A paper-based self-inductive folding displacement sensor for human respiration and motion signals measurement

Flexible sensors are gaining prominence in motion measurement and wearable health monitoring. Existing flexible sensors utilize various principles, including resistive, capacitive, inductive, and triboelectric sensing. Inductive sensors offer advantages like low material requirements and ease of wireless measurement. Self-inductive sensors, in particular, stand out due to their structural simplicity and stability, making them suitable for physiological signal monitoring. However, many flexible sensors employ expensive nanomaterials and complex fabrication processes. This research aims to develop a high-performance, low-cost self-inductive sensor using readily available materials. Paper, an inexpensive and environmentally friendly substrate, is chosen for its ease of folding, allowing for sensitivity enhancement through structural manipulation. The sensor is designed to measure displacement, which can be used to indirectly measure pressure and angles, making it suitable for a range of applications.

The paper reviews existing flexible inductive sensors, categorizing them into eddy current, mutual-inductive, and self-inductive types. Eddy current sensors, while extensively studied, often have complex structures. Mutual-inductive sensors require at least two separate parts, also complicating their design. Self-inductive sensors, using a single coil for sensing, offer better structural stability but lack extensive research. While PDMS and other polymers are commonly used substrates, their cost and processing complexity are drawbacks. The use of paper as a substrate for sensors is a growing area of research, offering cost-effectiveness and environmental benefits. The current methods for enhancing the sensitivity of displacement sensors often involve adding extra materials or creating complex structures, increasing the cost and fabrication difficulty. This work proposes a simple folding technique to improve sensitivity.

The PSIFS consists of a planar spiral coil fabricated on a paper substrate using copper foil tape. The sensing mechanism is based on the change in inductance resulting from three-dimensional deformation of the coil. Equations are derived to model the relationship between inductance and the geometric parameters of the coil, analyzing the contributions of self-inductance and mutual inductance. Finite element analysis (FEA) is used to simulate the electromagnetic behavior of the coil under different deformation modes (bending and folding). The fabrication process involves cutting the paper and copper tape to the desired shapes, and assembling the coil onto the paper substrate. The folding technique is implemented to enhance sensitivity. Experiments were conducted to evaluate the sensor's performance in terms of sensitivity, resolution, response time, stability, and robustness. The effects of various structural parameters (coil shape, duty ratio, number of turns, line width, and number of folds) on sensitivity were investigated both experimentally and through simulations. A two-dimensional analysis considering both sensitivity and size is performed to determine the optimal sensor design. The sensor's ability to measure pressure and angles, along with its application in monitoring respiration and joint movement, are demonstrated.

The research demonstrates the successful development of a highly sensitive and low-cost paper-based self-inductive folding displacement sensor. The sensor exhibits a displacement resolution of 20 µm and a measurement range of 43.2 mm, achieving an average sensitivity of 4.44% mm⁻¹ with a maximum error of 0.00904. The folding method significantly enhances sensitivity, achieving a three-fold increase with a single fold. Finite element simulations validate the sensing mechanism and the impact of structural parameters. The optimization of these parameters reveals that increasing the number of coil turns is the most effective way to enhance sensitivity while maintaining a reasonable sensor size. The sensor shows good linearity, low hysteresis (2.4%), and a short response time (<100 ms). Long-term stability tests demonstrate only a 1.02% inductance change after over 800 cycles. The PSIFS successfully monitors human respiration in various states (breath holding, feeble, normal, deep, and rapid breathing). Furthermore, it effectively tracks movements of fingers, wrists, elbows, shoulders, and knees. The sensitivity for angle measurement reaches 0.60% per degree, and the sensor also shows good potential for pressure measurement.

The findings demonstrate the successful realization of a high-performance, low-cost flexible sensor using readily available materials and simple fabrication methods. The significant sensitivity enhancement achieved through the folding method provides a novel approach to improving the performance of flexible sensors without resorting to complex fabrication processes or costly materials. The sensor's capabilities in monitoring various physiological signals (respiration and joint movement) highlight its potential for applications in wearable healthcare and human-machine interfaces. The ability to measure displacement, pressure, and angles indirectly expands its versatility. The use of paper as a substrate aligns with the growing trend of sustainable and eco-friendly electronics.

This research successfully demonstrates a high-performance, low-cost paper-based self-inductive folding displacement sensor. The simple design, ease of fabrication, and high sensitivity make it a promising candidate for various applications, particularly in wearable health monitoring and human-machine interfaces. Future research could focus on integrating the sensor with wireless communication for remote monitoring and exploring the sensor's use in other physiological signal measurements. Further investigations into material choices and structural optimizations could lead to even higher performance and robustness.

The current design and tests focus on specific configurations of coil geometry and folding patterns. Exploring a wider range of configurations could potentially optimize sensor performance for various application scenarios. The influence of temperature and humidity on the sensor's performance is noted, although considered minor compared to the displacement signals. More rigorous environmental testing would be beneficial to enhance the reliability and predictability of the sensor's operation in different conditions.