Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults

Overview of Radar-Based Gait Parameter Estimation Techniques for Fall Risk Assessment

Current methods for fall risk assessment rely on Quantitative Gait Analysis (QGA) using costly optical tracking systems, which are often only available at specialized laboratories that may not be easily accessible to rural communities. Radar placed in a home or assisted living facility can acquire continuous ambulatory recordings over extended durations of a subject's natural gait and activity. Thus, radar-based QGA has the potential to capture day-to-day variations in gait, is time efficient and removes the burden for the subject to come to a clinic, providing a more realistic picture of older adults' mobility. Although there has been research on gait-related health monitoring, most of this work focuses on classification-based methods, while only a few consider gait parameter estimation. On the one hand, metrics that are accurately and easily computable from radar data have not been demonstrated to have an established correlation with fall risk or other medical conditions; on the other hand, the accuracy of radar-based estimates of gait parameters that are well-accepted by the medical community as indicators of fall risk have not been adequately validated. This paper provides an overview of emerging radar-based techniques for gait parameter estimation, especially with emphasis on those relevant to fall risk. A pilot study that compares the accuracy of estimating gait parameters from different radar data representations - in particular, the micro-Doppler signature and skeletal point estimates - is conducted based on validation against an 8-camera, marker-based optical tracking system. The results of pilot study are discussed to assess the current state-of-the-art in radar-based QGA and potential directions for future research that can improve radar-based gait parameter estimation accuracy.

Artificial Intelligence Distinguishes Pathological Gait: The Analysis of Markerless Motion Capture Gait Data Acquired by an iOS Application (TDPT-GT)

Distinguishing pathological gait is challenging in neurology because of the difficulty of capturing total body movement and its analysis. We aimed to obtain a convenient recording with an iPhone and establish an algorithm based on deep learning. From May 2021 to November 2022 at Yamagata University Hospital, Shiga University, and Takahata Town, patients with idiopathic normal pressure hydrocephalus (n = 48), Parkinson's disease (n = 21), and other neuromuscular diseases (n = 45) comprised the pathological gait group (n = 114), and the control group consisted of 160 healthy volunteers. iPhone application TDPT-GT captured the subjects walking in a circular path of about 1 meter in diameter, a markerless motion capture system, with an iPhone camera, which generated the three-axis 30 frames per second (fps) relative coordinates of 27 body points. A light gradient boosting machine (Light GBM) with stratified k-fold cross-validation (k = 5) was applied for gait collection for about 1 min per person. The median ability model tested 200 frames of each person's data for its distinction capability, which resulted in the area under a curve of 0.719. The pathological gait captured by the iPhone could be distinguished by artificial intelligence.

Development of a data platform for monitoring personal health records in Japan: The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study

BACKGROUND

The Sustaining Health by Integrating Next-generation Ecosystems (SHINE) Study was developed as a data platform that incorporates personal health records (PHRs) into health-related data at the municipal level in Japan. This platform allows analyses of the associations between PHRs and future health statuses, and supports the production of evidence for developing preventive care interventions. Herein, we introduce the SHINE Study's profile and describe its use in preliminary analyses.

METHODS

The SHINE Study involves the collection of participants' health measurements and their addition to various health-related data from the Longevity Improvement & Fair Evidence (LIFE) Study. With cooperation from municipal governments, measurements can be acquired from persons enrolled in government-led long-term care prevention classes and health checkups who consent to participate in the SHINE Study. For preliminary analyses, we collected salivary test measurements, lifelog measurements, and gait measurements; these were linked with the LIFE Study's database. We analyzed the correlations between these measurements and the previous year's health care expenditures.

RESULTS

We successfully linked PHR data of 33 participants for salivary test measurements, 44 participants for lifelog measurements, and 32 participants for gait measurements. Only mean torso speed in the gait measurements was significantly correlated with health care expenditures (r = -0.387, P = 0.029).

CONCLUSION

The SHINE Study was developed as a data platform to collect and link PHRs with the LIFE Study's database. The analyses undertaken with this platform are expected to contribute to the development of preventive care tools and promote health in Japan.