Assessment of a Smart Sensing Shoe for Gait Phase Detection in Level Walking

Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques

The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot-ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments.

Validation of a newly developed low-cost, high-accuracy, camera-based gait analysis system

BACKGROUND

Gait analysis is essential for evaluating locomotor function and fall risk, particularly in the elderly and in various musculoskeletal disorders. Traditional gait analysis systems face challenges such as technical difficulties, high cost, and complexity of use. Therefore, there is a need for a more accessible and cost-effective system with a wider clinical applicability.

RESEARCH QUESTION

This study aimed to validate the newly developed IB-gait® system (InBody, Republic of Korea), a camera-based gait analysis tool, by comparing it against the VICON system.

METHODS

A total of 28 community-dwelling adults without gait abnormalities (mean age 24.9 years) were enrolled in this study. The participants underwent gait analysis at their self-selected speed using VICON and IB-gait® simultaneously. Nine spatiotemporal gait parameters, including stride length (m), step length (m), stride duration (s), double-limb duration (s), stance phase (s), swing phase (s), cadence (velocity × 120/stride length), and gait velocity (m/s) were measured. The agreement between the two systems was tested using Bland-Altman plots and intraclass correlation coefficients (ICC).

RESULTS

The IB-gait® showed a high degree of agreement with the VICON system in most gait parameters. The ICC showed excellent reliability for stride length (0.97), step length (0.92), gait velocity (0.97), cadence (0.97), and stride duration (0.79). However, it showed lower reliability in time-based parameters, including double-limb duration (0.12), stance phase (0.54), swing phase (0.241), and stance/swing phase ratio (0.11).

SIGNIFICANCE

The IB-gait® system appears to be a feasible and cost-effective alternative to VICON system for gait analysis, particularly showing a high level of agreement in the distance-based parameters. Its practicality in clinical settings makes it a valuable tool for widespread use in gait analysis. However, further refinement of time-based parameter measurements and validation in diverse patient populations are needed to enhance its applicability.

Combining Different Wearable Devices to Assess Gait Speed in Real-World Settings

Assessing mobility in daily life can provide significant insights into several clinical conditions, such as Chronic Obstructive Pulmonary Disease (COPD). In this paper, we present a comprehensive analysis of wearable devices' performance in gait speed estimation and explore optimal device combinations for everyday use. Using data collected from smartphones, smartwatches, and smart shoes, we evaluated the individual capabilities of each device and explored their synergistic effects when combined, thereby accommodating the preferences and possibilities of individuals for wearing different types of devices. Our study involved 20 healthy subjects performing a modified Six-Minute Walking Test (6MWT) under various conditions. The results revealed only little performance differences among devices, with the combination of smartwatches and smart shoes exhibiting superior estimation accuracy. Particularly, smartwatches captured additional health-related information and demonstrated enhanced accuracy when paired with other devices. Surprisingly, wearing all devices concurrently did not yield optimal results, suggesting a potential redundancy in feature extraction. Feature importance analysis highlighted key variables contributing to gait speed estimation, providing valuable insights for model refinement.

A method for gait events detection based on low spatial resolution pressure insoles data

The accurate identification of initial and final foot contacts is a crucial prerequisite for obtaining a reliable estimation of spatio-temporal parameters of gait. Well-accepted gold standard techniques in this field are force platforms and instrumented walkways, which provide a direct measure of the foot-ground reaction forces. Nonetheless, these tools are expensive, non-portable and restrict the analysis to laboratory settings. Instrumented insoles with a reduced number of pressure sensing elements might overcome these limitations, but a suitable method for gait events identification has not been adopted yet. The aim of this paper was to present and validate a method aiming at filling such void, as applied to a system including two insoles with 16 pressure sensing elements (element area = 310 mm²), sampling at 100 Hz. Gait events were identified exploiting the sensor redundancy and a cluster-based strategy. The method was tested in the laboratory against force platforms on nine healthy subjects for a total of 801 initial and final contacts. Initial and final contacts were detected with low average errors of (about 20 ms and 10 ms, respectively). Similarly, the errors in estimating stance duration and step duration averaged 20 ms and <10 ms, respectively. By selecting appropriate thresholds, the method may be easily applied to other pressure insoles featuring similar requirements.

An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases

Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms.

Self-Compensated Driving Circuit for Reducing Drift and Hysteresis in Force Sensing Resistors

Force Sensing Resistors (FSRs) are manufactured from a blend of conductive nanoparticles dispersed in an insulating polymer matrix. FSRs exhibit large amounts of hysteresis and drift error, but currently, a great effort is placed on improving their performance through different techniques applied during sensor manufacturing. In this article, a novel technique for improving the performance of FSRs is presented; the method can be applied to already-manufactured sensors, which is a clear benefit of the proposed procedure. The method is based on driving the sensors with a modified-astable 555 oscillator, in which the oscillation frequency is set from the sensor’s capacitance and resistance. Considering that the sensor’s capacitance and resistance have opposite signs in the drift characteristic, the driving circuit provides self-compensated force measurements over extended periods of time. The feasibility of the driving circuit to reduce hysteresis and to avoid sensitivity degradation is also tested. In order to obtain representative results, the experimental measurements from this study were performed over eight FlexiForce A201-25 sensors.