A Novel Adaptive, Real-Time Algorithm to Detect Gait Events From Wearable Sensors

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 23; no. 3; pp. 413 - 422
• Main Authors: Chia Bejarano, Noelia, Ambrosini, Emilia, Pedrocchi, Alessandra, Ferrigno, Giancarlo, Monticone, Marco, Ferrante, Simona
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptive algorithms; Adult; Aged; Aged, 80 and over; Algorithm design and analysis; Algorithms; Ambulatory gait system; Angular velocity; Biomechanical Phenomena; Calibration; Computer Systems; Contact; Contact angle; Delay; Delays; Female; Gait; Gait - physiology; Gait Disorders, Neurologic - physiopathology; hemiparetic gait; Humans; inertial and magnetic sensors; Integrated circuits; Leg - physiology; Legged locomotion; Male; Middle Aged; Monitoring, Ambulatory; Paresis - rehabilitation; Prosthesis Design; Real time; real-time signal processing; Real-time systems; Reproducibility of Results; Sensors; Stroke Rehabilitation; temporal gait parameters; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2014.2337914

A real-time, adaptive algorithm based on two inertial and magnetic sensors placed on the shanks was developed for gait-event detection. For each leg, the algorithm detected the Initial Contact (IC), as the minimum of the flexion/extension angle, and the End Contact (EC) and the Mid-Swing (MS), as minimum and maximum of the angular velocity, respectively. The algorithm consisted of calibration, real-time detection, and step-by-step update. Data collected from 22 healthy subjects (21 to 85 years) walking at three self-selected speeds were used to validate the algorithm against the GaitRite system. Comparable levels of accuracy and significantly lower detection delays were achieved with respect to other published methods. The algorithm robustness was tested on ten healthy subjects performing sudden speed changes and on ten stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14 ms were obtained. For stroke subjects, F1-scores of 0.998 and 0.944 were obtained for IC and EC, respectively, with mean detection delays always below 31 ms. The algorithm accurately detected gait events in real time from a heterogeneous dataset of gait patterns and paves the way for the design of closed-loop controllers for customized gait trainings and/or assistive devices.