Abnormal Gait Detection Using Wearable Hall-Effect Sensors

Navigating sensor-skin coupling challenges in magnetic-based blood pressure monitoring: Innovations and clinical implications for hypertension and aortovascular disease management

Non-invasive blood pressure monitoring has emerged as a critical frontier in cardiovascular healthcare, with magnetic sensors playing an increasingly pivotal role in wearable health technologies. This comprehensive review critically examines the complex challenges of sensor-skin coupling and its profound impact on the accuracy of blood pressure measurements in patients with hypertension and aortovascular disease. Despite the growing demand for precise, real-time health monitoring, significant limitations persist in current magnetic sensor technologies. Our analysis reveals how intricate interactions between sensor devices and skin characteristics including pigmentation, texture, and elasticity can substantially compromise measurement reliability. We systematically explore innovative approaches to mitigate these challenges, presenting cutting-edge strategies in advanced material development, adaptive calibration techniques, and sophisticated signal processing algorithms. The review synthesizes current research to demonstrate the multidisciplinary approaches necessary for enhancing magnetic sensor performance. By critically analyzing the nuanced interactions between sensor technologies and individual patient physiological profiles, we provide insights into developing more robust, personalized health monitoring systems. Our findings underscore the urgent need for continued innovation in non-invasive blood pressure monitoring, with direct implications for improved clinical assessment and patient outcomes in cardiovascular care.

Flexible Iron-On Sensor Embedded in Smart Sock for Gait Event Detection

Portable and wearable electronics for biomechanical data collection have become a growing part of everyday life. As smart technology improves and integrates into our lives, some devices remain ineffective, expensive, or difficult to access. We propose a washable iron-on textile pressure sensor for biometric data acquisition. Biometric data, such as human gait, are a powerful tool for the monitoring and diagnosis of ambulance and physical activity. To demonstrate this, our washable iron-on device is embedded into a sock and compared to gold standard force plate data. Biomechanical testing showed that our embedded sensor displayed a high aptitude for gait event detection, successfully identifying over 96% of heel strike and toe-off gait events. Our device demonstrates excellent attributes for further investigations into low-cost, washable, and highly versatile iron-on textiles for specialized biometric analysis.

Scientific Developments and New Technological Trajectories in Sensor Research

Scientific developments and new technological trajectories in sensors play an important role in understanding technological and social change. The goal of this study is to develop a scientometric analysis (using scientific documents and patents) to explain the evolution of sensor research and new sensor technologies that are critical to science and society. Results suggest that new directions in sensor research are driving technological trajectories of wireless sensor networks, biosensors and wearable sensors. These findings can help scholars to clarify new paths of technological change in sensors and policymakers to allocate research funds towards research fields and sensor technologies that have a high potential of growth for generating a positive societal impact.

Falls in Post-Polio Patients: Prevalence and Risk Factors

Simple Summary

People with post-polio syndrome (PPS) suffer frequent falls due to muscle weakness and problems with their balance. In order for a rehabilitation clinician to fit the patient with the optimal treatment plan to prevent imbalance and falls, we performed a simple 10-min walking test with 50 PPS patients. We also asked the patients how many falls they had experienced in the last year and they filled out a questionnaire regarding their balance confidence. We found that we can predict the occurrence of falls in PPS patients based on the consistency of their walking pattern. Since it is very easy to measure the walking pattern, our results may help rehabilitation clinicians to identify individuals at risk of fall and reduce the occurrence of falls in this population.

Abstract

Individuals with post-polio syndrome (PPS) suffer from falls and secondary damage. Aim: To (i) analyze the correlation between spatio-temporal gait data and fall measures (fear and frequency of falls) and to (ii) test whether the gait parameters are predictors of fall measures in PPS patients. Methods: Spatio-temporal gait data of 50 individuals with PPS (25 males; age 65.9 ± 8.0) were acquired during gait and while performing the Timed Up-and-Go test. Subjects filled the Activities-specific Balance Confidence Scale (ABC Scale) and reported number of falls during the past year. Results: ABC scores and number of falls correlated with the Timed Up-and-Go, and gait cadence and velocity. The number of falls also correlated with the swing duration symmetry index and the step length variability. Four gait variability parameters explained 33.2% of the variance of the report of falls (p = 0.006). The gait velocity was the best predictor of the ABC score and explained 24.8% of its variance (p = 0.001). Conclusion: Gait variability, easily measured by wearables or pressure-sensing mats, is an important predictor of falls in PPS population. Therefore, gait variability might be an efficient tool before devising a patient-specific fall prevention program for the PPS patient.