TY - JOUR
T1 - Estimation of manual wheelchair-based activities in the free-living environment using a neural network model with inertial body-worn sensors
AU - Fortune, Emma
AU - Cloud-Biebl, Beth A.
AU - Madansingh, Stefan I.
AU - Ngufor, Che G.
AU - Van Straaten, Meegan G.
AU - Goodwin, Brianna M.
AU - Murphree, Dennis H.
AU - Zhao, Kristin D.
AU - Morrow, Melissa M.
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2022/2
Y1 - 2022/2
N2 - Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants’ free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants’ estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users’ field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.
AB - Shoulder pain is common in manual wheelchair (MWC) users. Overuse is thought to be a major cause, but little is known about exposure to activities of daily living (ADLs). The study goal was to develop a method to estimate three conditions in the field: (1) non-propulsion activity, (2) MWC propulsion, and (3) static time using an inertial measurement unit (IMU). Upper arm IMU data were collected as ten MWC users performed lab-based MWC-related ADLs. A neural network model was developed to classify data as non-propulsion activity, propulsion, or static, and validated for the lab-based data collection by video comparison. Six of the participants’ free-living IMU data were collected and the lab-based model was applied to estimate daily non-propulsion activity, propulsion, and static time. The neural network model yielded lab-based validity measures ≥0.87 for differentiating non-propulsion activity, propulsion, and static time. A quasi-validation of one participant's field-based data yielded validity measures ≥0.66 for identifying propulsion. Participants’ estimated mean daily non-propulsion activity, propulsion, and static time ranged from 158 to 409, 13 to 25, and 367 to 609 min, respectively. The preliminary results suggest the model may be able to accurately identify MWC users’ field-based activities. The inclusion of field-based IMU data in the model could further improve field-based classification.
KW - Activity classification
KW - Body-worn sensors
KW - Inertial measurement units
KW - Shoulder overuse
KW - Spinal cord injury
KW - Wheelchair propulsion
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U2 - 10.1016/j.jelekin.2019.07.007
DO - 10.1016/j.jelekin.2019.07.007
M3 - Article
C2 - 31353200
AN - SCOPUS:85071360736
SN - 1050-6411
VL - 62
JO - Journal of Electromyography and Kinesiology
JF - Journal of Electromyography and Kinesiology
M1 - 102337
ER -