An adaptive, real-time cadence algorithm for unconstrained sensor placement. (February 2018)

• Record Type: Journal Article
• Authors: van Oeveren, B.T.; de Ruiter, C.J.; Beek, P.J.; Rispens, S.M.; van Dieën, J.H.
• Is Part Of: Medical engineering & physics. Volume 52(2018)
• Page Start: 49
• Page End: 58
• Publication Date: 2018-02
• Subjects: Running -- Gait cycle detection -- Stride frequency -- Accelerometer -- Cadence -- Sylvester's criterion -- Cross-correlation -- Autocorrelation; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2017.12.007
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A real-time cadence detection algorithm for unconstrained sensor positioning is described, taking into account the requirements for smartphone processing. The algorithm cross-correlates tri-axial accelerations of two sequential strides and verifies the completion of a cycle using the Sylvester's criterion. The Sylvester's criterion-based method was compared with common correlation methods. Only the described SC-based method consistently resulted in accurate feedback over all subjects, gaits and the ten sensor positions. Abstract: This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods. 22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s − 1 ), running (3.4 m s − 1 ), and sprinting (5.2 and 5.7 m s − 1 ), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm. The described algorithm resulted in consistent numbers of true positives (85.6–100.0%) and false positives (0.0–2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures. This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.