Abstract
Excessive mental workload affects human health and may lead to accidents. This study is motivated by the need to assess mental workload in the process of human-robot interaction, in particular, when the robot performs a dangerous task. In this study, the use of heart rate variability (HRV) signals with different time scales in mental workload assessment was analyzed. A humanoid dual-arm robot that can perform dangerous work was used as a human-robot interaction object. Electrocardiogram (ECG) signals of six subjects were collected in two states: during the task and in a relaxed state. Multiple time-scale (1, 3, and 5 min) HRV signals were extracted from ECG signals. Then, we extracted the same linear and nonlinear features from the HRV signals at different time scales. The performance of machine learning algorithms using the different time-scale HRV signals obtained during the human-robot interaction was evaluated. The results show that for the per-subject case with a 3 min HRV signal length, the K -nearest neighbor classifier achieved the best mental workload classification performance. For the cross-subject case with a 5 min time-scale signal length, the gentle boost classifier achieved the best mental workload classification accuracy. This study provides a novel research idea for using HRV signals to measure mental workload during human-robot interaction.
Highlights
Nowadays, robots, instead of humans, work in unstructured environments, expanding the scope of human work
ECG signals were obtained from six subjects while they were performing a task and while staying relaxed
heart rate variability (HRV) signals were extracted based on the ECG signals
Summary
Robots, instead of humans, work in unstructured environments, expanding the scope of human work. Humans interact with robots through visual, tactile, and other feedback [1,2,3,4]. The robot can be operated remotely to complete a dangerous task; this operation can be challenging for humans. Research in the field of robotics primarily focuses on how robots perform human control instructions, how they perceive environmental information, and how autonomous operation can be achieved [5, 6]. This research neglects the robot’s assessment of the human’s psychological activity and the emotions of humans interacting with the robot. It is of great significance to accurately measure the mental workload of the operator during their interaction with the robot [7, 8]
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