The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.

This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability (HRV) were acquired in an experiment from 53 participants when six basic emotion states were induced. Two RSP parameters were acquired from a chest-band respiration sensor, and five HRV parameters were acquired from a finger-clip blood volume pulse (BVP) sensor. A newly designed deep-learning model based on a convolutional neural network (CNN) was adopted for detecting the identification accuracy of individual emotions. Additionally, the signal combination of the acquired parameters was proposed to obtain high classification accuracy. Furthermore, a dominant factor influencing the accuracy was found by comparing the relativeness of the parameters, providing a basis for supporting the results of emotion classification. The users of this proposed model will soon be able to improve the emotion recognition model further based on CNN using multimodal physiological signals and their sensors.

Song SH, et al. Healthc Inform Res. 2017 Oct;23(4):285-292. https://doi.org/10.4258/hir.2017.23.4.285

Choi EJ, et al. Healthc Inform Res. 2018 Oct;24(4):309-316. https://doi.org/10.4258/hir.2018.24.4.309

This study aims to classify cybersickness (CS) caused by virtual reality (VR) immersion through a machine–deep–ensemble learning model. The heart rate variability and respiratory signal parameters of 20 subjects were measured, while watching a VR video for ∼5 minutes. After the experiment, the subjects were examined for CS and questioned to determine their CS states. Based on the results, we constructed a machine–deep–ensemble learning model that could identify and classify VR immersion CS among subjects. The ensemble model comprised four stacked machine learning models (support vector machine [SVM], k-nearest neighbor [KNN], random forest, and AdaBoost), which were used to derive prediction data, and then, classified the prediction data using a convolution neural network. This model was a multiclass classification model, allowing us to classify subjects' CS into three states (neutral, non-CS, and CS). The accuracy of SVM, KNN, random forest, and AdaBoost was 94.23 percent, 92.44 percent, 93.20 percent, and 90.33 percent, respectively, and the ensemble model could classify the three states with an accuracy of 96.48 percent. This implied that the ensemble model has a higher classification performance than when each model is used individually. Our results confirm that CS caused by VR immersion can be detected as physiological signal data with high accuracy. Moreover, our proposed model can determine the presence or absence of CS as well as the neutral state. Clinical Trial Registration Number: 20-2021-1.

This study aimed to classify emotion based on facial expression and physiological signals using deep learning and to compare the analyzed results. We asked 53 subjects to make facial expressions, expressing four types of emotion. Next, the emotion-inducing video was watched for 1 min, and the physiological signals were obtained. We defined four emotions as positive and negative emotions and designed three types of deep-learning models that can classify emotions. Each model used facial expressions and physiological signals as inputs, and a model in which these two types of input were applied simultaneously was also constructed. The accuracy of the model was 81.54% when physiological signals were used, 99.9% when facial expressions were used, and 86.2% when both were used. Constructing a deep-learning model with only facial expressions showed good performance. The results of this study confirm that the best approach for classifying emotion is using only facial expressions rather than data from multiple inputs. However, this is an opinion presented only in terms of accuracy without considering the computational cost, and it is suggested that physiological signals and multiple inputs be used according to the situation and research purpose.