Automated gait event detection for a variety of locomotion tasks using a novel gyroscope-based algorithm

作     者：Fadillioglu, Cagla Stetter, Bernd J. Ringhof, Steffen Krafft, Frieder C. Sell, Stefan Stein, Thorsten 

作者机构：Karlsruhe Inst Technol Inst Sports & Sports Sci Engler Bunte Ring 15 D-76131 Karlsruhe Germany Univ Freiburg Dept Sport & Sport Sci Schwarzwaldstr 175 D-79117 Freiburg Germany Hosp Neuenbuerg Joint Ctr Black Forest D-75305 Neuenbuerg Germany 

出 版 物：《GAIT & POSTURE》 (步法与姿势)

年 卷 期：2020年第81卷

页      面：102-108页

核心收录：

学科分类：0403[教育学-体育学] 1002[医学-临床医学] 1001[医学-基础医学(可授医学、理学学位)] 10[医学] 1009[医学-特种医学] 

基　　金：German Federal Ministry for Economic Affairs and Energy [4136601TS5] 

主　　题：Wearable sensors Rule-based algorithm Initial contact Toe-off Linear movements Turning conditions 

摘      要：Background: The robust identification of initial contact (IC) and toe-off (TO) events is a vital task in mobile sensor-based gait analysis. Shank attached gyroscopes in combination with suitable algorithms for data processing can robustly and accurately complete this task for gait event detection. However, little research has considered gait detection algorithms that are applicable to different locomotion tasks. Research question: Does a gait event detection algorithm for various locomotion tasks provide comparable estimation accuracies as existing task-specific algorithms? Methods: Thirteen males, equipped with a gyroscope attached to the right shank, volunteered to perform nine different locomotion tasks consisting of linear movements and movements with a change of direction. A rule based algorithm for IC and TO events was developed based on the shank sagittal plane angular velocity. The algorithm was evaluated against events determined by vertical ground reaction force. Absolute mean error (AME), relative absolute mean error (RAME) and Bland-Altman analysis was used to assess its accuracy. Results: The average AME and RAME were 11 +/- 3 ms and 3.07 +/- 1.33 %, respectively, for IC and 29 +/- 11 ms and 7.27 +/- 2.92 %, respectively, for TO. Alterations of the walking movement, such as turns and types of running, slightly reduced the accuracy of IC and TO detection. In comparison to previous methods, increased or comparable accuracies for both IC and TO detection are shown. Significance: The study shows that the proposed algorithm is capable of detecting gait events for a variety of locomotion tasks by means of a single gyroscope located on the shank. In consequence, the algorithm can be applied to activities, which consist of various movements (e.g., soccer). Ultimately, this extends the use of mobile sensor-based gait analysis.