RESUMEN
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.
Asunto(s)
Biorretroalimentación Psicológica , Dispositivos Electrónicos Vestibles , Humanos , Estimulación Acústica , Señales (Psicología) , MarchaRESUMEN
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.
Asunto(s)
Marcha , Redes Neurales de la Computación , Fenómenos Biomecánicos , Movimiento (Física) , Extremidad InferiorRESUMEN
Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains.
Asunto(s)
Miembros Artificiales , Humanos , Marcha , Caminata , Algoritmos , AceleraciónRESUMEN
Wearable sensors have been investigated for the purpose of gait analysis, namely gait event detection. Many types of algorithms have been developed specifically using inertial sensor data for detecting gait events. Though much attention has turned toward machine learning algorithms, most of these approaches suffer from large computational requirements and are not yet suitable for real-time applications such as in prostheses or for feedback control. Current rules-based algorithms for real-time use often require fusion of multiple sensor signals to achieve high accuracy, thus increasing complexity and decreasing usability of the instrument. We present our results of a novel, rules-based algorithm using a single accelerometer signal from the foot to reliably detect heel-strike and toe-off events. Using the derivative of the raw accelerometer signal and applying an optimizer and windowing approach, high performance was achieved with a sensitivity and specificity of 94.32% and 94.70% respectively, and a timing error of 6.52 ± 22.37 ms, including trials involving multiple speed transitions. This would enable development of a compact wearable system for robust gait analysis in real-world settings, providing key insights into gait quality with the capability for real-time system control.