Comparing loose clothing-mounted sensors with body-mounted sensors in the analysis of walking

A person’s walking pattern can reveal important information about their health. Mounting multiple sensors onto loose clothing potentially offers a comfortable way of collecting data about walking and other human movement. This research investigates how well the data from three sensors mounted on the lateral side of clothing (on a pair of trousers near the waist, upper thigh and lower shank) correlate with the data from sensors mounted on the frontal side of the body. Data collected from three participants (two male, one female) for two days were analysed. Gait cycles were extracted based on features in the lower-shank accelerometry and analysed in terms of sensor-to-vertical angles (SVA). The correlations in SVA between the clothing- and body-mounted sensor pairs were analysed. Correlation coefficients above 0.76 were found for the waist sensor pairs, while the thigh and lower-shank sensor pairs had correlations above 0.90. The cyclical nature of gait cycles was evident in the clothing data, and it was possible to distinguish the stance and swing phases of walking based on features in the clothing data. Furthermore, simultaneously recording data from the waist, thigh, and shank was helpful in capturing the movement of the whole leg

Using a foot mounted accelerometer to detect changes in gait patterns

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3 - 7 July 2013The purpose of this study is to investigate how datafrom a foot mounted accelerometer can be used to detect motorpattern healthy subjects performed walking trails under twodifferent conditions; normal and stiff ankle walking. Lowerbody kinematic data were collected as well as accelerometerdata from both feet. An algorithm is presented which quantifiesrelevant swing phase characteristics from the footaccelerometer. Peak total acceleration during initial swing wassignificantly higher in the stiff ankle condition (M = 33.10, SD =5.12) than in the normal walking condition (M = 29.47, SD =5.75; t(7) = 4.32, p = .003, two-tailed). There was a large effectsize (eta squared = 0.853). Time between peak accelerationduring initial swing to foot strike was significantly shorter inthe stiff ankle condition (M = 0.42, SD = 0.02) than in thenormal condition (M = 0.44, SD = 0.03; t(7) = -2.54, p = .039,two- tailed). There was a large effect size (eta squared = 0.693).Simple to process metrics from tri-axial accelerometer data onthe foot show potential to detect changes in ankle kinematicpatterns.Science Foundation Irelan