Validation of an IMU wearable for treadmill walking

Ligeti, A.G. and Forsyth, L. and Blyth, M. and Riches, P.E. (2023) Validation of an IMU wearable for treadmill walking. In: BioMedEng23, 2023-09-14 - 2023-09-15.

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Abstract

Introduction: With 100,000 total knee arthroplasty (TKA) procedures taking place in the United Kingdom annually [1], and 94% of these procedures occurring in individuals 50 years and older [2], the demand for home-based rehabilitation is high, however, compliance is poor [2]. Wearable technologies, such as MotionSenseTM (Stryker, US), can remotely support post-operative TKA rehabilitation by providing personalised rehabilitation and tracking of home exercises, enabling healthcare professionals to continuously monitor rehabilitation progress remotely. Validation of such devices across a range of potential ability levels against a known kinematic model in activities of  daily living is important for confident interpretation of resulting clinical data. The aim of this study therefore was to validate the accuracy of MotionSenseTM against a clinical motion capture standard. Methods: Upon receiving NHS ethics approval, twenty able-bodied young individuals (age 24 ± 4 years, mean ± SD) and 14 older participants (71 ± 5 years) volunteered and consented to the study. Retroreflective markers and MotionSenseTM sensors were attached to the lower limb (Figure 1). Volunteers walked for 5 minutes at a self-selected comfortable speed on a treadmill. Vicon PlugInGaitTM determined knee flexion (100 Hz) and the MotionSenseTM sensors exported data in real-time (~50Hz) to a mobile device on which a proprietary algorithm determined knee flexion. Following up-sampling to 1000Hz, cross-correlation was used to time synchronise the measurements in gait cycle windows identified from peak flexion to peak flexion. As the zero point for knee flexion depends on marker placement, the mean knee flexion was subtracted from each data set before calculating a root mean square error (RMSE) between the technologies, determined in each gait cycle window. T-tests compared the older and the younger populations and significance was taken at the 5% level.  Results and Discussion: Fewer gait cycles were collected and analysed on older compared to younger volunteers (93 ± 45 vs 170 ± 107, mean ± SD, p < 0.001). The older volunteers walked slower than the younger group (0.94 ± 0.12 ms-1 vs 1.17 ± 0.07 ms-1, p < 0.001). RMSE values are similar to previous studies [3, 4]. RMSE data demonstrated an excellent agreement between the devices with a pooled RMSE < 3.5° (Table 1). Walking speed may affect accuracy, as the MotionSenseTM was more accurate in the older group albeit non-significantly (p = 0.21). Conclusion: MotionSenseTM performed accurately during treadmill walking in both older and young populations. The difference between the technologies may be considered clinically negligible given the inherent variation in such analyses. Further research should be conducted on TKA patients validating the technology for this population.

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