研究目的
To investigate the use of green and orange illuminations from a multi-wavelength optoelectronic patch sensor (mOEPS) to improve robustness to motion artefacts in the extraction of oxygen saturation (SpO2) measurements during physical movements.
研究成果
The green and orange illuminations of mOEPS effectively extracted SpO2 with high correlation (r=0.98) and no significant difference (p=0.88) compared to standard pulse oximetry and other wavelengths, demonstrating robustness to motion during physical activities. This approach offers a cost-effective solution for wearable physiological monitoring without restricting movement, with potential applications in sports, healthcare, and clinical settings. Future work should focus on compliance with ISO and FDA standards and integration into wearable devices.
研究不足
The study was conducted on a limited number of healthy subjects (31) with specific demographics; results may not generalize to other populations. Motion artefacts from sweaty skin caused some outliers. The mOEPS requires further validation in clinical settings and against gold standards like blood gas analyzers. Packaging and automated adaption for different skin types need improvement.
1：Experimental Design and Method Selection:
The study used a multi-wavelength optoelectronic patch sensor (mOEPS) with green (525 nm), orange (595 nm), red (650 nm), and near-infrared (870 nm) illuminations. Signals were captured in reflectance mode, processed using Lambert-Beer's law and ratio of ratios method to extract SpO2. A band-pass filter (0.8–5 Hz) and sampling at 256 Hz were applied to reduce noise.
2：A band-pass filter (8–5 Hz) and sampling at 256 Hz were applied to reduce noise.
Sample Selection and Data Sources:
2. Sample Selection and Data Sources: 31 healthy subjects (male, age 25 ± 5 years, height 179 cm ± 4 cm, BMI 22 ± 1 kg/m2) were recruited. Data were divided into two sub-protocols: 15 subjects at rest with free hand movement, and 16 subjects during cycling (20 rpm) and walking (5 km/h) exercises.
3：List of Experimental Equipment and Materials:
mOEPS sensor, TempIRTM pulse oximeter (Shenzhen Jumper Medical Equipment Co., Ltd.), 4-channel PPG board (DISCO4, Dialog Devices Ltd.), data acquisition board (USB-6009, National Instruments Co.), MATLAB R2016b (MathWorks), LabVIEW GUI (National Instruments Co.), microcontroller for LED control.
4：Experimental Procedures and Operational Workflow:
Subjects abstained from alcohol and caffeine 24 hours prior. Sensors were attached to the palm; data were recorded for 3 minutes during rest and 4 minutes per activity. SpO2 was calculated over 10-second windows. Signals were amplified, demultiplexed, and processed offline.
5：Data Analysis Methods:
Data were analyzed using MATLAB for correlation (r), t-tests, Bland-Altman plots, and box-and-whisker plots to compare SpO2 readings between mOEPS and reference devices.
独家科研数据包，助您复现前沿成果，加速创新突破
获取完整内容
