Comprehensive validation of a wearable foot sensor system for estimating spatiotemporal gait parameters by simultaneous three-dimensional optical motion analysis

Artificial intelligence-enhanced 3D gait analysis with a single consumer-grade camera

Gait analysis is crucial for diagnosing and monitoring various healthcare conditions, but traditional marker-based motion capture (MoCap) systems require expensive equipment, extensive setup, and trained personnel, limiting their accessibility in clinical and home settings. Markerless systems reduce setup complexity but often require multiple cameras, fixed calibration, and are not designed for widespread clinical adoption. This study introduces 3DGait, an artificial intelligence-enhanced markerless 3-Dimensional gait analysis system that operates with a single consumer-grade depth camera, providing a streamlined, accessible alternative. The system integrates advanced machine learning algorithms to produce 49 angular, spatial, and temporal gait biomarkers commonly used in mobility analysis. We validated 3DGait against a marker-based MoCap (OptiTrack) using 16 trials from 8 healthy adults performing the Timed Up and Go (TUG) test. The system achieved an overall average mean absolute error (MAE) of 2.3°, with all MAE under 5.2°, and a Pearson's correlation coefficient (PCC) of 0.75 for angular biomarkers. All spatiotemporal biomarkers had errors no greater than 15 %. Temporal biomarkers (excluding TUG time) had errors under 0.03 s, corresponding to one video frame at 30 frames per second. These results demonstrate that 3DGait provides clinically acceptable gait metrics relative to marker-based MoCap, while eliminating the need for markers, calibration, or fixed camera placement. 3DGait's accessible, non-invasive and single camera design makes it practical for use in non-specialist clinics and home settings, supporting patient monitoring and chronic disease management. Future research will focus on validating 3DGait with diverse populations, including individuals with gait abnormalities, to broaden its clinical applications.

Comparison of feetme insoles with a motion capture system coupled to force plates for assessing gait and posture

Traditional gait measurement systems are often limited by factors such as cost, complexity, prolonged setup times, and requirements for specialized training and expertise. Wearable pressure- and motion-sensing insoles have opened new possibilities for accessible gait analysis in real-life conditions. This study evaluated the equivalence of FeetMe insole measurements of gait parameters to those of a laboratory gold standard, optoelectronic motion capture system coupled to force platforms (MoCap/FP). Gait and posture parameters were assessed in 37 healthy adults by FeetMe insoles and by MoCap/FP system simultaneously. Means and variances were compared, and inter-device agreement was assessed for each parameter. Between-device equivalence was demonstrated for all parameters assessed (two one-sided t tests: P < .001). For static parameters, six of 13 variables presented excellent interclass correlation coefficients (ICCs ≥ 0.90) and three had good ICCs (≥ 0.75 to < 0.90). Moreover, 10 of 11 spatiotemporal parameters showed excellent accuracy (ICCs ≥ 0.90), and three of four kinetic parameters showed moderate-to-good accuracy (ICCs between 0.78 and 0.89). In summary, FeetMe can be considered as a valid gait measurement tool compared to the high precision MoCap/FP system and could be used in clinical practice to assess a wide range of gait and posture parameters, overcoming some limitations of traditional systems.

Comparability between wearable inertial sensors and an electronic walkway for spatiotemporal and relative phase data in young children aged 6-11 years

BACKGROUND

Approaches to gait analysis are evolving rapidly and now include a wide range of options: from e-patches to video platforms to wearable inertial measurement unit systems. Newer options for gait analysis are generally more inclusive for the assessment of children, more cost effective and easier to administer. However, there is limited data on the comparability of newer systems with more established traditional approaches in young children.

RESEARCH QUESTION

To determine comparability between the Physilog®5 wearable inertial sensor and GAITRite® electronic walkway for spatiotemporal (stride length, time and velocity, cadence) and relative phase (double support time, stance, swing, loading, foot flat and push off) data in young children.

METHODS

A total 34 typically developing participants (41% female) aged 6-11 years old median age 8.99 years old (interquartile range 2.83) were assessed walking at self-selected speed over the GAITRite® electronic walkway while concurrently wearing shoe-attached Physilog®5 IMU sensors. Level of agreement was analysed by Lin's concordance correlation coefficient (CCC), Bland-Altman plots and 95% limit of agreement. Systematic bias was assessed using 95% confidence interval of the mean difference.

RESULTS

Excellent to almost perfect agreement was observed between systems for spatiotemporal metrics: cadence (CCC=0.996), stride length (CCC=0.993), stride time (CCC=0.996), stride velocity (CCC=0.988). The relative phase metrics adjusted for stride velocity showed improved comparability when compared to the unadjusted metrics: swing adjusted (adj) (CCC=0.635); stance adj (CCC: 0.879); loading adj: (CCC=0.626).

SIGNIFICANCE

Spatiotemporal metrics are highly compatible across GAITRite® electronic walkway and Physilog®5 IMU systems in young children. Relative phase metrics were somewhat compatible between systems when adjusted for stride velocity.

