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

Nutritional alterations, adverse consequences, and comprehensive assessment in spinal cord injury: a review

Spinal cord injury (SCI) leads to complex nutritional alterations, including energy imbalance, skewed macronutrient and micronutrient intake, and disrupted nutrient absorption and metabolism. These changes contribute to increased risks of obesity, cardiovascular disease, metabolic syndrome, and other comorbidities, profoundly affecting long-term recovery and quality of life. Despite the growing recognition of these challenges, nutritional assessment methods for SCI patients remain fragmented and insufficient. This review first outlines the major nutritional consequences and clinical implications of SCI, then focuses on current methods for assessing nutritional status in this population. Three major domains are discussed: body composition analysis, nutrient intake and absorption assessment, and energy metabolism monitoring. Traditional tools such as anthropometry, food diaries, and indirect calorimetry are discussed alongside advanced technologies including magnetic resonance imaging (MRI), dual-energy X-ray absorptiometry (DXA), and metabolomics. By highlighting both current limitations and emerging solutions, this review underscores the importance of personalized, technology-assisted nutritional assessment strategies to guide clinical decision-making and optimize outcomes for individuals with SCI.

Bioinspired Tilted Magnetized Flakes as a Self-Powered and Antislip Smart Outsole for Healthcare Monitoring and Human-Machine Interaction

Footwear smart devices capable of reliably capturing body actions and conveniently transmitting human-made information are of great interest to advance healthcare monitoring, human-machine interactions (HMIs), etc. while remaining challenging. Herein, we present a self-powered, antislip, and multifunctional smart outsole based on the gecko toe-inspired tilted magnetized flakes (TMFs) and underlying flexible coils. With the pressure-induced flake deflection and the built-in magnetic moment alignment, the TMF can produce a variable magnetic field to induce the voltage signals in coils for precise pressure perception and linear velocity sensing. The TMF-based smart outsole can thus serve as a real-time footwear recorder to monitor various body actions for exercise analysis and to track the abnormal landing speed for alerting potential injuries. The gecko toe-like flakes also enable the excellent antislip capability of the outsole with a much higher friction coefficient than the standard one of the low slip risk. By programming the magnetic moment alignments of the TMFs, a single-circuit outsole can further output multiple signals as encoded instructions for controlling the racing game. Along with excellent abrasion resistance and environmental immunity, the proposed outsole exhibits great potential as a convenient platform for reliable healthcare monitoring and efficient HMI.

Developing excellent plantar pressure sensors for monitoring human motions by using highly compressible and resilient PMMA conductive iongels

Based on real-time detection of plantar pressure, gait recognition could provide important health information for rehabilitation administration, fatigue prevention, and sports training assessment. So far, such researches are extremely limited due to lacking of reliable, stable and comfortable plantar pressure sensors. Herein, a strategy for preparing high compression strength and resilience conductive iongels has been proposed by implanting physically entangled polymer chains with covalently cross-linked networks. The resulting iongels have excellent mechanical properties including nice compliance (young's modulus < 300 kPa), high compression strength (>10 MPa at a strain of 90 %), and good resilience (self-recovery within seconds). And capacitive pressure sensor composed by them possesses excellent sensitivity, good linear response even under very small stress (∼kPa), and long-term durability (cycles > 100,000) under high-stress conditions (133 kPa). Then, capacitive pressure sensor arrays have been prepared for high-precision detection of plantar pressure spatial distribution, which also exhibit excellent sensing performances and long-term stability. Further, an extremely sensitive and fast response plantar pressure monitoring system has been designed for monitoring plantar pressure of foot at different postures including upright, forward and backward. The system achieves real-time tracking and monitoring of changes of plantar pressure during different static and dynamic posture processes. And the characteristics of plantar pressure information can be digitally and photography displayed. Finally, we propose an intelligent framework for real-time detection of plantar pressure by combining electronic insoles with data analysis system, which presents excellent applications in sport trainings and safety precautions.

AI-Assisted Insole Sensing System for Multifunctional Plantar-Healthcare Applications

The pervasive global issue of population aging has led to a growing demand for health monitoring, while the advent of electronic wearable devices has greatly alleviated the strain on the industry. However, these devices come with inherent limitations, such as electromagnetic radiation, complex structures, and high prices. Herein, a Solaris silicone rubber-integrated PMMA polymer optical fiber (S-POF) intelligent insole sensing system has been developed for remote, portable, cost-effective, and real-time gait monitoring. The system is capable of sensitively converting the pressure of key points on the sole into changes in light intensity with correlation coefficients of 0.995, 0.952, and 0.910. The S-POF sensing structure demonstrates excellent durability with a 4.8% variation in output after 10,000 cycles and provides stable feedback for bending angles. It also exhibits water resistance and temperature resistance within a certain range. Its multichannel multiplexing framework allows a smartphone to monitor multiple S-POF channels simultaneously, meeting the requirements of convenience for daily care. Also, the system can efficiently and accurately provide parameters such as pressure, step cadence, and pressure distribution, enabling the analysis of gait phases and patterns with errors of only 4.16% and 6.25% for the stance phase (STP) and the swing phase (SWP), respectively. Likewise, after comparing various AI models, an S-POF channel-based gait pattern recognition technique has been proposed with a high accuracy of up to 96.87%. Such experimental results demonstrate that the system is promising to further promote the development of rehabilitation and healthcare.