StressFit: A Hybrid Wearable Physicochemical Sensor Suite for Simultaneously Measuring Electromyogram and Sweat Cortisol

doi:10.21203/rs.3.rs-4821782/v1

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StressFit: A Hybrid Wearable Physicochemical Sensor Suite for Simultaneously Measuring Electromyogram and Sweat Cortisol

https://doi.org/10.21203/rs.3.rs-4821782/v1

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This study introduces StressFit, a novel hybrid wearable sensor system designed to simultaneously monitor electromyogram (EMG) signals and sweat cortisol levels. Our approach involves the development of a noninvasive skin patch capable of monitoring skin temperature, sweat pH, cortisol levels, and corresponding EMG signals using a combination of physical and electrochemical sensors integrated with EMG electrodes. StressFit was optimized by enhancing sensor output and mechanical resilience for practical application on curved body surfaces, ensuring accurate acquisition of cortisol, pH, body temperature, and EMG data without sensor interference. In addition, we integrated an onboard data processing unit with Internet of Things (IoT) capabilities for real-time acquisition, processing, and wireless transmission of sensor measurements. Sweat cortisol and EMG signals were measured during cycling exercises to evaluate the sensor suite’s performance. Our results demonstrate an increase in sweat cortisol levels and decrease in the EMG signal’s power spectral density following exercise. These findings suggest that combining sweat cortisol levels with EMG signals in real-time could serve as valuable indicators for stress assessment and early detection of abnormal physiological changes.

Biological sciences/Biotechnology

Physical sciences/Engineering

The wearable biosensor market experienced a twofold increase in size between 2014 and 2019 and is anticipated to reach $50 billion (Sharma et al., 2021). This growth is attributed to the expansion of Internet of Things (IoT) enabled flexible electronics and their applications in remote healthcare, human-machine interface (HMI), fitness monitoring (Evenson et al., 2015; Jeong et al., 2013; Searle and Kirkup, 2000; Steinhubl et al., 2015) and agricultural applications (Hossain et al., 2024; Hossain and Tabassum, 2023). However, the development of wearable sensors for acquiring electrophysiological (EP) signals, such as electrocardiogram (ECG), electroencephalogram (EEG), and electromyogram (EMG), faces significant challenges due to complex manufacturing processes and biocompatibility issues. Existing commercial EP signal acquisition devices typically involve thick electrodes affixed to the skin with terminal connections (Gerdle et al., 1999). Various research teams have proposed alternative flexible electrode designs, such as graphene tattoo-based (Kabiri Ameri et al., 2017), reduced graphene oxide-based on flexible polyethylene terephthalate (PET) substrate (Murastov et al., 2020), platinum electrodes on parylene-C substrate (Park et al., 2016), cellulose acetate composite electrodes on pristine cellulose network (PCN) substrate (Cataldi et al., 2016), and reduced graphene oxide electrodes on porous polydimethylsiloxane (PDMS) (Yun et al., 2017). While these electrodes offer flexibility, skin conformity, and high performance, their fabrication processes are intricate and time-consuming. In contrast, carbon electrodes fabricated via electrospinning (Huang and Chiu, 2021) and PDMS-supported graphene electrodes (Yang et al., 2021) offer simpler manufacturing processes but encounter challenges with skin conformity. In contrast, laser-induced graphene (LIG) presents a promising approach for electrode fabrication, offering flexibility, excellent electrical conductivity, thermal stability, and high surface area (Dallinger et al., 2020). Additionally, the pore size, thickness, and surface roughness of LIGs can be readily adjusted by varying laser parameters such as speed, power, and current (Duy et al., 2018). In addition to measuring physical parameters like heart rate (Trung and Lee, 2016), respiration (Kundu et al., 2013), skin temperature (Wen et al., 2016), and brain/neural signals (Casson et al., 2010), current research efforts on LIG (Sharma et al., 2024; Tehrani et al., 2019; Tu et al., 2023; Yang et al., 2020) are primarily directed toward biochemical sensors, which enable the tracking of clinically significant biomarkers. The abundance of stress-related biomarkers found in sweat, such as metabolites, amino acids, proteins, and hormones, renders sweat an optimal medium for exploring physiological conditions, intoxication, and metabolic syndrome (Emaminejad et al., 2017; Gao et al., 2016). Among the commonly encountered stress biomarkers are cortisol, C reactive protein (CRP), Interleukin-6 (IL-6), IL-8, glucose, prolactin, oxytocin, and catecholamine (Tu et al., 2023). Studies indicate that the stress levels of athletes are influenced by both physiological signals (Krantz et al., 2004) and adrenal-released biomarkers like cortisol and adrenaline (Jang et al., 2018; Kaushik et al., 2014). Prior research underscores the significant role of electromyography (EMG) in identifying normal mental stress conditions (Wijsman et al., 2010), as well as mental stress associated with physical exertion (Larsson et al., 1995) and repetitive work (Rissén et al., 2000). Similarly, cortisol, a steroid hormone, has been shown to correlate with both physical and mental stresses, including post-traumatic stress disorder (PTSD) (Hauer et al., 2009), Cushing syndrome, and Addison’s disease (Stevens et al., 2008; Zhou et al., 2004).