Pathological Gait Analysis With an Open-Source Cloud-Enabled Platform Empowered by Semi-Supervised Learning-PathoOpenGait

We present PathoOpenGait, a cloud-based platform for comprehensive gait analysis. Gait assessment is crucial in neurodegenerative diseases such as Parkinson's and multiple system atrophy, yet current techniques are neither affordable nor efficient. PathoOpenGait utilizes 2D and 3D data from a binocular 3D camera for monitoring and analyzing gait parameters. Our algorithms, including a semi-supervised learning-boosted neural network model for turn time estimation and deterministic algorithms to estimate gait parameters, were rigorously validated on annotated gait records, demonstrating high precision and consistency. We further demonstrate PathoOpenGait's applicability in clinical settings by analyzing gait trials from Parkinson's patients and healthy controls. PathoOpenGait is the first open-source, cloud-based system for gait analysis, providing a user-friendly tool for continuous patient care and monitoring. It offers a cost-effective and accessible solution for both clinicians and patients, revolutionizing the field of gait assessment. PathoOpenGait is available at https://pathoopengait.cmdm.tw.

Assessment of the reproducibility and accuracy of the Visia® Complexion Analysis Camera System for objective skin analysis of facial wrinkles and skin age

Objective

This study aimed to investigate the reproducibility and accuracy of the Visia® Complexion Analysis Camera System by Canfield Scientific for objective skin analysis.

Methods

Nineteen participants underwent facial capture with the Visia® camera following a standardised protocol. During the first session, the participants sat down and positioned their faces in a capture rig, closed their eyes and had their faces captured from the left, front and right sides, with threefold repetition of the captures from the front side. After 4 weeks, the participants underwent recapture in a similar manner. Based on the frontal views, data for two measurement methods of the Visia® camera system, the absolute scores and the percentiles, were obtained with regard to the skin criterion wrinkles via automated software calculation. Means and standard deviations were evaluated. Based on the side views, the data for the Truskin Ages® were calculated by the Visia® camera system and compared with the calendrical ages, which served as the gold standard for comparison.

Results

In the assessment of the reproducibility of the data of the capture system the standard deviation from the frontal captures among all participants was about 3% when the absolute scores of the wrinkles were compared with each other; specifically, the average deviation was 3.36% during the first capture session and 3.4% during the second capture session. Meanwhile, the standard deviation of the measurements was about 9% when the percentiles were compared; specifically, the average deviation was 8.2% during the first capture session and 10.7% during the second capture session. In the assessment of the accuracy the correlation between the calendrical age and the calculated Truskin Age® for both facial sides was very high at a correlation coefficient rho value of >0.8 (right side: r=0.896; left side: r=0.827) and statistically significant at a p-value of <0.001. The average calendrical age and Truskin Age® deviated only slightly from each other and did not differ significantly (right side: p=0.174; left side: p=0.190). The Truskin Age® was slightly higher than the calendrical age by a mean value of 1.37 years for both facial sides. The analysis of the absolute differences revealed that in 50% of the cases, there was a maximum difference of 3 years, and in 75% of the cases, there were maximum differences of 4.5 years for the right side and 5.5 years for the left side.

Conclusion

The assessment of the reproducibility and accuracy of the objective measurement method, the Visia® camera system, contributed to the validation of the system. The evaluation of the reproducibility revealed a satisfactory precision of the repeated captures when investigating facial wrinkles. Absolute scores should be preferred over percentiles owing to their better precision. The calculation of the accuracy of the Truskin Age® data from the Visia® camera system revealed only a slight deviation from the true calendrical ages. The correlation between both data groups was highly significant.

The Use of Embedded IMU Insoles to Assess Gait Parameters: A Validation and Test-Retest Reliability Study

Wireless wearable insoles are interesting tools to collect gait parameters during daily life activities. However, studies have to be performed specifically for each type of insoles on a big data set to validate the measurement in ecological situations. This study aims to assess the criterion validity and test-retest reliability of gait parameters from wearable insoles compared to motion capture system. Gait of 30 healthy participants was recorded using DSPro® insoles and a motion capture system during overground and treadmill walking at three different speeds. Criterion validity and test-retest reliability of spatio-temporal parameters were estimated with an intraclass correlation coefficient (ICC). For both systems, reliability was found higher than 0.70 for all variables (p < 0.001) except for minimum toe clearance (ICC < 0.50) with motion capture system during overground walking. Regardless of speed and condition of walking, Speed, Cadence, Stride Length, Stride Time and Stance Time variables were validated (ICC > 0.90; p < 0.001). During walking on treadmill, loading time was not validated during slow speed (ICC < 0.70). This study highlights good criterion validity and test-retest reliability of spatiotemporal gait parameters measurement using wearable insoles and opens a new possibility to improve care management of patients using clinical gait analysis in daily life activities.