Electroencephalogram signals emotion recognition based on convolutional neural network-recurrent neural network framework with channel-temporal attention mechanism for older adults.

Reminiscence and conversation between older adults and younger volunteers using past photographs are very effective in improving the emotional state of older adults and alleviating depression. However, we need to evaluate the emotional state of the older adult while conversing on the past photographs. While electroencephalogram (EEG) has a significantly stronger association with emotion than other physiological signals, the challenge is to eliminate muscle artifacts in the EEG during speech as well as to reduce the number of dry electrodes to improve user comfort while maintaining high emotion recognition accuracy. Therefore, we proposed the CTA-CNN-Bi-LSTM emotion recognition framework. EEG signals of eight channels (P3, P4, F3, F4, F7, F8, T7, and T8) were first implemented in the MEMD-CCA method on three brain regions separately (Frontal, Temporal, Parietal) to remove the muscle artifacts then were fed into the Channel-Temporal attention module to get the weights of channels and temporal points most relevant to the positive, negative and neutral emotions to recode the EEG data. A Convolutional Neural Networks (CNNs) module then extracted the spatial information in the new EEG data to obtain the spatial feature maps which were then sequentially inputted into a Bi-LSTM module to learn the bi-directional temporal information for emotion recognition. Finally, we designed four group experiments to demonstrate that the proposed CTA-CNN-Bi-LSTM framework outperforms the previous works. And the highest average recognition accuracy of the positive, negative, and neutral emotions achieved 98.75%.

A Library of Old Photos Supporting Conversation of Two Generations Serving Reminiscence Therapy.

In Japan, a shift in family patterns has led to a sense of social isolation among older people, which increases the risk of major neurocognitive disorder. Interventions for them using old photos to implement reminiscence therapy (RT) have been proved to be effective. A super-aged society has in turn led to a shortage of medical resources and older people prefer home care over institutional care. Therefore, there is an urgent need for volunteers to help in RT. However, the age of volunteers tends to be increasingly younger. The lack of knowledge and experience of the past for the young volunteers makes it difficult for them to select appropriate stimulated materials. To improve this situation, a library of old photos for RT was developed to support conversation between the two generations. A two-factor experiment and emotion assessment scales were designed to explore the effect of different old photo types on the fluency of conversation between the two generations and their emotion. It was found that the types of old photos have little effect on older people and that conversations were almost pleasant. However, the pleasantness of older people was enhanced when using photos that they wanted to talk about (P = 0.006). Meanwhile, pleasure in conversation of the older people increased with the attention of the young people to the topic (R = 0.304, p < 0.001). Conversely, photo type has a strong impact on young people. When photos are selected that older people do not want to talk about or photos that young people do not know the content and are not interested in, concern for the topic of young people drops dramatically. Therefore, when RT, it is important to avoid using the types of photos above that cause a drop in younger people's attention.

Heart sound recognition technology based on convolutional neural network.

The mortality rate of heart disease continues to rise each year: developing mechanisms to reduce mortality from heart disease is a top concern in today's society. Heart sound auscultation is a crucial skill used to detect and diagnose heart disease. In this study, we propose a heart sound signal classification algorithm based on a convolutional neural network. The algorithm is based on heart sound data collected in the clinic and from medical books. The heart sound signals were first preprocessed into a grayscale image of 5 seconds. The training samples were then used to train and optimize the convolutional neural network; obtaining a training result with an accuracy of 95.17% and a loss value of 0.23. Finally, the convolutional neural network was used to test the test set samples. The results showed an accuracy of 94.80%, sensitivity of 94.29%, specificity of 95.54%, precision of 93.44%, F1_score of 93.84%, and an AUC of 0.943. Compared with other algorithms, the accuracy and sensitivity of the algorithms were improved. This shows that the method used in this study can effectively classify heart sound signals and could prove useful in assisting heart sound auscultation.

Emotion Recognition Using Electroencephalography Signals of Older People for Reminiscence Therapy.

