Affective analysis of patients in homecare video-assisted telemedicine using computational intelligence

A discriminative multi-modal adaptation neural network model for video action recognition

Research on video-based understanding and learning has attracted widespread interest and has been adopted in various real applications, such as e-healthcare, action recognition, affective computing, to name a few. Amongst them, video-based action recognition is one of the most representative examples. With the advancement of multi-sensory technology, action recognition using multi-modal data has recently drawn wide attention. However, the research community faces new challenges in effectively exploring and utilizing the discriminative and complementary information across different modalities. Although score level fusion approaches have been popularly employed for multi-modal action recognition, they simply add the scores derived separately from different modalities without proper consideration of cross-modality semantics amongst multiple input data sources, invariably causing sub-optimal performance. To address this issue, this paper presents a two-stream heterogeneous network to extract and jointly process complementary features derived from RGB and skeleton modalities, respectively. Then, a discriminative multi-modal adaptation neural network model (DMANNM) is proposed and applied to the heterogeneous network, by integrating statistical machine learning (SML) principles with convolutional neural network (CNN) architecture. In addition, to achieve high recognition accuracy by the generated multi-modal structure, an effective nonlinear classification algorithm is presented in this work. Leveraging the joint strength of SML and CNN architecture, the proposed model forms an adaptive platform for handling datasets of different scales. To demonstrate the effectiveness and the generic nature of the proposed model, we conducted experiments on four popular video-based action recognition datasets with different scales: NTU RGB+D, NTU RGB+D 120, Northwestern-UCLA (N-UCLA), and SYSU. The experimental results show the superiority of the proposed method over state-of-the-art compared.

Enhancing patient acceptance of ICD implantation through structured shared decision making: conversation is key

BACKGROUND

Implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy defibrillators (CRT-D) are lifesaving treatments for patients at risk for sudden cardiac death. Effective physician-patient communication during the shared decision-making process is essential. Electrophysiologist-patient conversations were targeted to obtain objective data on the interaction, understand the conversation framework, and uncover opportunities for improved communication.

METHODS

Individuals previously identified as requiring an ICD/CRT-D but declined implantation were recruited for this four-stage interview and survey-based study. Quantitative analysis of surveys and AI analysis of conversation videos was conducted to evaluate patient participant expectations, analyze feedback about the conversations with study physicians, and gauge willingness for device implantation.

RESULTS

The study included 27 patients (mean age 51 years, 51.9% female) and 9 study physicians. Patients were significantly more willing to undergo ICD/CRT-D implantation after conversing with study physicians compared to their own physicians and pre-conversation surveys (mean scores: 5.0, 3.1, and 4.4 out of 7, respectively; p < 0.001). Patient participants had higher satisfaction with the study conversation, rating study physicians higher in effectiveness of explanations, responsiveness to questions, and overall quality of the conversation compared to their own physicians (all p < 0.001).

CONCLUSIONS

In a cohort of patients who previously declined ICD/CRT-D implantation, patient satisfaction and willingness to undergo implantation of a guideline-directed device therapy increased significantly following a structured conversation with study physicians. Identified key elements could be integrated into user-friendly tools and educational materials to facilitate these conversations, improving patient engagement with the decision-making process and enhancing informed acceptance of indicated device therapies.

Speech Emotion Recognition Based on Modified ReliefF

As the key of human-computer natural interaction, the research of emotion recognition is of great significance to the development of computer intelligence. In view of the issue that the current emotional feature dimension is too high, which affects the classification performance, this paper proposes a modified ReliefF feature selection algorithm to screen out feature subsets with smaller dimensions and better performance from high-dimensional features to further improve the efficiency and accuracy of emotion recognition. In the modified algorithm, the selection range of random samples is adjusted; the correlation between features is measured by the maximum information coefficient, and the distance measurement method between samples is established based on the correlation. The experimental results on the eNTERFACE'05 and SAVEE speech emotional datasets show that the features filtered based on the modified algorithm significantly reduce the data dimensions and effectively improve the accuracy of emotion recognition.

Research on Integration of Emotion Analysis in English Modular Teaching Based on Natural Language Processing

Now, the application of intelligent technologies such as machine learning and deep learning in natural language processing has achieved good results. This article studies the integration of emotion analysis in English module teaching of natural language processing. Vocabulary is a very important part in English teaching. Learning vocabulary well can improve students’ reading ability. However, in the process of students’ learning, vocabulary is the most basic and difficult to learn. Poor vocabulary learning and insufficient accumulation will restrict students’ reading ability. Improving vocabulary teaching mode and learning methods can stimulate students’ interest in learning and effectively improve their reading ability. In the third part of the article, the neural network language model and statistical model are used to analyze the key technologies of natural language processing, and then the Naive Bayes algorithm and support vector machine model algorithm are used to optimize the data. Finally, two classes are selected for comparative experiment, then, by integrating emotional teaching into students’ classroom and analyzing students’ interest, the conclusion is that integrating emotional teaching in teaching can effectively improve students’ academic achievements, and at the same time, integrating emotional teaching in teaching can also stimulate students’ enthusiasm for learning English and effectively change students’ learning attitude.

An IoT-Enabled Platform for the Assessment of Physical and Mental Activities Utilizing Augmented Reality Exergaming

Augmented reality (AR) and Internet of Things (IoT) are among the core technological elements of modern information systems and applications in which advanced features for user interactivity and monitoring are required. These technologies are continuously improving and are available nowadays in all popular programming environments and platforms, allowing for their wide adoption in many different business and research applications. In the fields of healthcare and assisted living, AR is extensively applied in the development of exergames, facilitating the implementation of innovative gamification techniques, while IoT can effectively support the users’ health monitoring aspects. In this work, we present a prototype platform for exergames that combines AR and IoT on commodity mobile devices for the development of serious games in the healthcare domain. The main objective of the solution was to promote the utilization of gamification techniques to boost the users’ physical activities and to assist the regular assessment of their health and cognitive statuses through challenges and quests in the virtual and real world. With the integration of sensors and wearable devices by design, the platform has the capability of real-time monitoring the users’ biosignals and activities during the game, collecting data for each session, which can be analyzed afterwards by healthcare professionals. The solution was validated in real world scenarios and the results were analyzed in order to further improve the performance and usability of the prototype.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.