Wearable and flexible biosensors highlighted in the literature for stress monitoring typically focus on either analyzing a single marker, such as sweat cortisol levels (Samson and Koh, 2020), or conducting multiplexed analysis of chemical markers (Sudol and Mann, 2017). Noteworthy examples of biochemical sensors include CortiWatch (Rice et al., 2019), molecularly imprinted polymer sensors (Parlak et al., 2018), and LIG-based electrochemical sensors (Torrente-Rodríguez et al., 2020). The emerging trends of sensor fusion and integration with IoT have sparked interest in combining physiological and chemical sensing capabilities. This study introduces StressFit, a hybrid, noninvasive wearable sensor suite capable of reliably measuring both physiological and biochemical signals on a single platform. Furthermore, on-device temperature and pH sensors were integrated to correct for variations in sweat cortisol levels, minimizing measurement errors. Subsequently, signal processing techniques were employed to discern differences in EMG signals before and after exercise, which, when combined with real-time sweat cortisol level monitoring, facilitates more precise monitoring of stress levels. In summary, the key innovations of this research include:

A hybrid, flexible physicochemical sensor suite combining EMG signal detection with sweat cortisol level monitoring, supplemented by integrated pH and temperature corrections.
Utilization of LIG electrode fabrication, enabling cleanroom-free, roll-to-roll, and straightforward production of electrodes on a flexible substrate.
Integration of a data processing unit with IoT capability.

The StressFIT Platform

The StressFIT platform comprises a skin-interfaced multilayered sensor patch for monitoring sweat cortisol levels and EMG signals. During a stress period, the hypothalamus releases corticotropin-releasing hormone (CRH), which stimulates the anterior pituitary gland to secrete adrenocorticotropic hormone (ACTH). ACTH then triggers the adrenal glands to produce cortisol (Angelousi et al., 2000; Ramamoorthy and Cidlowski, 2016). This cortisol eventually enters the circulatory system, and a portion may be excreted through sweat (Fig. 1a). The sensor patch incorporates a three-electrode-based electrochemical sensor for cortisol detection, along with pH and temperature sensors for measurement corrections. Three-electrode-based EMG electrodes are positioned opposite the electrochemical sensors. All the electrodes are engraved on a flexible polyimide sheet using LIG (Fig. 1b). The cortisol sensor is selectivity functionalized with a polyaniline-gold nanoparticle doped multiwalled carbon nanotube (PANI-AuNP MWCNT) nanocomposite layer atop the bare LIG electrode, followed by the functionalization of anti-cortisol antibody. Subsequently, a bovine serum albumin (BSA) layer was applied to prevent nonspecific binding of interfering molecules, ensuring stability, heightened sensitivity, and minimizing denaturation (Fig. 1c). A medical gauge was used to wick sweat from the skin to the sensor surface. Data from the sensors is collected using a custom electronic circuit and transmitted to the Blynk Cloud platform, accessible via web application or mobile devices. The electronic-level block diagram in Fig. 1d outlines the flow of electrical signals involved in the electrochemical cortisol sensor, physical sensors (pH and Temperature), and EMG measurements. The cortisol sensor functions via amperometry, while the pH and temperature sensors exhibit resistive behavior. For amperometric sensing of cortisol, a preprogrammed microcontroller produces a stimulation waveform that applies a specific sensing voltammogram to electrodes connected to a potentiostat through an external digital-to-analog converter (DAC). The resulting current response is amplified and converted to voltage by a transimpedance amplifier (TIA) before being read by an analog-to-digital converter (ADC) having 8-bit resolution. Resistance variations from the physical sensors are measured via voltage dividers, which convert resistance changes to voltage values, and then send the voltage readings to the ADC. The EMG electrodes send the EMG signals to the ADC via an EMG interface. Figure 1e shows the real-time implementation of the StressFIT system. Data acquired from the sensors are wirelessly transmitted over WiFi to a cloud platform (Fig. 1f) for further analysis. This setup enables monitoring of mental stress levels post-exercise by analyzing both cortisol levels and EMG signals.

Electrode Fabrication

The step-by-step fabrication procedure of the integrated physicochemical sensor is explained in Supplementary Information, Note S1 and illustrated in Figure S1.

