Microchannel pressure sensor for continuous and real-time wearable gait monitoring

The study addresses the need for highly sensitive, robust, and continuously monitoring wearable pressure sensors for applications such as gait analysis. Existing piezoresistive/piezoelectric sensors using CNTs or graphene offer high sensitivity but are limited to tens of kPa due to structural fragility at high pressure. Capacitive sensors with PDMS or ionic liquids can span broader ranges but often require costly microfabrication (etching, lithography) to optimize microstructures. Liquid metal-based sensors (notably EGaIn) promise wide-range detection due to high conductivity and fluidity at room temperature, but practical devices often depend on complex microchannel designs or multi-wire arrays that complicate wearables. The research question is whether a simple, low-cost fabrication with an optimized microchannel architecture in EcoFlex can yield a single-device, dual-channel pressure sensor capable of precise pressure localization and continuous, wide-range monitoring for real-time gait assessment.

The paper reviews advances in flexible pressure sensors across resistive, capacitive, and liquid-metal platforms. Carbon-based (CNT, graphene) sensors achieve low detection limits from subtle resistance changes but are constrained by limited quantification ranges and potential structural damage at higher loads. Capacitive sensors employing PDMS or ionic liquids provide broader ranges but rely on costly micro/nanofabrication to tailor microstructures. Liquid metal EGaIn-based devices leverage high conductivity and low viscosity for broad-range pressure detection; however, prior implementations often used complex curvilinear microchannels fabricated by laser cutting or lithography or grid/parallel arrays requiring multiple connections and data multiplexing, limiting wearable applicability. Prior work by the authors showed simple 3D-printed molds for EGaIn-EcoFlex multi-strain sensing. Building on this, the present design targets dual-channel, multi-section pressure localization with minimal wiring and low-cost fabrication while maintaining high sensitivity and durability.

Fabrication: Microchannels were formed using a 3D-printed rigid plastic mold (Objet500 Connex2, Stratasys; Vero photopolymer inks). Channel dimensions were designed as 250 µm (width) × 125 µm (height) with 500 µm spacing. EcoFlex 00-30 silicone prepolymer (1:1 mix, Smooth-On) was poured into the mold and cured at room temperature (~4 h) to form open microchannels, then wet-bonded upside-down onto a spin-coated EcoFlex backing layer to seal the channels. EGaIn liquid metal (Sigma-Aldrich) was injected via syringe, with an additional needle at the outlet to vent air and ensure complete filling. Electrical connections were made using 32 AWG enameled copper wires bonded to terminal ports of each sensing channel. Encapsulation integrity was verified under extreme stretching; measured channel dimensions were 254 µm width and 130 µm height (<2.5% error from design). Sensor architecture: The device contains two independent sensing channels arranged across five sections. Each section comprises 12 microchannels in total; the count per section differs between channels (channel 1 increases from 2 to 10; channel 2 decreases from 10 to 2), enabling pressure localization via relative resistance changes. Sections are separated by 1.5 mm; inter-channel spacing is 500 µm; active length ~17 mm. Measurement setup: A custom motorized press applied pressures 0–0.1 MPa (and up to 0.6 MPa in supplementary tests), with control over depth, speed, cycles, and duration. Contact area for standard tests was 85 mm² (17 × 5 mm²). Data were acquired using NI USB-6251 with NI SCB-68; 5 V DC was supplied and voltage/resistance changes were measured due to EGaIn microchannel deformation. Outputs R1 and R2 denote resistance from sensing channels 1 and 2, respectively. Data analysis: Pressure P = F/A, with A = 85 mm². Relative response ΔR/R0 was plotted versus pressure and fitted to y = Ax² + Bx + C. Sensitivity S was defined as d(ΔR/R)/dP; reported at 0.05 MPa. A pressure location index = ΔR1/(ΔR1 + ΔR2) was used to localize the pressed section; experimental indices were compared to theoretical ratios derived from channel counts per section. Cyclic tests included: 10 cycles up to 0.05 MPa across sections; hysteresis assessments at 0.01–0.06 MPa; durability test of 1000 cycles at 0.08 MPa; and larger-area pressing (17 × 90 mm²) and higher-pressure testing (up to 0.6 MPa). Gait monitoring application: The sensor was attached to the right forefoot using a Velcro strap, spanning from the hallux (section 1) to the little toe (section 5). R1 and R2 were recorded during standing, walking (correct/incorrect posture at 1 mph), jogging (5 mph), and walking at varied speeds (0–3 mph) on a treadmill. A wired setup interfaced with Arduino UNO for threshold-based section discrimination; a wireless system using Arduino MKR WiFi 1010 transmitted data to a PC via WiFi for real-time monitoring.

