Stress Assessment for Work Proficiency Analysis by Heart Rate Variability This paper analyzed the electrocardiograms obtained from workers with different proficient levels and considered the stress index. As an example of a simple work, we analyzed the process of combining three cases (Case combination) and the step of inserting nine parts (DIP insertion) into the foundation. We have classified the subjects as beginners and experienced groups with different levels of proficiency, and performed frequency analysis on electrocardiograms measured during each process. Following that we calculated the heart beat interval time RR interval (RRI) from the measurement result and calculated lowfrequency (LF) and highfrequency (HF) by PSD estimation. Moreover, we calculated the ratio LF HF of sympathetic activity (LF) and parasympathetic activity (HF), and compared it with those of beginners and experts. As a result, we confirmed that the value of LFHF during work based on beginneru0027s resting time was larger than that of experienced person.

Stress Recognition with State Classification Considering Temporal Variation of Stress Responses To avoid and manage stressrelated problems, several works have investigated how to recognize stress states of people by exploiting the machine learning. Most of works utilize the supervised learning which requires labels for data to be trained and tested. Thus, labels which represent stress state of each data such as stressed or not become definitely important. Conventional stress state classification methods assign labels to unit data per a certain experimental period by applying stressor appearances or selfevaluation scores. However, those methods ignore temporal variations in stress responses which are involuntarily triggered inside the body within a shorter period of time than an experimental period. Therefore, we propose a stress state classification method by considering not only useru0027s subjective evaluations but also temporal changes of stress responses in short periods. For the demonstration, we label our experimental data of 40 subjects by using our proposed classification method and conventional ones, respectively. Then, we train and test stress recognition models with 6 machine learning algorithms and our implemented neural network ones based on the labeled data. Finally, binary stress recognition with our proposed classification method improves the recognition accuracy by up to 31.6% as compared to those with conventional techniques.