The demand for highly sensitive and multifunctional pressure sensors capable of continuous pressure readings is rapidly increasing, particularly in fields like automotive, aerospace, healthcare, and robotics. Wearable and implantable devices necessitate accurate pressure sensing across a wide range, from small to large pressures. Various materials and methods have been explored to improve sensor performance, including the use of high-conductivity materials such as carbon nanotubes (CNTs) and graphene for piezoelectric or piezoresistive pressure sensors. While these materials exhibit exceedingly low detection limits, their quantification range is often limited to tens of kPa due to dimensional breakage under high pressure. Capacitive-type pressure sensors using materials like polydimethylsiloxane (PDMS) or ionic liquids offer wider detection ranges but often require complex and expensive fabrication processes, hindering cost-effectiveness. Liquid-state electronics using liquid metals, such as EGaIn, offer a promising approach to overcome these limitations. EGaIn's low viscosity at room temperature, excellent electrical conductivity, and high readability make it suitable for detecting a wide range of pressures. However, selecting a suitable substrate with durability and deformability is crucial for effective transmission of external stimuli to the sensing material. Deformable elastomers, such as EcoFlex, are preferred substrates due to their mechanical stability, chemical inertness, and biocompatibility. Many researchers have incorporated EGaIn into EcoFlex to create soft pressure sensors, but previous designs often involve complex and expensive fabrication methods or limit sensing to a single pressure point at a time. This research aims to address these challenges by developing a multi-pressure detection method with a single device, avoiding the complexities and discomfort associated with multiple wire connections, making it suitable for real-world applications, specifically wearable devices.

Existing pressure sensor technologies utilize diverse materials and mechanisms. Carbon nanotubes (CNTs) and graphene, known for high conductivity, are used in piezoelectric and piezoresistive sensors. These sensors achieve extremely low detection limits but are often limited in their pressure range due to material limitations under high pressures. Capacitive sensors, employing PDMS or ionic liquids, provide broader pressure ranges but suffer from complex and costly fabrication processes. Liquid metals, like EGaIn, offer a potential solution due to their unique electrical properties and compatibility with soft substrates. While various soft matrix/EGaIn sensors have been reported, many suffer from limitations in microchannel design, restricting them to sensing only one pressure point at a time. Although multi-pressure sensing within a single device has been attempted with grid or parallel microchannel designs, these approaches are impractical for wearable devices due to complex data handling and increased costs.

This research fabricated a novel pressure sensor using a cost-effective process. A 3D-printed mold was used to create a microchannel architecture in EcoFlex 00-30 silicone. EGaIn liquid metal was then injected into these microchannels. The resulting sensor comprised two sensing channels, each divided into five sections. The number of microchannels in each section varied systematically between the two channels, allowing for precise localization of pressure. The sensor was characterized using a custom-built pressure testing system, applying pressures ranging from 0 to 0.1 MPa. Resistance changes in the two sensing channels (R₁ and R₂) were measured under various loading/unloading pressures. A pressure location index, calculated as ΔR₁/(ΔR₁ + ΔR₂), was used to determine the location of applied pressure. The sensor's sensitivity was calculated from the slope of the output (ΔR/R) versus applied pressure (ΔP). Cyclic tests were performed to assess the sensor's durability and reliability over multiple cycles and varying pressures. Signal-to-noise ratio (SNR) calculations determined the sensor's measurement resolution. Finally, the sensor's functionality was demonstrated through real-time gait monitoring by attaching it to a subject's foot and measuring resistance changes during various walking speeds and postures. For the gait monitoring, both wired and wireless data acquisition systems were utilized. The wired system used an NI USB-6251 data acquisition system, while the wireless system employed an Arduino MKR WiFi 1010 microcontroller.

The fabricated pressure sensor demonstrated high sensitivity (66.07 MPa⁻¹), low measurement resolution (0.056 kPa), and a wide reliable sensing range (0-100 kPa). The innovative microchannel architecture allowed accurate identification of pressure location. The sensor exhibited a non-linear (parabolic) relationship between resistance change and applied pressure, unlike many previous linear-response sensors. Cyclic tests showed high reliability and durability even after 1000 cycles. The signal-to-noise ratio was found to be 72.10 dB. Gait monitoring experiments demonstrated the sensor's ability to distinguish between different walking postures (correct, incorrect, jogging) and walking speeds (0.5-3 mph) based on distinct pressure distribution patterns. The use of both wired and wireless systems successfully collected gait data, highlighting the sensor's potential for real-world applications, even in individuals with mobility impairments.

The results demonstrate a significant advancement in wearable pressure sensor technology. The sensor's high sensitivity, wide pressure range, and accurate pressure localization capabilities surpass many existing sensors. The non-linear response is advantageous as it allows for broader application across a wider range of pressures. The sensor's success in differentiating walking postures and speeds highlights its potential for clinical use in gait analysis and rehabilitation. The development of a wireless data acquisition system further enhances its practicality for real-world applications, particularly for patients needing continuous monitoring without the constraints of wired connections. The cost-effective fabrication method using readily available materials contributes to the sensor's potential for wider adoption.

This study successfully developed a highly sensitive and reliable pressure sensor using a cost-effective fabrication method. The sensor's unique microchannel architecture enables accurate pressure localization and differentiation of walking postures, paving the way for applications in clinical gait analysis and rehabilitation. Future research could focus on integrating the sensor into more sophisticated wearable systems for continuous, long-term health monitoring. Exploring the potential of this sensor for detecting other types of human motion is another potential direction for future research. Also the long term reliability of the wireless sensor and its potential for integration with machine learning algorithms for automatic gait analysis needs to be investigated.

While the sensor demonstrates promising results, further investigation into its long-term stability and robustness under diverse environmental conditions is necessary. The current study focused on gait monitoring on a single leg; further studies are needed to validate the sensor's performance in a broader population and with more diverse gait patterns. The small sample size used in the gait monitoring could be expanded for better statistical validation of the findings.