Objective: Numerous communication support systems based on reminiscence therapy have been developed. However, when using communication support systems, the emotional assessment of older people is generally conducted using verbal feedback or questionnaires. The purpose of this study is to investigate the feasibility of using Electroencephalography (EEG) signals for automatic emotion recognition during RT for older people. Participants: Eleven older people (mean 71.25, SD 4.66) and seven young people (mean 22.4, SD 1.51) participated in the experiment. Methods: Old public photographs were used as material for reminiscence therapy. The EEG signals of the older people were collected while the older people and young people were talking about the contents of the photos. Since emotions change slowly and responses are characterized by delayed effects in EEG, the depth models LSTM and Bi-LSTM were selected to extract complex emotional features from EEG signals for automatic recognition of emotions. Results: The EEG data of 8 channels were inputted into the LSTM and Bi-LSTM models to classify positive and negative emotions. The recognition highest accuracy rate of the two models were 90.8% and 95.8% respectively. The four-channel EEG data based Bi-LSTM also reached 94.4%. Conclusion: Since the Bi-LSTM model could tap into the influence of "past" and "future" emotional states on the current emotional state in the EEG signal, we found that it can help improve the ability to recognize positive and negative emotions in older people. In particular, it is feasible to use EEG signals without the necessity of multimodal physiological signals for emotion recognition in the communication support systems for reminiscence therapy when using this model.

Translational Acceleration, Rotational Speed, and Joint Angle of Patients Related to Correct/Incorrect Methods of Transfer Skills by Nurses.

Currently, due to shortages in the nursing faculty and low access to actual patients, it is difficult for students to receive feedback from teachers and practice with actual patients to obtain clinic experience. Thus, both evaluation systems and simulated patients have become urgent requirements. Accordingly, this study proposes a method to evaluate the nurse's transfer skill through observation from the patient. After verifying the proposed method, it will be integrated with a robotic patient as a future work. To verify if such an evaluation is practical, a checklist comprising 16 steps with correct and incorrect methods was proposed by the nursing teachers. Further, the evaluation parameters were determined as translational acceleration, rotational speed, and joint angle of patient. Inertial sensors and motion capture were employed to measure the translational acceleration, rotational speed, and joint angle. An experiment was conducted with two nursing teachers, who were asked to carry out both correct and incorrect methods. According to the results, three parameters reveal the difference for a patient under correct/incorrect methods and can further be used to evaluate the nurse's skill once the thresholds are determined. In addition, the applicability of inertial sensors is confirmed for the use of robot development.

Relationship between nursing students' preference for types of teaching materials and learning effects of self-learning tool.

The purpose of this study was to investigate the relationship between learning effects of the self-learning tool for nursing students and types of teaching materials. Ten nursing students were asked to perform transfer a patient from bed to wheelchair after watching the demo video and practicing 20 minutes. The students' performance was evaluated before and after practicing. The students were also asked to choose teaching materials that would be developed in the future. Out of nine teaching materials, the students chose seven of them. Correspondence analysis was conducted between the results of the evaluation of students' transfer technique and their preference of teaching materials. The results indicated that there was no relationship between the preference of teaching materials and the scores of transfer techniques. The authors concluded that the self-learning was not affected by the preference of teaching materials.

Daily assistance for individuals with dementia via videophone.

We previously developed remote reminiscence conversation and schedule prompter systems via the videophone to improve psychological stability and to assist individuals with dementia to perform household tasks. Our results showed that the psychological stability of 1 patient persisted for 3 hours after remote conversations. The task completion rate afforded by the schedule prompter system, which displays a video reminder series automatically, was 52%. In the present study, we also investigated whether psychological stability was sustained in other patients. Furthermore, motivational prompter videos were added to enhance the original schedule prompter system. We found that 1 in 4 patients living at home showed greater stability while conversing with a conversation partner on the videophone than while watching television programs, and that she remained stable for 3 hours after remote conversations. The task completion rate afforded by the revised schedule prompter system was 82%. These 2 remote systems are promising tools for assisting individuals with dementia in their daily lives.