Characterization of LIG Electrodes

The impact of exposure parameters of the laser beam on the material properties of LIG was examined. Initially, the laser power was maintained at a constant value while adjusting the speed to determine the optimal speed yielding the lowest electrode resistance (Fig. 2a). Subsequently, the speed was held constant while varying the laser beam power to identify the optimal power level resulting in minimal resistance of the LIG electrodes (Fig. 2b). Finally, the laser current was adjusted while keeping the power and raster speed constant, leading to the generation of an optimum resistance plot as shown in Fig. 2c. This iterative process was repeated until the optimal combination of power, speed, and current was determined. The parameters of a 3.5 W pulsed laser beam operating at 20 mA and a speed of approximately 10 mm/s yielded the lowest resistance, and hence these beam settings were employed for all subsequent LIG electrode fabrication. The scanning electron microscopic (SEM) image of the bare LIG electrode surface is shown in Fig. 2d. The SEM image shows a porous LIG surface, attributed to photothermal conversion followed by gasification (Bai et al., 2022). Considering that the LIG electrodes were fabricated on a flexible polyimide substrate, the sheet underwent up to 100 cycles of bending in the vertical and horizontal directions, and the resulting variations in the surface morphology were recorded through SEM (Supporting Information Figure S2).

LIG Functionalization for pH Sensing

The pH sensor consisted of two electrodes: a working electrode (WE_pH) and a common reference electrode shared with the cortisol sensor. Instead of adopting the open-circuit voltammetry approach, which would require an additional voltage booster (Galvan et al., 2022), we utilized the transduction of pH-induced resistance variation across the electrodes into a voltage signal to measure sweat pH levels. When the two electrodes are covered with sweat, a conductive path forms between them, allowing the pH sensor to function as a variable resistor. The LIG working electrode (identified as WE_pH in Figure S1vi) was coated with a pH-selective material, polyaniline (PANI). The process commenced by immersing the pH electrodes in a chemical mixture containing 0.1 M aniline and 0.1 M HCl, followed by 90 cycles of Cyclic Voltammetry (CV) conducted from − 0.2 V to 0.6 V at a scan rate of 50 mV/s (Supporting Information, Figure S3). At the conclusion of this process, PANI nanofibers were deposited on the WE_pH. These PANI nanofibers exhibit high sensitivity to hydronium ions. Furthermore, the oxidation-reduction equilibrium between the hydronium ion and PANI layer imparts a high surface area, potential stability, biocompatibility, and reproducible characteristics to the working electrode (Park et al., 2019). Different cycles of PANI layer have been tested (Supporting Information, Figure S4a), and it has been observed that 90 cycles of deposition provide the optimized sensitivity and surface morphology (Supporting Information, Figure S4).

LIG Functionalization for Temperature Sensing

The temperature sensor also exhibited resistance variations in response to changes in temperature. The temperature sensor was fabricated by drop-casting poly(3,4-ethylene dioxythiophene): poly(styrene sulfonate) (PEDOT:PSS) onto the two LIG electrodes (referred to as WE_Temp), as depicted in Figure S1vi. In summary, a mixture comprising 10 mg of PEDOT:PSS (1.3 wt% in water) and 5 mg of Triton X-100 surfactant underwent ultrasonication for 30 minutes. Following this, (3-glycidyloxypropyl)trimethoxysilane (GOPS) was added to the mixture at a weight ratio of 9:1 and further sonicated for 30 minutes. The mixture was then desiccated for 50 minutes to completely remove air bubbles. Subsequently, 10 µL of the solution was drop-cast over the LIG electrodes and desiccated for an additional 10 minutes. The coating was annealed at 150°C for 1 hour. To prevent degradation of the coating upon exposure to moisture, a 10 µm-thick Kapton tape was applied to cover the PEDOT:PSS coating (Noushin et al., 2022a). Although different weight ratios of GOPS to PEDOT:PSS have been investigated (Supporting Information, Figure S5), weight ratio of 9:1 provides the best response in terms of sensitivity.

LIG Functionalization for Cortisol Sensing

PANI is widely utilized in electrochemical biosensing due to its conductive properties and numerous amino groups, which facilitate biomolecule binding, accelerate electron transfer, and enhance redox stability. When combined with multi-walled carbon nanotubes (MWCNTs) and gold nanoparticles (AuNPs), PANI forms a composite coating that significantly increases the surface area to volume ratio, leading to highly sensitive biosensing applications (Shao et al., 2021). Additionally, the presence of CNTs within the PANI-AuNP-MWCNT nanocomposite matrix serves as a dopant for PANI, ensuring composite stability during electrochemical measurements and maintaining steady, reversible biosensor behavior (Zea et al., 2020). The synthesis of PANI-AuNP-MWCNT composite involved mixing the precursor aniline with the oxidant AuNP-MWCNT. PANI was synthesized by dissolving 0.1 M aniline in an equal volume of 0.1 M aqueous HCl solution via sonication. Subsequently, the resulting PANI solution was combined with 1 mg mL^− 1 of AuNP-MWCNT nanocomposite, the preparation methods of which are detailed elsewhere (Noushin et al., 2022b). The cortisol sensor electrodes were then immersed in the resulting aqueous solution, and 200 voltammetry cycles were conducted from − 0.4 V to + 0.8 V at a scan rate of 0.05 V/s to co-deposit PANI-AuNP-MWCNT on the working electrode, WEc. The SEM image of PANI-AuNP-MWCNT presented in Fig. 2e shows an increase in the surface area compared to the bare LIG shown in Fig. 2d (Sabzi et al., 2010).

