Rules-Based Real-Time Gait Event Detection Algorithm for Lower-Limb Prosthesis Users during Level-Ground and Ramp Walking

The Development of a Wearable Biofeedback System to Elicit Temporal Gait Asymmetry using Rhythmic Auditory Stimulation and an Assessment of Immediate Effects

Temporal gait asymmetry (TGA) is commonly observed in individuals facing mobility challenges. Rhythmic auditory stimulation (RAS) can improve temporal gait parameters by promoting synchronization with external cues. While biofeedback for gait training, providing real-time feedback based on specific gait parameters measured, has been proven to successfully elicit changes in gait patterns, RAS-based biofeedback as a treatment for TGA has not been explored. In this study, a wearable RAS-based biofeedback gait training system was developed to measure temporal gait symmetry in real time and deliver RAS accordingly. Three different RAS-based biofeedback strategies were compared: open- and closed-loop RAS at constant and variable target levels. The main objective was to assess the ability of the system to induce TGA with able-bodied (AB) participants and evaluate and compare each strategy. With all three strategies, temporal symmetry was significantly altered compared to the baseline, with the closed-loop strategy yielding the most significant changes when comparing at different target levels. Speed and cadence remained largely unchanged during RAS-based biofeedback gait training. Setting the metronome to a target beyond the intended target may potentially bring the individual closer to their symmetry target. These findings hold promise for developing personalized and effective gait training interventions to address TGA in patient populations with mobility limitations using RAS.

Convolutional Neural Network for Estimating Spatiotemporal and Kinematic Gait Parameters using a Single Inertial Sensor. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083203 DOI: 10.1109/embc40787.2023.10340904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Lower limb disability severely impacts gait, thus requiring clinical interventions. Inertial sensor systems offer the potential for objective monitoring and assessment of gait in and out of the clinic. However, it is imperative such systems are capable of measuring important gait parameters while being minimally obtrusive (requiring few sensors). This work used convolutional neural networks to estimate a set of six spatiotemporal and kinematic gait parameters based on raw inertial sensor data. This differs from previous work which either was limited to spatiotemporal parameters or required conventional strap-down integration techniques to estimate kinematic parameters. Additionally, we investigated a data segmentation method which does not rely on gait event detection, further supporting its applicability in real-world settings.Preliminary results demonstrate our model achieved high accuracy on a mix of spatiotemporal and kinematic gait parameters, either meeting or exceeding benchmarks based on literature. We achieved 0.04 ± 0.03 mean absolute error for stance-time symmetry ratio and an absolute error of 4.78 ± 4.78, 4.50 ± 4.33, and 6.47 ± 7.37cm for right and left step length and stride length, respectively. Lastly, errors for knee and hip ranges of motion were 2.31 ± 4.20 and 1.73 ± 1.93°, respectively. The results suggest that machine learning can be a useful tool for long-term monitoring of gait using a single inertial sensor to estimate measures of gait quality.

Overground walking with a constraint force on the nonparetic leg during swing improves weight shift toward the paretic side in people after stroke

Targeting enhancing the use of the paretic leg during locomotor practice might improve motor function of the paretic leg. The purpose of this study was to determine whether application of constraint force to the nonparetic leg in the posterior direction during overground walking would enhance the use of the paretic leg in people with chronic stroke. Fifteen individuals after stroke participated in two experimental conditions, i.e., overground walking with a constraint force applied to the nonparetic leg and overground walking only. Each participant was tested in the following procedures that consisted of overground walking with either constraint force or no constraint force, instrumented split-belt treadmill walking, and pressure-sensitive gait mat walking before and after the overground walking. Overground walking practice with constraint force resulted in greater enhancement in lateral weight shift toward the paretic side (P < 0.01), muscle activity of the paretic hip abductors (P = 0.04), and propulsion force of the paretic leg (P = 0.05) compared with the results of the no-constraint condition. Overground walking practice with constraint force tended to induce greater increase in self-selected overground walking speed (P = 0.06) compared with the effect of the no-constraint condition. The increase in propulsion force from the paretic leg was positively correlated with the increase in self-selected walking speed (r = 0.6, P = 0.03). Overground walking with constraint force applied to the nonparetic leg during swing phase of gait may enhance use of the paretic leg, improve weight shifting toward the paretic side and propulsion of the paretic leg, and consequently increase walking speed.NEW & NOTEWORTHY Application of constraint force to the nonparetic leg during overground walking induced improved lateral weight shifts toward the paretic leg and enhanced muscle activity of the paretic leg during walking. In addition, one session of overground walking with constraint force might induce an increase in propulsive force of the paretic leg and an increase in self-selected overground walking speed, which might be partially due to the improvement in motor control of the paretic leg.