| 000 | 02406 am a22002893u 4500 | ||
|---|---|---|---|
| 042 | _adc | ||
| 100 | 1 | 0 |
_aFortune, Emma _eauthor _9495 |
| 700 | 1 | 0 |
_aCloud-Biebl, Beth A. _eauthor _9496 |
| 700 | 1 | 0 |
_aMadansingh, Stefan I. _eauthor _9497 |
| 700 | 1 | 0 |
_aNgufor, Che G. _eauthor _9498 |
| 700 | 1 | 0 |
_aVan Straaten, Meegan G. _eauthor |
| 700 | 1 | 0 |
_aGoodwin, Brianna M. _eauthor _9500 |
| 700 | 1 | 0 |
_aMurphree, Dennis H. _eauthor _9501 |
| 700 | 1 | 0 |
_aZhao, Kristin D. _eauthor |
| 700 | 1 | 0 |
_aMorrow, Melissa M. _eauthor |
| 245 | 0 | 0 | _aEstimation of Manual Wheelchair-Based Activities in the Free-Living Environment using a Neural Network Model with Inertial Body-Worn Sensors |
| 260 | _c2022-02. | ||
| 500 | _a/pmc/articles/PMC6980511/ | ||
| 500 | _a/pubmed/31353200 | ||
| 520 | _aShoulder 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. | ||
| 540 | _a | ||
| 546 | _aen | ||
| 690 | _aArticle | ||
| 655 | 7 |
_aText _2local |
|
| 786 | 0 | _nJ Electromyogr Kinesiol | |
| 856 | 4 | 1 |
_uhttp://dx.doi.org/10.1016/j.jelekin.2019.07.007 _zConnect to this object online. |
| 999 |
_c1438 _d1438 |
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