TY - GEN
T1 - Development and evaluation of a multimodal sensor motor learning assessment
AU - Li, Zhengxiong
AU - Brown, Michael
AU - Wu, Junqi
AU - Song, Chen
AU - Lin, Feng
AU - Langan, Jeanne
AU - Xu, Wenyao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/4/2
Y1 - 2018/4/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85049674631
U2 - 10.1109/BSN.2018.8329689
DO - 10.1109/BSN.2018.8329689
M3 - Conference contribution
AN - SCOPUS:85049674631
T3 - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
SP - 185
EP - 188
BT - 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2018
Y2 - 4 March 2018 through 7 March 2018
ER -