TY - JOUR PY - 2014// TI - A novel adaptive, real-time algorithm to detect gait events from wearable sensors JO - IEEE transactions on neural systems and rehabilitation engineering A1 - Chia Bejarano, Noelia A1 - Ambrosini, Emilia A1 - Pedrocchi, Alessandra A1 - Ferrigno, Giancarlo A1 - Monticone, Marco A1 - Ferrante, Simona SP - 413 EP - 422 VL - 23 IS - 3 N2 - 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 10 healthy subjects performing sudden speed changes and on 10 stroke subjects (43 to 89 years). For healthy subjects, F1-scores of 1 and mean detection delays lower than 14ms 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 31ms. 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.

Language: en

LA - en SN - 1534-4320 UR - http://dx.doi.org/10.1109/TNSRE.2014.2337914 ID - ref1 ER -