Search

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters. [ABSTRACT FROM AUTHOR]

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.

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.