For covalent immobilization, a solution of anti-cortisol at a concentration of 2 mg mL^− 1 in phosphate-buffered saline (PBS; pH = 7.4) was mixed with 0.4 M N′-ethylcarbodiimide hydrochloride (EDC) as a coupling agent and 0.1 M N-Hydroxysuccinimide (NHS) as an activator, then centrifuged at 250 rpm for 30 min. Five microliters of the resulting antibody solution were drop-cast onto the PANI-AuNP-MWCNT electrode and allowed to dry for 12 hours in a humid chamber. To prevent nonspecific binding, a 2 mg mL^− 1 solution of bovine serum albumin (BSA) prepared in PBS was drop-cast onto the electrode, followed by rinsing with PBS and Tween 20 to remove any unbound anti-cortisol. The functionalized BSA/anti-cortisol/PANI-AuNP-MWCNT electrode was stored at 4°C when not in use (Arya et al., 2011).

Preparation of Artificial Sweat Sample

The performance of the cortisol and pH sensors was initially assessed using artificial sweat (AS) samples. Cortisol levels in perspiration typically range from 0.02 to 0.4 µM (equivalent to 8.16 to 141.7 ng/mL) (Jang et al., 2018; Kaushik et al., 2014), while sweat pH values typically fall within the range of 4 to 7 (Chung et al., 2021). Consequently, AS samples were spiked with a diverse range of cortisol concentrations (ranging from 0.1 pM to 1000 nM) and pH levels (ranging from 2 to 13). The AS solution was prepared by combining four chemical components: 85 mM NaCl, 13 mM KCl, 17 mM lactate, and 16 mM urea in 0.1 M PBS, pH 7.4. The pH of the AS solution was adjusted by varying the volumes of 0.01 M HCl and 0.01 M NaOH added to the aforementioned chemical mixture (Tang et al., 2021).

Characterization of Cortisol Sensor

All CV characterizations were conducted using the CHI660E series electrochemical workstation. A CV plot was recorded following each functionalization step, as depicted in Fig. 2f. The electrochemical deposition of the PANI-AuNP-MWCNT nanocomposite coating notably augmented the redox current, consequently enhancing sensitivity (as verified by the higher slope of the calibration curve with the PANI-AuNP-MWCNT coating, Supporting information, Figure S6). Subsequent to the PANI-AuNP-MWCNT deposition, the sequential addition of each successive layer led to a reduction in peak redox current due to the insulating property of the immobilized layers. The CV plots obtained at different scan rates and the resultant calibration plot (Supporting Information, Figure S7) confirmed that electron transfer kinetics through the nanocomposite coating were diffusion-controlled and reversible, according to Randles-Sevcik equation (Ali et al., 2018). In addition, Fourier transform infrared (FTIR) spectroscopy was conducted on the AuNP-doped MWCNT to confirm the presence of functional groups such as OH (between 3,500 and 4,000 cm^− 1), C-H (at 2,975–3,065 cm^− 1, 1,390–1700 cm^− 1, and 1,100–1,160 cm^− 1 range), C-O and C-Ö bonds (1,200 and 1790 cm^− 1 respectively) and Au-S (at 750 cm^− 1) (Thamri et al., 2016) (Fig. 2g).

Characterization of EMG Sensor

The performance of the EMG sensor was initially evaluated by examining the skin-to-electrode impedance at various frequencies. The plot depicted in Fig. 2h illustrates two distinct operational regions. For frequencies exceeding 50 Hz, the absolute impedance (|z|) of both commercial (Sparkfuns_myoware 2.0) and LIG-based EMG electrodes remained nearly constant, with mean values of 22.42 kΩ (± 0.17) and 21.35 kΩ (± 0.16), respectively. Conversely, at frequencies below 50 Hz, motion artifacts predominated, resulting in a significant increase in impedance as frequencies decreased. However, the impedance trends were comparable for both the LIG and commercial electrodes.

