scholarly journals Comparison Analysis of Different Time-Scale Heart Rate Variability Signals for Mental Workload Assessment in Human-Robot Interaction

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Yawei Li ◽  
Chunhe Song ◽  
Yihan Jiang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Time Scale ◽  
Mental Workload ◽  
Human Robot Interaction ◽  
Machine Learning Algorithms ◽  
Robot Interaction ◽  
Ecg Signals ◽  
Different Time Scales

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.

Impact of trajectory profiles on user stress in close human-robot interaction

2018 ◽  
Vol 66 (6) ◽  
pp. 483-491
Author(s):  
Barbara Kühnlenz ◽  
Maximilian Erhart ◽  
Marcel Kainert ◽  
Zhi-Qiao Wang ◽  
Julian Wilm ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
User Study ◽  
Industrial Robot ◽  
Skin Resistance ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
The Impact ◽  
Average Skin

Abstract The impact of different trajectory embodiments in terms of velocity profiles on users’ mental stress in close human-robot interaction is investigated. A cooperative assembly scenario is chosen using a standard industrial robot. Conditions are implemented in a repeated measures within-subjects design comparing linear with trapezoidal trajectories. Heart rate variability and galvanic skin conductance are chosen as objective stress markers and evaluated using the average standard deviation of the beat-to-beat intervals (SDNN) and the average skin resistance. Additionally, evaluations of user experience and acceptance are conducted based on evaluated subjective measures. The results of the user study reveal a significant increase of average heart rate variability and average skin resistance in the trapezoidal condition indicating a reduced mental stress level independent of demographical and dispositional factors.

scholarly journals Research of HRV as a Measure of Mental Workload in Human and Dual-Arm Robot Interaction

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2174
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Yongliang Wang ◽  
Yun Su ◽  
Chunhe Song ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Mental Workload ◽  
Well Being ◽  
Human Robot Interaction ◽  
Direct Perception ◽  
Robot Interaction ◽  
Ecg Signals ◽  
Average Classification Accuracy ◽  
Dual Arm Robot ◽  
Dual Arm

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.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

scholarly journals Nonlinearities of heart rate variability in animal models of impaired cardiac control: contribution of different time scales

2017 ◽  
Vol 123 (2) ◽  
pp. 344-351 ◽  
Author(s):  
Luiz Eduardo Virgilio Silva ◽  
Renata Maria Lataro ◽  
Jaci Airton Castania ◽  
Carlos Alberto Aguiar Silva ◽  
Helio Cesar Salgado ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiscale Entropy ◽  
Time Frequency ◽  
Cardiac Control ◽  
Long Time ◽  
Nonlinear Features ◽  
Short Time

Heart rate variability (HRV) has been extensively explored by traditional linear approaches (e.g., spectral analysis); however, several studies have pointed to the presence of nonlinear features in HRV, suggesting that linear tools might fail to account for the complexity of the HRV dynamics. Even though the prevalent notion is that HRV is nonlinear, the actual presence of nonlinear features is rarely verified. In this study, the presence of nonlinear dynamics was checked as a function of time scales in three experimental models of rats with different impairment of the cardiac control: namely, rats with heart failure (HF), spontaneously hypertensive rats (SHRs), and sinoaortic denervated (SAD) rats. Multiscale entropy (MSE) and refined MSE (RMSE) were chosen as the discriminating statistic for the surrogate test utilized to detect nonlinearity. Nonlinear dynamics is less present in HF animals at both short and long time scales compared with controls. A similar finding was found in SHR only at short time scales. SAD increased the presence of nonlinear dynamics exclusively at short time scales. Those findings suggest that a working baroreflex contributes to linearize HRV and to reduce the likelihood to observe nonlinear components of the cardiac control at short time scales. In addition, an increased sympathetic modulation seems to be a source of nonlinear dynamics at long time scales. Testing nonlinear dynamics as a function of the time scales can provide a characterization of the cardiac control complementary to more traditional markers in time, frequency, and information domains. NEW & NOTEWORTHY Although heart rate variability (HRV) dynamics is widely assumed to be nonlinear, nonlinearity tests are rarely used to check this hypothesis. By adopting multiscale entropy (MSE) and refined MSE (RMSE) as the discriminating statistic for the nonlinearity test, we show that nonlinear dynamics varies with time scale and the type of cardiac dysfunction. Moreover, as complexity metrics and nonlinearities provide complementary information, we strongly recommend using the test for nonlinearity as an additional index to characterize HRV.

scholarly journals Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task

2017 ◽  
pp. 69-72 ◽  
Author(s):  
Rossana Castaldo ◽  
Luis Montesinos ◽  
Tim S. Wan ◽  
Andra Serban ◽  
Sebastiano Massaro ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Mental Workload ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
And Performance

scholarly journals Stationarity Testing of Accumulated Ethernet Traffic

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhiping Lu ◽  
Ming Li ◽  
Wei Zhao
Keyword(s):  
Time Scales ◽  
Unit Root ◽  
Time Scale ◽  
Unit Root Tests ◽  
Quantitative Results ◽  
Different Time Scales

We investigate the stationarity property of the accumulated Ethernet traffic series. We applied several widely used stationarity and unit root tests, such as Dickey-Fuller test and its augmented version, Phillips-Perron test, as well as the Kwiatkowski-Phillips-Schmidt-Shin test and some of its generalizations, to the assessment of the stationarity of the traffic traces at the different time scales. The quantitative results in this research provide evidence that when the time scale increases, the accumulated traffic series are more stationary.

scholarly journals Multiple Time Scales Analysis for Identifying Congestive Heart Failure Based on Heart Rate Variability

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17862-17871 ◽  
Author(s):  
Baiyang Hu ◽  
Shoushui Wei ◽  
Dingwen Wei ◽  
Lina Zhao ◽  
Guohun Zhu ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time
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