Douma, E.H. (2021) The extraction of gait features based on lower back and wrist IMU data during different walking conditions. thesis, Human Movement Sciences.
Full text available on request.Abstract
Introduction Gait analysis is traditionally performed in a supervised laboratory environment, however measurement of subjects’ gait in unsupervised environments reduces the effect of physiological and psychological processes on task performance and increases environmental validity. Unsupervised measurements might therefore be a more reliable tool to evaluate gait and daily life functioning. Wearable measurement systems like lower back accelerometers are often used for this purpose, although wrist-worn accelerometers like smartwatches might also be an interesting option. Aims 1) To examine whether there are differences in gait features during outdoor walking (free walking), walking during clinical tests (clinical walking) and walking during ADL tasks (ADL walking); 2) to examine whether there are differences in gait features when extracted from a wrist-worn accelerometer compared to a lower back-worn accelerometer. Methods Accelerometer data is gathered during three different walking conditions from healthy young and older subjects. Gait features (walking speed, stride length, stride time, stride frequency, root mean square (RMS) acceleration, index of harmonicity (IH) in mediolateral (ML), ventral (V) and anteroposterior (AP) directions, step regularity, stride regularity, step symmetry) are calculated from gait bouts and compared between walking types and sensor locations. Results Free walking differed significantly from both ADL and clinical walking for stride time, RMS acceleration, IH in ML direction, step regularity and stride regularity. Besides, free and ADL walking were different for stride length, stride frequency and IH in V direction. Clinical and ADL walking were only different for IH in V direction. A main effect of sensor location was found for RMS acceleration, IH in V direction, step regularity and step symmetry, and a sensor*walking type interaction effect was found for all qualitative features except IH in AP direction and step symmetry. In particular during free walking, the lower back and wrist sensor differed for all these gait features. Discussion Free, unsupervised outdoor walking results in significantly different gait compared to supervised walking. This underlines the importance of measuring walking in different walking environments for examining daily life functioning. The difference between gait features extracted from lower back versus wrist accelerometer data during free walking suggests that there is a need for gait detection algorithms specifically for wrist accelerometer data, especially since clinical and ADL walking do not cogently show differences between the three types of walking. Further research is needed to examine the possibilities for analysis based on unsupervised walking and wrist accelerometry.
| Item Type: | Thesis (UNSPECIFIED) |
|---|---|
| Supervisor name: | Lamoth, prof. dr. C.J.C. and Bernaldo de Quiros, drs. M. |
| Faculty: | Medical Sciences |
| Date Deposited: | 13 May 2022 09:41 |
| Last Modified: | 13 May 2022 09:41 |
| URI: | https://umcg.studenttheses.ub.rug.nl/id/eprint/3293 |
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