Novel approach to human walking speed enhancement based on drift estimation. (April 2018)

• Record Type: Journal Article
• Authors: Brzostowski, Krzysztof
• Is Part Of: Biomedical signal processing and control. Volume 42(2018)
• Page Start: 18
• Page End: 29
• Publication Date: 2018-04
• Subjects: Sensor fusion -- Non-linear signal processing -- Filtering -- Segmentation -- Strap-down integration -- Inertial sensing; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.01.002
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: We propose the approach to enhancement human walking speed estimation. Our approach based on the idea of drift cancellation from estimated human walking speed. The proposed method to drift cancellation based on piecewise polynomial modeling of the drift. We compare our method with the ZUPT-based approach and with ground truth. The proposed approach can mitigate the drift from the estimated human walking speed. Abstract: The speed of human walking is a valuable indicator of individuals' health status, for example in sports or medicine. Classic methods such as the camera-based approach have serious limitations: (1) they can be used only in the laboratory setting, and (2) the computational complexity of the data processing is remarkably high. The development of small wearable inertial sensing systems and suitable methods of data processing allow the analysis of human motion to be performed outside the laboratory. Moreover, such solutions can work in the real-time regime. Unfortunately, the inertia-based human walking speed estimation systems applying the strap-down integration approach have an unavoidable drawback. The disadvantage is the estimation inaccuracies caused by the accumulation of errors. This paper presents an approach for attenuating these errors by applying a method for estimating drift.