assisted Gait

Successful robot rollators work under the shared control paradigm as the best way to adjust dynamically to users' needs and preferences in rehabilitation and daily living activities. Deciding how much weight users have in emerging motion commands is necessary to assess their condition and needs, usually from on-board sensors. Unfortunately, some relevant parameters for safe and comfortable gait assistance (i.e. balance or stress) are extremely difficult to measure using only on-board sensors. Therefore, wearable devices that offer real-time physiological data acquisition are meant to be a valuable source of relevant information of users' psychological states such stress. However, detecting stress in real life with an unobtrusive wearable device is a challenging task. The objective of this study is to develop a method for real-time stress detection based in the wrist band Empatica E4 that can accurately, continuously and unobtrusively monitor psychological stress in real life to feed the system to provide smart gait-assistance. In this preliminary study we explore the feasibility, accuracy and reliability of the wrist-band with machine learning and signal processing techniques applied to electrodermal activity from 6 healthy participants in laboratory conditions. Specifically, the participants' electrodermal activity (EDA) gathered by the Empatica E4 under a standardized stress induction test (Affective Picture System) is analized to evaluate the sensitivity, validity and robustness of the measure. The present study will be followed by a pilot in the lab with 20 participants fulfilling trajectories of different level of difficulty with the roller, previously to the clinical trials with rehabilitation patients.