Data Acquisition and Processing Circuit

The full data acquisition and processing circuit shown in Fig. 1d is composed of a potentiostat, an EMG interface circuit, and a microcontroller unit (ESP32). The microcontroller generates a staircase voltage waveform serving as an input to the cortisol sensor and a transimpedance amplifier measures the current generated by the cortisol sensor. Our potentiostat enabled CV electrochemical analysis. Cortisol levels measured using our potentiostat were validated against values obtained from the commercial electrochemical workstation CHI660E, yielding a coefficient of variance below 3%. The resistance variations of the temperature and pH sensors were assessed using a voltage divider topology. Details of the voltage divider topology and the potentiostat are provided in our prior work (Noushin et al., 2022a).

The EMG interface circuit comprised of several components. The voltage pulses acquired by the EMG electrodes was first boosted with a gain of 22 in the preamplification stage. This amplified signal was then fed into an instrumentation amplifier, the AD620, which had a Common-Mode Rejection Ratio (CMRR) greater than 80dB. The output from this amplifier underwent second-order filtering, using a 20 Hz low-pass filter (LPF) and a 500 Hz high-pass filter (HPF). Subsequently, a low-power Sullen Key bandpass filter (with a passband ranging from 60 to 300 Hz) was used to filter the resulting EMG signal. The signal was then amplified with a gain of 101 and passed through a 67 Hz filter to eliminate 50 Hz noise. The output of this 67 Hz filter was finally passed into the ADC pin of the microcontroller. The detailed EMG circuit diagram is presented in Supporting Information, Figure S8. The wearable system operates using a Lithium Ion Battery rated at 3.7V and 2AHr. A single full charge allows the system to run continuously on the battery for 5 hours, after which the battery was recharged.

Human Subjects Participation

This study did not involve the participation of any external human subjects. The Principal Investigator volunteered to collect data from herself. As there were no other participants, informed consent was not required according to the institution’s Human Research Protection Program.

Both temperature and pH sensors are resistive in nature, exhibiting variations in resistance in response to changes in temperature and pH, respectively. The mean skin temperature and sweat pH levels are approximately 33°C and 6.3, respectively. Consequently, the pH sensor underwent reproducible calibration for pH values ranging from 2 to 13 (Fig. 3a-b), while the temperature sensor was also reproducibly calibrated for temperatures within the range of 25°C to 65°C (Fig. 3c-d). Both sensors displayed exceptional reproducibility, with a cycle to cycle reproducibility of 99.35%.

To characterize the cortisol sensor, cyclic voltammetry was conducted for varying concentrations of cortisol in AS, with the redox peak current in Fig. 3e displaying an inverse relationship with increasing cortisol concentrations. This phenomenon primarily resulted from the increasing insulating property of the sensor surface upon binding of anti-cortisol antibodies to increasing concentrations of cortisol. Figure 3f presents the calibration plot derived from the CV plots shown in Fig. 3e, demonstrating a linear relationship between current response and cortisol concentrations.

To further assess sensor selectivity, the sensor was exposed to a mixture of the target analyte (cortisol) and various co-existing sweat species. Specifically, two different concentrations of cortisol (10 pM and 100 nM) were mixed with interferents including 1 fM IL-10, 1 mM glucose, 1 nM IL-6, 1µM ascorbic acid, 1µM lactate, 1µM tyrosine, and 1 µM CRP. A negligible current variation was observed in the absence of cortisol; however, in the presence of cortisol, a measurable change in redox current was noted, as depicted in Fig. 3g. The EMG signal obtained from the electrode positioned opposite the electrochemical sensor exhibited a peak-to-peak voltage of up to 1.3 mV, as shown in (Fig. 3h), with the highest negative peak reaching approximately − 0.6 mV. Notably, these peak voltages may vary depending on the level of amplification and noise cancellation applied in the developed acquisition circuit and signal processing of the acquired EMG data, although no signal processing was applied in this case. Furthermore, the pH sensor was calibrated at different temperatures, showing a minimal coefficient of variance of less than 2%. Similarly, the temperature sensor underwent calibration at various relative humidity values, with a coefficient of variance of less than 2.8%. These findings are illustrated in Fig. 3i-j, affirming that the pH sensor remained unaffected by temperature changes and that the Kapton tape effectively shielded the temperature sensor from fluctuations in the ambient humidity. Reproducibility and repeatability tests of the cortisol sensor involved measuring three different cortisol levels with three identical sensors over three consecutive days, with results displayed in Supporting Information, Figure S9, indicating the sensor's reproducible and repeatable nature with minor coefficient of variance between measurements on different days. The cortisol sensor also demonstrated resilience against variations in sweat pH and temperature. Calibration plots for different pH and temperature values (Fig. 3k-l) exhibited a coefficient of variance of less than 3.5%, confirming the feasibility of the proposed sensors for on-body measurements. Moreover, pH and temperature-induced variations in cortisol levels were corrected by the onboard pH and temperature sensors.

