Quantifying Cognitive Workload Using a Non-Contact Magnetocardiography (MCG) Wearable Sensor

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9115
• Main Authors: Wang, Zitong, Zhu, Keren, Kaur, Archana, Recker, Robyn, Yang, Jingzhen, Kiourti, Asimina
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.11.2022; MDPI
• Subjects: Accuracy; Body mass index; Classification; Cognition - physiology; Cognitive tasks; cognitive workload classification; Digital signal processors; Electrocardiography; Electrodes; Electroencephalography; Form factors; Heart; Heart beat; Heart Rate - physiology; heart rate variability; Humans; Magnetic fields; Magnetocardiography; Parameters; Portable computers; Sensors; Signal processing; Signal Processing, Computer-Assisted; Signal quality; Skin; Technology application; wearable and non-shielded sensor; Wearable technology; Workload; Workloads; cognitive workload classification; heart rate variability; magnetocardiography; wearable and non-shielded sensor
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22239115

Quantifying cognitive workload, i.e., the level of mental effort put forth by an individual in response to a cognitive task, is relevant for healthcare, training and gaming applications. However, there is currently no technology available that can readily and reliably quantify the cognitive workload of an individual in a real-world environment at a seamless way and affordable price. In this work, we overcome these limitations and demonstrate the feasibility of a magnetocardiography (MCG) sensor to reliably classify high vs. low cognitive workload while being non-contact, fully passive and low-cost, with the potential to have a wearable form factor. The operating principle relies on measuring the naturally emanated magnetic fields from the heart and subsequently analyzing the heart rate variability (HRV) matrix in three time-domain parameters: standard deviation of RR intervals (SDRR); root mean square of successive differences between heartbeats (RMSSD); and mean values of adjacent R-peaks in the cardiac signals (MeanRR). A total of 13 participants were recruited, two of whom were excluded due to low signal quality. The results show that SDRR and RMSSD achieve a 100% success rate in classifying high vs. low cognitive workload, while MeanRR achieves a 91% success rate. Tests for the same individual yield an intra-subject classification accuracy of 100% for all three HRV parameters. Future studies should leverage machine learning and advanced digital signal processing to achieve automated classification of cognitive workload and reliable operation in a natural environment.