The increasing life expectancy of an aging population has caused a higher demand for healthcare services, highlighting a shortage within the healthcare system due to insufficient projected growth of healthcare personnel. One solution to this issue is the utilization of the Internet of Medical Things (IoMT). This study aims to describe the general acceptance level amongst healthcare recipients of adopting IoMT in Swedish remote healthcare and how the acceptance level differs amongst different age groups depending on the application of IoMT. Acceptance and implementation of IoMT in remote healthcare are discussed, with an emphasis on factors such as user-friendliness and practical hardware design. In this study, a digital survey was conducted with 103 respondents, which demonstrated that the majority were positive about using IoMT for monitoring and treating health conditions remotely. Although awareness of security risks exists, respondents generally expressed a low number of concerns regarding unauthorized access to information and modification of IoMT devices in general. The study's conclusion highlights the notable acceptance of IoMT in remote healthcare in Sweden. The results indicate network-connected devices are integrated into people's daily lives, and user-friendliness and cybersecurity influence the adoption of IoMT. Older age groups demonstrated less concern about cybersecurity than younger age groups. When analysing specific applications, the results show some age-related differences in acceptance, which also varied depending on the application, particularly when comparing more life-critical devices such as IoMT pacemakers with IoMT fall detectors. / Den ökande livslängden hos en ökande befolkning har skapat ett ökat vårdbehov och detta har påvisat en brist inom sjukvården då den förväntade utvecklingen av sjukvårdspersonal är otillräcklig. En lösning på detta är användandet av Internet of Medical Things (IoMT) och denna studie har som syfte att beskriva den allmänna acceptansnivån hos vårdtagare för användandet av IoMT inom den svenska distansvården samt hur acceptansnivån skiljer sig för olika åldrar beroende på tillämpning av IoMT. Acceptans och implementering av IoMT i distanssjukvården diskuteras, och faktorer som användarvänlighet och praktisk hårdvarudesign betonas. I denna studie utfördes en digital enkätundersökning innehållande 103 respondenter som visade att majoriteten var positiva till att använda IoMT för att övervaka och behandla hälsotillstånd på distans. Även om medvetenheten om säkerhetsrisker finns, var respondenternas oro för obehörig åtkomst till information samt modifiering av IoMT-utrustning generellt låg. Studiens slutsats framhäver att distansvård med IoMT har en hög acceptans i Sverige. Resultaten indikerar att nätverksanslutna enheter är integrerade i människors vardag och att användarvänlighet samt cybersäkerhet påverkar användningen av IoMT. Äldre åldersgrupper visade mindre oro för cybersäkerheten än yngre åldersgrupper. Vid analys av specifika tillämpningar visar resultaten att det finns vissa åldersrelaterade skillnader i acceptans, vilka också varierade beroende på tillämpning, särskilt i jämförelse med mer livskritiska enheter såsom IoMT-pacemakers i jämförelse med IoMT-falldetektorer.

This research was about the continuous intention of healthcare professionals to use internet of medical things (IoMT) embedded with artificial intelligence (AI). IoMT and AI are evolving innovations and diffusing at the same time. It was not known in what way the two complex technologies diffusing concurrently could influence continuous intention to use IoMT. In addition, behavioural aspects namely motivation and training to use IoMT have been argued to intervene in the relationship between an AI based IoMT and continuous intention to use IoMT. Diffusion of Innovation theory was applied to explain the relationship between diffusion factors that aid the diffusion of AI based IoMT and continuous intention to use IoMT. The five factors relative advantage, compatibility, complexity, observability and trialability were chosen as determinants of continuous intention to use IoMT using DoI theory. Self-determination theory and theory of planned behaviour were used to introduce the interventions in the relationship between diffusion factors and continuous intention to use IoMT. UTAUT was used to explain the influence of the moderators artificial intelligence awareness, novelty seeking behaviour and age of healthcare professionals. The central issue investigated was the determinants of continuous intention of healthcare professionals to use IoMT with behavioural attributes of motivation and training conceived as mediators of the relationship between diffusion factors and continuous intention to use IoMT in the presence of moderators. Quantitative research methodology was used to test the research model developed to understand the relationship between the five diffusion of innovation theory factors and continuous intention to use IoMT when AI based IoMT is still diffusing. The concurrent diffusion of two new technologies was investigated using a research model that was developed for studying the healthcare professionals and their intention. The research was conducted in Bahrain in the healthcare sector. A sample of 354 healthcare professionals participated in the research. Structural equation modelling was used to analyse the data and test the hypothesis. The research showed that healthcare professionals will continue to use concurrently diffusing technologies depending on the relative advantage, complexity and compatibility of the innovations that diffuse. In addition, the results show that healthcare professionals will be motivated by the compatibility of AI-based IoMT if they have to continuously use IoMT. Furthermore, training enables both the organization and the healthcare professionals to overcome dilemma in case they have to continue to use an innovation during its diffusion or when new innovation surface in the market. Finally, artificial intelligence awareness is able to moderate the relationship between relative advantage, complexity and training to use IoMT. Thus, this research contributes to the discipline of behavioural intention of healthcare professionals in determining the influence of an artificial intelligence based IoMT on continuous intention to use IoMT when artificial intelligence embedded in IoMT diffuses concurrently with IoMT. Where IoMT diffusion factors can be used as a determine of continuous intention to use IoMT, artificial intelligence could be understood as a moderator of the relationship between diffusion factors and training to use IoMT, thus demonstrating the combined diffusion of the two technologies diffusing concurrently.

No / This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the under- standing of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.

This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person's stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. This research proposes a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects in the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments, with iLog Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. SaYoPillow has 96% accuracy which is close to other existing research works. This research can not only help in keeping an individual self-aware by providing immediate feedback to change the lifestyle of the person in order to lead a healthier life, but can also play a significant role in the state-of-the-art by allowing computing on the edge devices.