Development and Evaluation of a Multimodal Sensor Motor Learning Assessment

Zhengxiong Li1, Michael Brown, Junqi Wu, Chen Song2, Feng Lin3, Jeanne Langan2, Wenyao Xu2

  • 1University at Buffalo
  • 2State University of New York, Buffalo
  • 3University of Colorado Denver

Details

15:00 - 15:15 | Wed 7 Mar | Antilles CD | WeCT1.5

Session: BSN Session # 7 – Innovations in Sensing

Abstract

Motor learning is the ability to acquire a new motor skill, which plays an important role in rehabilitation as patients learn exercise programs or modify movements to regain pain free function. In this paper, we design an easy-to-use multimodal sensor system to assess motor learning. We developed a motor learning assessment device with a touch screen and Leap Motion to record the subject hand movement during a Serial Reaction Time Task(SRTT). The SRTT consists of upper limb reaching to targets in multi-dimensions. The device records metrics of time and movement efficiency and examines motor learning based on data analysis. This device can provide clinicians with data that can inform their approach to training. We recruited a total of 11 participants, with and without chronic pain to evaluate the device using a classifier model to assess participants' performance. The model shows our system works well to identify motor learning differences in individuals with and without chronic pain.