- High sensitivity: 66.07 MPa−1 (equivalently 66,070 kPa−1) at 0.05 MPa, attributed to EcoFlex 00-30’s low Young’s modulus (~0.5 MPa) enabling microchannel volume changes and EGaIn resistance modulation. - Measurement resolution: 0.056 kPa, derived from SNR-based analysis; average SNR 72.10 dB. - Reliable sensing range: 0–100 kPa with consistent parabolic response fits across sections; responses also reliable up to 0.6 MPa in supplementary tests. - Stretchability and robustness: Device stretches up to 250% without EGaIn leakage; durable over 1000 cycles at 0.08 MPa with stable performance. - Pressure localization: Dual-channel design and pressure location index ΔR1/(ΔR1 + ΔR2) accurately identified pressed sections; experimental indices matched theoretical predictions across time and pressure magnitudes. - Repeatability and low hysteresis: Overlapping resistance curves across varying pressures (0.01–0.06 MPa) for both channels confirmed reliability and low hysteresis. - Practical demonstrations: Enabled multi-section discrimination in user interfaces (piano keys and joystick) responding to light finger presses; in gait monitoring, distinguished correct vs. incorrect walking postures and jogging patterns, and maintained consistent pattern recognition across walking speeds (0–3 mph).

The proposed EGaIn-in-EcoFlex microchannel pressure sensor addresses the need for a single, low-cost, wearable device capable of continuous, real-time pressure monitoring and spatial localization. The dual-channel, five-section architecture allows precise pressure location identification using a simple ratio metric, avoiding complex wiring and multiplexing associated with grid arrays. Compared to prior sensors with linear but narrow ranges, the parabolic response of the hyperelastic EcoFlex-embedded liquid metal provides high sensitivity over a broad 0–100 kPa range with excellent SNR and fine resolution. The simple 3D-printed mold and syringe injection method eliminate expensive lithographic processes, improving manufacturability and scalability. In gait monitoring, the sensor captured dynamic center-of-pressure shifts across the forefoot, distinguishing correct from incorrect walking and differentiating gait modes (walking vs. jogging) irrespective of speed. The successful wired and wireless demonstrations indicate suitability for real-world wearable monitoring, supporting potential applications in clinical gait assessment and rehabilitation for mobility impairments (e.g., Parkinson’s disease).

A cost-effective, highly sensitive, and stretchable microchannel pressure sensor based on EGaIn and EcoFlex was developed for continuous, real-time wearable gait monitoring. The dual-channel, multi-section design achieved both absolute pressure measurement and precise localization, delivering a sensitivity of 66.07 MPa−1, SNR of 72 dB, measurement resolution of 0.056 kPa, reliable sensing over 0–100 kPa, and robust performance under prolonged cycling and large deformations. The sensor accurately differentiated gait postures and operated across walking/jogging speeds, with both wired and wireless implementations. These results highlight strong potential for clinical and rehabilitation applications. Future work could expand to multi-sensor arrays covering larger foot regions, integrate on-board processing for real-time gait analytics, and conduct multi-participant clinical validations.