Feasibility of Electrodermal Activity and Photoplethysmography Data Acquisition at the Foot Using a Sock Form Factor

Abstract

Wearable devices have been shown to play an important role in disease prevention and health management, through the multimodal acquisition of peripheral biosignals. However, many of these wearables are exposed, limiting their long-term acceptability by some user groups. To overcome this, a wearable smart sock integrating a PPG sensor and an EDA sensor with textile electrodes was developed. Using the smart sock, EDA and PPG measurements at the foot/ankle were performed in test populations of 19 and 15 subjects, respectively. Both measurements were validated by simultaneously recording the same signals with a standard device at the hand. For the EDA measurements, Pearson correlations of up to 0.95 were obtained for the SCL component, and a mean consensus of 69% for peaks detected in the two locations was obtained. As for the PPG measurements, after fine-tuning the automatic detection of systolic peaks, the index finger and ankle, accuracies of 99.46% and 87.85% were obtained, respectively. Moreover, an HR estimation error of 17.40±14.80 Beats-Per-Minute (BPM) was obtained. Overall, the results support the feasibility of this wearable form factor for unobtrusive EDA and PPG monitoring.

Affective gaming using adaptive speed controlled by biofeedback

“…Several methods can be used in order to collect biosignals with diferent devices such as wired electrodes, fnger clips, or wearable devices. Ferreira et al (2023) used a sock form factor for measuring PPG and EDA and the results support the feasibility of sock form factor for unobtrusive EDA and PPG monitoring [10]. Cantento et al (2011) obtained recognition rates for emotions of 81% to distinguish between positive and negative emotion with multimodal biosignal sensor data only [7].…”

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

“…For instance, the BITalino device has been widely used within the research community for biosignal acquisitions [ 35 , 36 , 37 , 38 , 39 , 40 ]. Moreover, the ScientISST CORE [ 41 ] is a novel signal acquisition board especially developed for biomedical applications, and has also been used in similar research contexts [ 22 , 42 , 43 , 44 , 45 ]. For the BITalino, the 10-bit ADC was used, and for the ScientISST CORE, the 23-bit ADC was used.…”

“…These differences were then averaged across all subjects and layouts for each sensor, providing the statistical variability of the HR estimation error within the test population. This variability was characterized by the mean and standard deviation of the differences in mean HRs per sensor [ 22 ].…”

“…Being one of the farthest extremities of the body from the heart, it allows for the collection of peripheral blood perfusion data more accurately, helping detect conditions such as peripheral artery disease or peripheral vascular disease. It can also be easily integrated into undergarments such as socks to allow for constant monitoring without sacrificing comfort [ 22 , 32 , 33 , 34 ].…”