The correction factors (denoted by the symbol $\:f$) for the intercept and slope were calculated at different temperature and pH values utilizing the following equations, with a reference temperature of 25°C and a reference pH of 7.0 (Hossain and Tabassum, 2023; Noushin et al., 2022b):

$$\:{f}_{intercept\:\left(temp\right)}=\frac{{intercept}_{temp}}{{intercept}_{25^\circ\:C}}\:\:\:\:{f}_{intercept\left(pH\right)}=\frac{{intercept}_{pH}}{{intercept}_{7.0}}$$

$$\:{f}_{slope\:\left(temp\right)}=\frac{{slope}_{temp}}{{slope}_{25^\circ\:C}}\:\:\:\:{f}_{slope\left(pH\right)}=\frac{{slope}_{pH}}{{slope}_{7.0}}$$

The cortisol sensor responses underwent correction by recalculating the slope and intercept of the calibration plots depicted in Fig. 3k-l. The corrected intercept and slope values were determined using the following equations (Noushin et al., 2022b):

$$\:intercept\:\left(correct\right)={f}_{intercept\:\left(temp\right)}{f}_{intercept\left(pH\right)}intercept\left(original\right)\:$$

$$\:slope\:\left(correct\right)={f}_{slope\:\left(temp\right)}{f}_{slope\left(pH\right)}slope\left(original\right)$$

Cortisol levels measured with our sensor are compared against those measured with high performance liquid chromatography (HPLC) (Supporting Information Figure S10-S11a, Note S2) (Jia et al., 2016). An excellent correlation was observed between the sensor-measured and HPLC-measured cortisol levels with a Pearson correlation coefficient of 0.99.

Due to the wearable design of the sensor suite, there exists a significant possibility that the sensors may undergo bending deformations during real-world use, particularly in the presence of motion artifacts. To evaluate the durability of the sensor suite under these conditions, the sensors underwent bending at a 45° angle both horizontally and vertically (Fig. 4a) using the Thorlabs Motorized Translation Stage Z8, at a speed of 2 mm/s, for 100 cycles. It was observed that the sensor responses remained relatively stable under both vertical (Fig. 4b-d) and horizontal (Fig. 4e-g) bending conditions. The coefficient of variance for 100 cycles of bending was approximately 2.37%. The slight deviation observed may be attributed to the potential flaking of the LIG from the polyimide substrate (Supporting Information, Figure S2). Additionally, the bending-induced hysteresis is less than 5.23%.

Circadian Rhythm Analysis

Cortisol levels follow a distinct diurnal pattern, peaking upon waking and declining throughout the day in both plasma (Oster et al., 2017) and saliva (Ivars et al., 2015). Previous studies indicate that sweat cortisol levels share similarities with those in saliva (Russell et al., 2014). In this study, we hypothesized that circulating cortisol molecules migrate from eccrine and apocrine glands, where they are gradually secreted into sweat, and ultimately excreted through sweat pores onto the epidermal surface (Katchman et al., 2018). Therefore, it is plausible to speculate that sweat cortisol levels may exhibit a degree of correlation with the circadian rhythm, primarily governed by the body's biological clock and the light-dark cycle (Fig. 4h). If cortisol levels follow the circadian pattern observed in the body's circulatory systems, it could provide valuable insights into several mental conditions (Herbert, 2013; Yehuda, 1997). We tested our hypothesis by monitoring the sweat cortisol levels in a healthy individual over four days. Each day consistently displayed elevated cortisol levels in the morning (AM) and lower levels in the afternoon (PM), mirroring the diurnal patterns seen in circulating blood cortisol levels (Fig. 4i). The cortisol concentration in sweat and analysis of their diurnal patterns can offer valuable insights for psychoneurological studies (Dickerson and Kemeny, 2004; Kudielka and Wüst, 2010) and serve as a crucial indicator for assessing human performance (Mariotti, 2015). For example, the heightened sensitivity of the Hypothalamus-pituitary-adrenal (HPA) axis to external stimuli is a key differentiating factor between PTSD and other psychiatric disorders (Yehuda, 1997).

Continuous Stress Analysis

Additionally, we examined the correlation of sweat cortisol levels with stress induced by physical exercise within a brief timeframe. To accomplish this, we chose aerobic activities such as cycling, which is a robust trigger for cortisol secretion (Kanaley et al., 2001). In this investigation, a 55-minute session of stationary cycling at a consistent workload was chosen to analyze sweat cortisol concentrations (Fig. 4j). The sweat cortisol levels were assessed every 5 minutes in a healthy individual for four Days. It was noted that the sweat cortisol levels gradually increased for nearly 40 minutes during biking and then slightly decreased towards the end of the exercise (Fig. 4k), consistent with previous findings (Torrente-Rodríguez et al., 2020) (Sardesai et al., 2023).

