Key Takeaways & Executive Findings
- •• • The sensor achieves a sensitivity of 2.15 kPa⁻¹ within 0–10 kPa, enabling precise detection of subtle pressure variations critical for fall posture recognition and rehabilitation monitoring. • • Response and recovery times of 29 ms and 39 ms, respectively, ensure real-time monitoring capabilities essential for immediate fall detection and timely medical intervention. • • Demonstrates excellent long-term stability over 5000 cycles, indicating reliability for continuous wearable applications in elderly care without performance degradation. • • Full degradation within 6 hours and cell viability >98% confirm environmental sustainability and biocompatibility, addressing disposal concerns and enabling safe skin contact for prolonged use.
Abstract
Flexible pressure sensors (FPSs) are pivotal for fall detection and rehabilitation training, yet conventional synthetic-based sensors suffer from resource-intensive manufacturing, high costs, and environmental pollution. This study introduces a sustainable fabrication strategy for FPSs using natural materials. Electrodes were fabricated by treating natural wood strips with a flame retardant, converting them into high-quality graphene via cost-effective infrared laser processing, and transferring onto starch-based substrates. The dielectric layer comprised an electrospun composite nanofiber membrane of cyclodextrin and carbon nanotubes. The resultant capacitive FPS exhibited high sensitivity (2.15 kPa⁻¹ within 0–10 kPa), a low detection limit (~6.5 Pa), rapid response and recovery times (29 ms and 39 ms), and excellent long-term stability exceeding 5000 cycles. Biocompatibility was outstanding (cell viability >98%), and the sensor fully degraded within 6 hours. Integrated with wireless technology, the sensor enabled a fall detection and rehabilitation monitoring system. Data processing utilized a Tiny Machine Learning module on a mobile platform, transmitting data to a cloud-based system. The system accurately identified five common fall postures and assisted clinicians in guiding rehabilitation exercises, achieving recognition accuracies of 99% and 100%, respectively. This work offers a sustainable healthcare solution for elderly care, addressing environmental and economic limitations of existing FPS technologies.
1. Introduction
Global aging has intensified the need for effective health monitoring systems for the elderly, particularly for fall detection and rehabilitation. Falls are a leading cause of severe injury among the elderly, necessitating rapid and accurate detection systems. Current fall monitoring technologies primarily rely on computer vision and wearable devices. While computer vision offers non-invasive and real-time monitoring, it suffers from privacy concerns and vulnerability to occlusion and environmental interference, limiting its applicability. Wearable devices, in contrast, provide greater versatility, lower cost, and energy efficiency, making them suitable for large-scale deployment. However, conventional wearable pressure sensors are often fabricated from synthetic polymers, which involve resource-intensive manufacturing, high costs, and environmental pollution, hindering their sustainable adoption.
This study addresses these bottlenecks by introducing a fully biodegradable flexible pressure sensor derived from natural materials. The sensor leverages laser-induced graphene from wood and electrospun cyclodextrin/carbon nanotube nanofibers, achieving high performance while ensuring environmental compatibility. The integration with wireless technology and Tiny Machine Learning enables accurate fall detection and rehabilitation monitoring, offering a sustainable and clinically viable solution for elderly care.
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XIE Shengyu, LI Zihe, LI Chenhao, ZHOU Qihui, SUNG Ho-Kun, CHERGOGOR Leonid, YAO Zhao, LI Yang, LI Yuanyue (2026). Natural Material-Based Biodegradable Flexible Pressure Sensor for Fall Detection and Rehabilitation Monitoring in Elderly Care. SCIENCE CHINA Materials. https://doi.org/10.1007/s40843-025-3716-x
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Frequently Asked Questions
What are the failure mechanisms of the sensor under repeated mechanical stress, and how does it maintain performance over 5000 cycles?
The sensor's long-term stability over 5000 cycles is attributed to the robust mechanical properties of the laser-induced graphene electrodes and the electrospun nanofiber dielectric layer. The natural wood-derived graphene exhibits strong adhesion to the starch substrate, preventing delamination. The cyclodextrin/carbon nanotube composite provides elastic recovery, minimizing plastic deformation. No significant performance degradation was observed, indicating excellent fatigue resistance.
How does the cost of manufacturing this natural material-based sensor compare to conventional synthetic-based sensors?
The fabrication process utilizes cost-effective infrared laser processing and abundant natural materials (wood, starch, cyclodextrin), significantly reducing material and processing costs compared to synthetic polymer-based sensors that require complex synthesis and cleanroom facilities. The scalability of laser-induced graphene production further enhances cost competitiveness.
What are the scalability bottlenecks for mass production of this sensor, particularly regarding the electrospinning and laser processing steps?
Electrospinning of cyclodextrin/carbon nanotube nanofibers can be scaled using multi-nozzle systems, but uniformity and throughput may pose challenges. Laser-induced graphene production is scalable via roll-to-roll processing, but the transfer onto starch substrates requires careful handling to avoid damage. Overall, the processes are amenable to industrial scale-up with optimization.
How does the sensor's sensitivity and detection limit compare to commercial flexible pressure sensors, and what are the implications for fall detection?
The sensor exhibits a sensitivity of 2.15 kPa⁻¹ within 0–10 kPa and a detection limit of ~6.5 Pa, which is competitive with or superior to many commercial sensors. This high sensitivity enables detection of subtle pressure changes associated with different fall postures, facilitating accurate classification.
What is the clinical validation status of the fall detection system, and what are the next steps for regulatory approval?
The system achieved 99% accuracy in identifying five fall postures and 100% accuracy in guiding rehabilitation exercises in a controlled setting. Further clinical trials with elderly subjects are required to validate real-world performance. Regulatory approval would necessitate compliance with medical device standards, including biocompatibility (ISO 10993) and data security (HIPAA/GDPR).
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