Key result
Mental workload classification using heart rate variability signals achieved an average accuracy of 98.77% with K-Nearest Neighbor and 80.56% across subjects with Gentle Boost.
Why the study?
Mental workload increases with indirect perception during robot operation, making workload assessment important to avoid serious accidents caused by mental overloading.
Heart rate variability (HRV) signals can effectively classify mental workload during human-robot interaction, which could help prevent accidents caused by mental overloading.
May aid safer robot teleoperation via workload classification; leaves open real-world validation before operational use.
Robots instead of humans work in unstructured environments, expanding the scope of human work. The interactions between humans and robots are indirect through operating terminals. The mental workloads of human increase with the lack of direct perception to the real scenes. Thus, mental workload assessment is important, which could effectively avoid serious accidents caused by mental overloading. In this paper, the operating object is a dual-arm robot. The classification of operator’s mental workload is studied by using the heart rate variability (HRV) signal. First, two kinds of electrocardiogram (ECG) signals are collected from six subjects who performed tasks or maintained a relaxed state. Then, HRV data is obtained from ECG signals and 20 kinds of HRV features are extracted. Last, six different classifications are used for mental workload classification. Using each subject’s HRV signal to train the model, the subject’s mental workload is classified. Average classification accuracy of 98.77% is obtained using the K-Nearest Neighbor (KNN) method. By using the HRV signal of five subjects for training and that of one subject for testing with the Gentle Boost (GB) method, the highest average classification accuracy (80.56%) is obtained. This study has implications for the analysis of HRV signals characteristic of mental workload in different subjects, which could improve operators’ well-being and safety in the human-robot interaction process.
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Shao et al. (2020) studied Mental workload (n=6). Task performance (mental workload) vs. Relaxed state was evaluated on Mental workload classification accuracy. Mental workload classification using heart rate variability signals achieved an average accuracy of 98.77% with K-Nearest Neighbor and 80.56% across subjects with Gentle Boost.
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