Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure

Abstract

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

Keywords: activity monitoring; galvanometric response; heart rate; personalized medicine; skin temperature; telehealth; wearable physiological sensors.

Figure 1

(A) The overall system diagram includes four commercial sensors integrated together using a commercial microcontroller; (B) The system components and PCBs are arranged prior to packaging; (C) The components are shown in the packaged armband; and (D) The final wearable device is shown.

Figure 2

Single-sided amplitude spectrum from the Fast Fourier Transform of a single wavelength from a representative photoplethysmogram. Peak frequency occurs at a frequency of 1.342 Hz.

Figure 3

(A) The pulse oximetry sensor outputs a representative raw plethysmography waveform that shows the output from both infrared and red wavelengths. Magnified views of (B) infrared and (C) red are presented to show smaller scale features.

Figure 4

Linear regression models to compare the mean commercial sensor pulse oximetry rate to mean heart rate calculated using (A) red waveforms and (B) infrared waveforms from multiple test subjects. 95% confidence intervals are plotted for each regression model.

Figure 5

(A) Mean surrogate core body temperature is plotted against mean skin temperature from multiple subjects. (B) Raw skin temperature is biased, demonstrating good coincidence with surrogate core body temperature measurements.

Figure 6

Representative triaxial accelerometry representing intensity levels: (A) level 1 sitting, (B) level 2 walking, (C) level 3 climbing stairs, (D) level 4 jogging, (E) level 5 sprinting.

Figure 7

Representative galvanic skin response shows an increase in skin conductance as a result of perspiration during a jogging period.