Effectiveness of personalised reminiscence photo videos for individuals with dementia.

Reminiscence intervention is effective for increasing self-esteem and decreasing behavioural disturbances in individuals with dementia. This paper introduces the concept of a "personalised reminiscence photo video" as a convenient method for reminiscence intervention. The video utilises a slideshow video of personal photos with narration, background music, and pan/zoom visual effects. A group of 15 individuals with dementia watched personalised reminiscence photo videos as well as two types of TV shows: a variety show and a news show. Eighty percent of the subjects (12 out of 15) showed more attention to their personalised reminiscence photo video than to the other two types of TV shows, thus suggesting the effectiveness of personalised reminiscence photo videos for reminiscence intervention.

The proposal of measurement of RNs at acute care setting cognitive process when they make cognitive error.

This paper presents the proposal for the design of a measurement of cognitive process of nurses at acute care setting when they make cognitive error. nurses especially at acute care setting perform their numerous nursing tasks simultaneously in time-stressed, information-rich situation. To cope with these severe cognitive demands expertise nurses develop simplification strategies not to consume their mental resources so much. However medical errors happen, regardless of nurses' experience. The resource allocation theory of Norman and Bobrow suggests that inappropriate selection of simplification strategy when the task is needed more attention induce the cognitive error. In this paper too much mental workload is presumed one of the reasons to make inappropriate selection. And the study design to measure cognitive process under the different mental workload situation by talk aloud protocol and psycho-physiological response is proposed.

Features of standardized nursing terminology sets in Japan.

UNLABELLED: This paper reports the features and relationships between standardizes nursing terminology sets used in Japan. First, we analyzed the common parts in five standardized nursing terminology sets: the Japan Nursing Practice Standard Master (JNPSM) that includes the names of nursing activities and is built by the Medical Information Center Development Center (MEDIS-DC); the labels of the Japan Classification of Nursing Practice (JCNP), built by the term advisory committee in the Japan Academy of Nursing Science; the labels of the International Classification for Nursing Practice (ICNP) translated to Japanese; the labels, domain names, and class names of the North American Nursing Diagnosis Association (NANDA) Nursing Diagnoses 2003-2004 translated to Japanese; and the terms included in the labels of Nursing Interventions Classification (NIC) translated to Japanese. Then we compared them with terms in a thesaurus dictionary, the Bunrui Goihyo, that contains general Japanese words and is built by the National Institute for Japanese Language. THE RESULTS: 1) the level of interchangeability between four standardized nursing terminology sets is quite low; 2) abbreviations and katakana words are frequently used to express nursing activities; 3) general Japanese words are usually used to express the status or situation of patients.

Nursing spoken corpora for understanding nursing assignments.

We have built Spoken Corpora using voice data that were obtained by monitored and recorded daily nursing assignments in hospitals. The devices, we have developed are able to monitor daily nursing assignments and record multimedia data, such as video, voice, pedometer, acceleration, and. so on. Focusing on voice data, we have built two types of corpora one for analyzing nursing tasks and the other for collecting nursing terms. This work represents the first trail in Japan to develop such special devices and to make corpora for understanding nursing assignments. In this paper, we report our experiments done in "E-nightingale Project," present an effective task analysis of nursing activities, and describe results of an analysis of nursing terms for developing "Automatic Time Study Survey System."

Networked interaction therapy: relieving stress in memory-impaired people and their family members.

To relieve the stress in the lives of memory-impaired people and their family members, we propose the concept of Networked Interaction Therapy that connects them with community support group members via Internet by utilizing Internet communication, image understanding and sensory-interaction media technologies. To study their need and acceptance for our services, we made short video clips to illustrate our concept, and interviewed the care givers. Based on these studies, we also conducted the experiments with the low-fidelity mock-ups.