Biblio

Healthcare has become one of the most important aspects of people’s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient’s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model’s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.

Stress-related disorders are increasing because of work load, forces in teamwork, surroundings pressures and health related conditions. Thus, to avoid people living under heavy stress and develop more severe stress-related disorders, different internet and applications of stress management interventions are offered. Mobile applications with self-assessed health, burnout-scores and well-being are commonly used as outcome measures. Few studies have used sickleave to compare effects of stress interventions. A new approach is to use nature and garden in a multimodal stress management context. This study aimed to explore the effects of immersive and non-immersive games application by using nature theme virtual stress therapy in reducing stress level. Two weeks’ of experiments had involved 18 participants. Nine (9) of them were invited to join the first experiment which focused on immersive virtual reality (VR) experience. Their Blood Volume Pulse with Heart Rate (BVP+HR) and Skin Conductance (SC) were recorded using BioGraph Infiniti Biofeedback System that comes with three (3) sensors attached to the fingers. The second experiment were joined by another nine (9) participants. This experiment was testing on non-immersive desktop control experience. The same protocol measurements were taken which are BVP+HR and SC. Participants were given the experience to feel and get carried into the virtual nature as a therapy so that they will reduce stress. The result of this study points to whether immersive or non-immersive VR display using nature theme virtual therapy would reduce individuals stress level. After conducted series of experiments, results showed that both immersive and non-immersive VR display reduced stress level. However, participants were satisfied of using the immersive version as it provided a 360 degree of viewing, immersed experiences and feeling engaged. Thus, this showed and proved that applications developed with nature theme affect successfully reduce stress level no matter it is put in immersive or non-immersive display.

Intent classification is an important and relevant area of research in artificial intelligence and machine learning, with applications ranging from marketing and product design to intelligent communication. This paper explores the performance of various models and techniques for short text intent classification in the context of chatbots. The problem was explored for use within the mental wellness and therapy chatbot application, Wysa, to give improved responses to free-text user input. The authors looked at classifying text samples in-to 4 categories - assertions, refutations, clarifiers and transitions. For this, the suitability of the following techniques was evaluated: count vectors, TF-IDF, sentence embeddings and n-grams, as well as modifications of the same. Each technique was used to train a number of state-of-the-art classifiers, and the results have been compiled and presented. This is the first documented implementation of Arora's modification to sentence embeddings for real world use. It also introduces a technique to generate custom stop words that gave a significant gain in performance (10 percentage points). The best pipeline, using these techniques together, gave an accuracy of 95 percent.

With the recent advances in computing, artificial intelligence (AI) is quickly becoming a key component in the future of advanced applications. In one application in particular, AI has played a major role - that of revolutionizing traditional healthcare assistance. Using embodied interactive agents, or interactive robots, in healthcare scenarios has emerged as an innovative way to interact with patients. As an essential factor for interpersonal interaction, trust plays a crucial role in establishing and maintaining a patient-agent relationship. In this paper, we discuss a study related to healthcare in which we examine aspects of trust between humans and interactive robots during a therapy intervention in which the agent provides corrective feedback. A total of twenty participants were randomly assigned to receive corrective feedback from either a robotic agent or a human agent. Survey results indicate trust in a therapy intervention coupled with a robotic agent is comparable to that of trust in an intervention coupled with a human agent. Results also show a trend that the agent condition has a medium-sized effect on trust. In addition, we found that participants in the robot therapist condition are 3.5 times likely to have trust involved in their decision than the participants in the human therapist condition. These results indicate that the deployment of interactive robot agents in healthcare scenarios has the potential to maintain quality of health for future generations.

The revolution of smart devices has a significant and positive impact on the lives of many people, especially in regard to elements of healthcare. In part, this revolution is attributed to technological advances that enable individuals to wear and use medical devices to monitor their health activities, but remotely. Also, these smart, wearable medical devices assist health care providers in monitoring their patients remotely, thereby enabling physicians to respond quickly in the event of emergencies. An ancillary advantage is that health care costs will be reduced, another benefit that, when paired with prompt medical treatment, indicates significant advances in the contemporary management of health care. However, the competition among manufacturers of these medical devices creates a complexity of small and smart wearable devices such as ECG and EMG. This complexity results in other issues such as patient security, privacy, confidentiality, and identity theft. In this paper, we discuss the design and implementation of a hybrid real-time cryptography algorithm to secure lightweight wearable medical devices. The proposed system is based on an emerging innovative technology between the genomic encryptions and the deterministic chaos method to provide a quick and secure cryptography algorithm for real-time health monitoring that permits for threats to patient confidentiality to be addressed. The proposed algorithm also considers the limitations of memory and size of the wearable health devices. The experimental results and the encryption analysis indicate that the proposed algorithm provides a high level of security for the remote health monitoring system.