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.

It has been largely suggested in neuroscience literature that to generate a vast variety of movements, the Central Nervous System (CNS) recruits a reduced set of coordinated patterns of muscle activities, defined as muscle synergies. Recent neurophysiological studies have recommended the analysis of muscle synergies to finely assess the patient's impairment, to design personalized interventions based on the specific nature of the impairment, and to evaluate the treatment outcomes. In this scope, the aim of this study was to design a personalized multi-channel functional electrical stimulation (FES) controller for gait training, integrating three novel aspects: (1) the FES strategy was based on healthy muscle synergies in order to mimic the neural solutions adopted by the CNS to generate locomotion; (2) the FES strategy was personalized according to an initial locomotion assessment of the patient and was designed to specifically activate the impaired biomechanical functions; (3) the FES strategy was mapped accurately on the altered gait kinematics providing a maximal synchronization between patient's volitional gait and stimulation patterns. The novel intervention was tested on two chronic stroke patients. They underwent a 4-week intervention consisting of 30-min sessions of FES-supported treadmill walking three times per week. The two patients were characterized by a mild gait disability (walking speed > 0.8 m/s) at baseline. However, before treatment both patients presented only three independent muscle synergies during locomotion, resembling two different gait abnormalities. After treatment, the number of extracted synergies became four and they increased their resemblance with the physiological muscle synergies, which indicated a general improvement in muscle coordination. The originally merged synergies seemed to regain their distinct role in locomotion control. The treatment benefits were more evident for one patient, who achieved a clinically important change in dynamic balance (Mini-Best Test increased from 17 to 22) coupled with a very positive perceived treatment effect (GRC = 4). The treatment had started the neuro-motor relearning process also on the second subject, but twelve sessions were not enough to achieve clinically relevant improvements. This attempt to apply the novel theories of neuroscience research in stroke rehabilitation has provided promising results, and deserves to be further investigated in a larger clinical study.

The aim of this study was to develop a methodology based on muscle synergies to investigate whether rectilinear and curvilinear walking shared the same neuro-motor organization, and how this organization was fine-tuned by the walking condition. Thirteen healthy subjects walked on rectilinear and curvilinear paths. Electromyographic data from thirteen back and lower-limb muscles were acquired, together with kinematic data using inertial sensors. Four macroscopically invariant muscle synergies, extracted through non-negative matrix factorization, proved a shared modular organization across conditions. The fine-tuning of muscle synergies was studied through non-negative matrix reconstruction, applied by fixing muscle weights or activation profiles to those of the rectilinear condition. The activation profiles tended to be recruited for a longer period and with a larger amplitude during curvilinear walking. The muscles of the posterior side of the lower limb were those mainly influenced by the fine-tuning, with the muscles inside the rotation path being more active than the outer muscles. This study shows that rectilinear and curvilinear walking share a unique motor command. However, a fine-tuning in muscle synergies is introduced during curvilinear conditions, adapting the kinematic strategy to the new biomechanical needs.

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Abstract-This study aimed at developing an adaptive algorithm to detect in real time temporal gait events, based on data acquired from inertial and magnetic measurement units.Trials on 9 healthy subjects were performed to select the best body locations for the sensors out of 8 different possibilities, trying to optimize system portability, data intervariability and real-time algorithm simplicity. Subjects walked over the GaitRite mat at different self-selected speeds: normal, fast, and slow. Results showed a significantly low variability (p<0.05) of the shank angular velocity in the sagittal plane, reducing the number of sensors required for the real-time algorithm to two (the ones placed on the shanks).The detection of the Initial Contact (IC) and the End Contact (EC) was based on the shank angular velocity and flexion/extension angle. The gait events were identified as local minima on the sagittal-plane angular velocity. Features extracted from the signals of the previous steps were used to improve the events localization. These features were selfcalibrated at the beginning of the trial and updated every step.The algorithm was validated against the GaitRite system and was compared to two other real-time algorithms available in the literature to assess its reliability and performance. F1-scores of 0.9987 for IC and 0.9996 for EC were obtained. Our algorithm detected the gait events with a mean (SD) delay of 68.6 (15.1) ms for IC and 7.8 (23.6) ms for EC, with respect to the GaitRite, for the self-selected normal speed. These values were significantly lower than those obtained by other published algorithms.Results indicated that the system is suitable for real-time gait monitoring, assessment and ambulatory rehabilitation, based on biofeedback or neuroprostheses.