TY - BOOK 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. TI - Estimation of Manual Wheelchair-Based Activities in the Free-Living Environment using a Neural Network Model with Inertial Body-Worn Sensors PY - 2022///-02 KW - Text KW - local N1 - /pmc/articles/PMC6980511; /pubmed/31353200 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 158-409, 13-25, and 367-609 mins, 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 UR - http://dx.doi.org/10.1016/j.jelekin.2019.07.007 ER -