Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices
Zanetti, Renato ; Arza, Adriana ; Aminifar, Amir LU
and Atienza, David
(2022)
In IEEE Transactions on Biomedical Engineering
69(1).
p.265-277
- Abstract
Objective: Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operators cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. Methods: Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement... (More)
Objective: Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operators cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. Methods: Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. Results: We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. Conclusion: We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. Significance: The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.
(Less)
- author
- Zanetti, Renato
; Arza, Adriana
; Aminifar, Amir
LU
and Atienza, David
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Biomedical monitoring, Cognitive Workload Monitoring, Data processing, Edge Computing, EEG, Electroencephalography, Feature extraction, HumanMachine Interaction, Monitoring, Random access memory, Task analysis, Wearable Devices
- in
- IEEE Transactions on Biomedical Engineering
- volume
- 69
- issue
- 1
- pages
- 265 - 277
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- pmid:34166183
- scopus:85112407719
- ISSN
- 0018-9294
- DOI
- 10.1109/TBME.2021.3092206
- language
- English
- LU publication?
- yes
- id
- d89c40bc-5f74-4218-94a2-5e8ac2f33f23
- date added to LUP
- 2021-09-17 11:05:55
- date last changed
- 2024-04-20 11:23:01
@article{d89c40bc-5f74-4218-94a2-5e8ac2f33f23,
abstract = {{<p>Objective: Cognitive workload monitoring (CWM) can enhance human-machine interaction by supporting task execution assistance considering the operators cognitive state. Therefore, we propose a machine learning design methodology and a data processing strategy to enable CWM on resource-constrained wearable devices. Methods: Our CWM solution is built upon edge computing on a simple wearable system, with only four peripheral channels of electroencephalography (EEG). We assess our solution on experimental data from 24 volunteers. Moreover, to overcome the system's memory constraints, we adopt an optimization strategy for model size reduction and a multi-batch data processing scheme for optimizing RAM memory footprint. Finally, we implement our data processing strategy on a state-of-the-art wearable platform and assess its execution and system battery life. Results: We achieve an accuracy of 74.5% and a 74.0% geometric mean between sensitivity and specificity for CWM classification on unseen data. Besides, the proposed model optimization strategy generates a 27.5x smaller model compared to the one generated with default parameters, and the multi-batch data processing scheme reduces RAM memory footprint by 14x compared to a single batch data processing. Finally, our algorithm uses only 1.28% of the available processing time, thus, allowing our system to achieve 28.5 hours of battery life. Conclusion: We provide a reliable and optimized CWM solution using wearable devices, enabling more than a day of operation on a single battery charge. Significance: The proposed methodology enables real-time data processing on resource-constrained devices and supports real-time wearable monitoring based on EEG for applications as CWM in human-machine interaction.</p>}},
author = {{Zanetti, Renato and Arza, Adriana and Aminifar, Amir and Atienza, David}},
issn = {{0018-9294}},
keywords = {{Biomedical monitoring; Cognitive Workload Monitoring; Data processing; Edge Computing; EEG; Electroencephalography; Feature extraction; HumanMachine Interaction; Monitoring; Random access memory; Task analysis; Wearable Devices}},
language = {{eng}},
number = {{1}},
pages = {{265--277}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Engineering}},
title = {{Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices}},
url = {{http://dx.doi.org/10.1109/TBME.2021.3092206}},
doi = {{10.1109/TBME.2021.3092206}},
volume = {{69}},
year = {{2022}},
}