Crosstalk Analysis

One critical aspect to consider when designing a multimodal integrated sensor system is obtaining reliable data from the sensors. By "reliable," we mean obtaining clean signals from each sensor without any interference or crosstalk between different sensing modalities. In this study, we observed instances of crosstalk between the cortisol and EMG sensors. The close proximity of the cortisol sensor to the EMG sensor (Fig. 5a) causes the high-frequency pulses generated by the EMG sensor to induce signal drift in the cortisol sensor. This is evidenced by the distortions in the cortisol sensor signal when the EMG sensor’s signal recording is switched ‘on’ and ‘off’, as illustrated in Fig. 5b-c. This phenomenon occurs due to crosstalk observed from the EMG measurement to the cortisol measurement. To mitigate this cross-talk issue, the separation between the cortisol and EMG sensors was increased from 3.0 mm (Fig. 5a) to 5.0 mm (Fig. 5d), reducing the interference between the sensor signals (Fig. 5e-f), a strategy also supported by previous research (Sempionatto et al., 2021). No crosstalk was observed between the cortisol measurements and the pH or temperature measurements.

EMG Signal Interpretation

Analyzing the raw EMG signal is challenging due to its random fluctuations around the baseline (0V), making it difficult to extract meaningful information (Akkaya et al., 2012). Therefore, to interpret the raw EMG signal, a transformation function must be applied. In this work, the sensor patch was positioned on the Pronator Teres muscle in the hand, and EMG data was collected during repeated weight pulling exercises. EMG bursts were evident when the muscle was in motion, while no pulses were observed when the muscle was at rest, as shown in Fig. 6a.

Two widely used functions for interpreting the EMG signal are the root mean square (RMS) value and the average rectified value (ARV), also known as mean absolute value (MAV). The RMS approximates the standard deviation of the signal, representing the extent of deviation from zero (applicable when the EMG signal has a mean of 0V). It corresponds to the square root of the total power within the signal. Conversely, the ARV computes the average of the rectified signal. Figure S12 in the Supporting Information shows the RMS and ARV values computed for the EMG signals recorded at the start and end of the cycling exercise depicted in Fig. 4j-k. The ARV correlates with the RMS of the EMG signal when the muscle activity level is adequately high (Gatewood et al., 2017). The amplitude of the EMG signal is usually computed for a brief time interval (or a fixed window) within which the EMG signal is presumed to be nearly stationary. During isometric contractions, the signal is segmented into windows or epochs lasting around 0.5 to 1 second. Conversely, when examining dynamic contractions, shorter epoch durations are often utilized to capture finer amplitude changes occurring throughout the contraction sequence. By employing a moving average window, the EMG amplitude is assessed for a brief segment (or window) of the EMG signal. Subsequently, the EMG window being examined is progressively shifted forward in time, allowing for the calculation of an estimate for each consecutive portion of the signal. The moving average serves as a straightforward technique for smoothing EMG data, functioning as a low-pass filter that diminishes random fluctuations. Short-duration windows enable the detection of rapid changes in muscle movement, while longer epochs result in a more pronounced smoothing effect of the raw EMG signal. However, computation of moving average on the raw EMG signal might generate some abnormalities. To prevent this, the signal is rectified first (Fig. 6b) before being inputted into the moving average filter. Computing the moving average with epochs that comes with overlap in time (50% overlap or more between successive windows) can contribute to significantly decline in variability of the EMG voltage waveform (Fig. 6c). However, this approach may reduce the ability to detect abrupt changes in EMG signal amplitude. Reducing the cut-off frequencies will lead to a more even EMG amplitude profile. For example, the 5Hz low-pass filter produces a smoother signal (Figure S13) compared to the output depicted in Fig. 6c.

A notable characteristic of the EMG signal is that as its voltage increases, it indicates increased muscle activation (assuming minimal interference from adjacent muscles). This refers to the activation of additional motor units to augment force generation and/or adjustments in the firing rates of motor units (Reaz et al., 2006). Notably, examining the frequency domain characteristics of the EMG signal offers insights into the variations in its frequency components, which are reflected as changes in the power spectral density. Changes in the mean or median frequency of the EMG power spectral density are commonly utilized to monitor peripheral muscle fatigue (Lowery et al., 2002). This is because muscle fatigue leads to alterations in muscle contraction due to changes in the concentrations of various ions (K⁺, Na⁺, Ca²⁺) and metabolites within the muscle tissue, resulting in changes in fiber conduction velocity (Wan et al., 2017). As muscle conduction velocity decreases (defined as the speed at which the action potential propagates through the muscle fiber), the action potential duration increases, leading to a decrease in frequency content within the action potential (Murakami et al., 2014). To verify this phenomenon through the cycling exercise, besides monitoring cortisol levels, we captured the EMG signal at the initiation of the cycling exercise (Fig. 6d) and near the end of a 55 min-long exercise session, as depicted in Fig. 6e. While significant changes may not be readily apparent in these time domain signals, differences are discernible in the frequency domain. The power spectral density of the EMG signal recorded at the onset of the cycling exercise exhibits sharp power response, whereas the EMG signal obtained at the conclusion of the exercise session displays a broader power response. This suggests that at the beginning of the exercise, there are few dominant frequency components (Fig. 6f), whereas at the end, numerous dominant frequency components are present (Fig. 6g). Thus, analyzing the power spectral density of the EMG signal provides deeper insights into whether the signal was recorded at the commencement (less stressed condition) or the conclusion (heightened stress condition) of the cycling exercise. We also compared the power spectral density of the EMG signal measured with our sensor versus the commercial Sparkfuns_myoware 2.0 electrode (Supporting Information Figure S11b). An excellent correlation was observed between the LIG-measured and myoware-measured power spectral density values with a Pearson correlation coefficient of 0.99.

