Unrestricted stride detection during stair climbing using IMUs. (June 2021)

• Record Type: Journal Article
• Authors: Siebers, Hannah Lena; Siroros, Nad; Alrawashdeh, Waleed; Migliorini, Filippo; Tingart, Markus; Eschweiler, Jörg; Betsch, Marcel
• Is Part Of: Medical engineering & physics. Volume 92(2021)
• Page Start: 10
• Page End: 17
• Publication Date: 2021-06
• Subjects: Step detection -- Gait analysis -- Kinematic -- IMU; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2021.04.004
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: A new algorithm for automatic stride detection during walking and stair climbing. A new algorithm based on combined data of foot and shank-mounted IMUs. Performance advance during ascending and descending stairs of the new algorithm. Abstract: Stride detection, or the identification of the initial (IC) and terminal contact (TC) of the feet while walking, is important for gait analysis. Automatic stride detection based only on kinematic data is challenging, even when using portable, low-cost, user-friendly Inertial Measurement Units (IMUs). Although there are algorithms for straight walking available, they are often not applicable to other movement patterns. Furthermore, these algorithms are based on the use of different IMUs placed on different locations of the body with different pre-processing filters and rely on analyzing different measurement signals. Therefore, it is difficult to apply existing algorithms for specific study settings. To achieve a new algorithm, thirty-five healthy participants were analyzed during walking and stair climbing while kinematic motion data was measured using the IMU system MyoMotion. Based on the analysis of different published methods for IC and TC detection, a new robust stride detection algorithm was developed and validated in comparison with two different algorithms. From this, it was determined that the newly developed algorithm was successful in automatic stride detection during walking and ascending/ descending stairs with 100% detected gait events, while the other algorithms failed during stair climbing with only 44% and 91% detected gait events.