New Internet of Medical Things for Home-Based Treatment of Anorectal Disorders

Home-based healthcare provides a viable and cost-effective method of delivery for resource- and labour-intensive therapies, such as rehabilitation therapies, including anorectal biofeedback. However, existing systems for home anorectal biofeedback are not able to monitor patient compliance or assess the quality of exercises performed, and as a result have yet to see wide spread clinical adoption. In this paper, we propose a new Internet of Medical Things (IoMT) system to provide home-based biofeedback therapy, facilitating remote monitoring by the physician. We discuss our user-centric design process and the proposed architecture, including a new sensing probe, mobile app, and cloud-based web application. A case study involving biofeedback training exercises was performed. Data from the IoMT was compared against the clinical standard, high-definition anorectal manometry. We demonstrated the feasibility of our proposed IoMT in providing anorectal pressure profiles equivalent to clinical manometry and its application for home-based anorectal biofeedback therapy.

Innovations in Internet of Medical Things, Artificial Intelligence and Readiness of Health Care Sector towards Health 4.0 Adoption

Recent years witnessed lots of advancements in Internet of Medical Things, Innovations in Artificial Intelligence and Big Data Analytics based health care practices. Further, recent pandemic has compelled health care institutions to adopt remote patient care practices throughout the world and India is not an exception. Growth in mobile infrastructure and cheap mobile data packages also encouraged adoption of telemedicine and m-health care practices in India which eventually supports attempts of health care policy makers for transition of traditional health care systems to Health 4.0 in the lines of industry4.0. However, success of Health 4.0 depends upon the coordinated efforts from all the stakeholders. In this regard this research has been conducted to investigate the current status of Health 4.0 implementation in India and readiness of Indian health care sector towards its adoption. This paper further employs the SWOT-AHP analysis to identify the current areas that need immediate improvement to facilitate Health 4.0 adoption.

Ambient assisted living framework for elderly care using Internet of medical things, smart sensors, and GRU deep learning techniques

Due to the increase in the global aging population and its associated age-related challenges, various cognitive, physical, and social problems can arise in older adults, such as reduced walking speed, mobility, falls, fatigue, difficulties in performing daily activities, memory-related and social isolation issues. In turn, there is a need for continuous supervision, intervention, assistance, and care for elderly people for active and healthy aging. This research proposes an ambient assisted living system with the Internet of Medical Things that leverages deep learning techniques to monitor and evaluate the elderly activities and vital signs for clinical decision support. The novelty of the proposed approach is that bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques with mutual information-based feature selection technique is applied to select robust features to identify the target activities and abnormalities. Experiments were conducted on two datasets (the recorded Ambient Assisted Living data and MHealth benchmark data) with bidirectional Gated Recurrent Unit, and Gated Recurrent Unit deep learning techniques and compared with other state of art techniques. Different evaluation metrics were used to assess the performance, findings reveal that bidirectional Gated Recurrent Unit deep learning techniques outperform other state of art approaches with an accuracy of 98.14% for Ambient Assisted Living data, and 99.26% for MHealth data using the proposed approach.