Toward Wearable MagnetoCardioGraphy (MCG) for Cognitive Workload Monitoring: Advancements in Sensor and Study Design

Abstract

Despite cognitive workload (CW) being a critical metric in several applications, no technology exists to seamlessly and reliably quantify CW. Previously, we demonstrated the feasibility of a wearable MagnetoCardioGraphy (MCG) sensor to classify high vs. low CW based on MCG-derived heart rate variability (mHRV). However, our sensor was unable to address certain critical operational requirements, resulting in noisy signals, often to the point of being unusable. In addition, test conditions for the participants were not decoupled from motion (i.e., physical activity (PA)), raising questions as to whether the noted changes in mHRV were attributed to CW, PA, or both. This study reports software and hardware advancements to optimize the MCG data quality, and investigates whether changes in CW (in the absence of PA) can be reliably detected. Performance is validated for healthy adults (n = 10) performing three types of CW tasks (one for low CW and two for high CW to eliminate the memory effect). Results demonstrate the ability to retrieve MCG R-peaks throughout the recordings, as well as the ability to differentiate high vs. low CW in all cases, confirming that CW does modulate the mHRV. A paired Bonferroni t-test with significance α=0.01 confirms the hypothesis that an increase in CW decreases mHRV. Our findings lay the groundwork toward a seamless, practical, and low-cost sensor for monitoring CW.

Keywords: ElectroCardioGraphy (ECG); MagnetoCardioGraphy (MCG); cognitive workload (CW); heart rate variability (HRV).

Figure 1

(a) Proposed MCG sensor with 3-layer 3D-printed fixture, ethernet cables, and straps. (b) Previous MCG sensor with jumper wires kept in place with tape [16].

Figure 2

Exploded 3D view of the MCG sensor design.

Figure 3

(a) Front and (b) back view showing how the ratchet straps hold the MCG sensor in place upon a human subject.

Figure 4

Visualization of the complete MCG sensor setup showing how the ethernet cables connect to the amplifier board, subsequent ADC, and laptop.

Figure 5

Block diagram of beat estimation.

Figure 6

Testing scenarios: (a) Scenario 1, (b) Scenario 2, and (c) Scenario 3.

Figure 7

Experimental setup for data collection with visualization on employed sensors.

Figure 8

A zoom-in on pre-processed ECG (blue) and MCG (red) data obtained through (a) the advanced MCG sensor reported in this paper and (b) the MCG sensor reported in [16].

Figure 9

A zoom-in on post-processed ECG (blue) and MCG (red) data obtained through (a) the advanced MCG sensor reported in this paper and (b) the MCG sensor reported in [16].

Figure 10

IMU data in X and Y axis in units of g (m/s²) for (a) Scenario 1, (b) Scenario 2, and (c) Scenario 3.

Figure 11

Validation results for MCG: this plot shows how the HRV values obtained through our MCG sensor match those obtained from the ECG sensor.

Figure 12

(a) Box plot for MCG for Scenarios 1 vs. 2, (b) box plot for ECG for Scenarios 1 vs.2, (c) box plot for MCG for Scenarios 1 vs. 3, and (d) box plot for ECG for Scenarios 1 vs. 3. The blue line that connects the box plots represents the mean of the calculated HRV in each scenario.