Statistical Analysis

Statistical analysis using the t-test was conducted to determine the statistically significant differences (represented by p values) in measured cortisol levels or EMG signals during the cycling exercise depicted in Fig. 4j-k. Figure 7a shows the dynamic changes in the cortisol level, while Fig. 7b shows the power spectral density of the EMG signal monitored over 55 minutes during a cycling exercise session with consistent workload. Results plotted in Fig. 7c indicate a highly statistically significant difference between cortisol levels measured at the beginning of the exercise session (denoted by i = 5 min) and at a later stage (denoted by ii = 40 min), with p = 0.0071 (i.e., < 0.01). Similarly, cortisol levels measured at 40 min (denoted by ii) and at the end of the exercise session (denoted by iii = 60 min) demonstrate a statistically significant difference, with p = 0.0271 (i.e., < 0.05). There was a moderate statistically significant difference in the power spectral density of the EMG signal measured at the beginning (denoted by i = 10 min) and the middle (denoted by ii = 30 min) of the exercise session, with p = 0.0454 (Fig. 7d). Likewise, the power spectral density of the EMG signal measured at 30 min (denoted by ii) and at the end of the exercise session (denoted by iii = 50 min) shows a moderate statistically significant difference, with p = 0.0471. These statistically significant results confirm the correlation of cortisol and EMG signals with the stress levels during an exercise session.

In this study, we've showcased a skin-conforming wearable physiochemical sensor suite adept at concurrently tracking the EMG signal, sweat cortisol and pH levels, along with skin temperature. This is achieved by tackling crucial engineering hurdles in physiochemical sensors, specifically addressing crosstalk between EMG and electrochemical sensors by increasing their spatial separation. The mechanical robustness of the developed sensor suite is verified through both horizontal and vertical bending tests, while temperature and humidity corrections ensure the accuracy of the sensor readings. Future advancements in this work will focus on developing flexible electronic circuits powered by energy harvesting, coupled with machine learning-assisted signal processing to gain deeper insights into monitoring human physiology. By integrating these remarkable capabilities into the sensor suite and integrating additional sensors, we envision a fully integrated, versatile multiplexed wearable sweat sensing platform that can provide valuable insights into various aspects of human health. We believe this device is among the pioneering technologies that integrate physiological and electrochemical sensing modalities, paving the way for comprehensive monitoring of human physiological parameters and ultimately advancing telehealth.

Author Contribution

S.T. conceptualized and supervised the project. N.H. developed the sensors, conducted the experiments, analyzed the results, and prepared the figures. T.N. contributed to cortisol sensor development and characterizations. S.T. and N.H. contributed to writing and reviewing the manuscript.

Acknowledgement

This work was supported in part by the University of Texas System’s Rising STARs grant and in part by the VentureWell Grant 21716-20.

Data Availability

The datasets generated and/or analyzed during this study may be available from the corresponding author upon reasonable request.

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Submission checks completed at journal
07 Aug, 2024
First submitted to journal
29 Jul, 2024

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StressFit: A Hybrid Wearable Physicochemical Sensor Suite for Simultaneously Measuring Electromyogram and Sweat Cortisol

Status:

Version 1

Abstract

Figures

Introduction

The StressFIT Platform

Materials and Methods

Electrode Fabrication

Characterization of LIG Electrodes

LIG Functionalization for pH Sensing

LIG Functionalization for Temperature Sensing

LIG Functionalization for Cortisol Sensing

Preparation of Artificial Sweat Sample

Characterization of Cortisol Sensor

Characterization of EMG Sensor

Data Acquisition and Processing Circuit

Human Subjects Participation

Results

Circadian Rhythm Analysis

Continuous Stress Analysis

Crosstalk Analysis

EMG Signal Interpretation

Statistical Analysis

Discussion

Declarations

Author Contribution

Acknowledgement

Data Availability

References

Additional Declarations

Supplementary Files

Status:

Version 1