Sensing and computing for smart healthcare

Abstract

The emerging technology and innovation on sensing technology, data computing, and artificial intelligence (AI) has resulted in an accelerated development of smart healthcare. This thematic issue on Sensing and Computing for Smart Healthcare aims to highlight the diverse advances and the latest developments and emergent technologies in healthcare applications concerning remote human health monitoring, physiological sensing and imaging, wear-able biosensors, intelligent computing and AI. The thematic issue attracted a good number of submissions from researchers in these domains. After critical peer-review and selection, four manuscripts were accepted for publica-tion in this thematic issue, covering the topics of image analysis and AI, physiological signal processing and disease detection, and ambient assisted living. The paper “ Ambient assisted living framework for elderly care using internet of medical things, smart sensors, and GRU deep learning techniques ” by Syed et al. proposes an Ambient Assisted Living (AAL) system with Internet of Medical Things (IoMT) that leverages deep learning techniques to monitor and evaluate the elderly’s activities and vital signs for clinical decision support. By combining smart sensors (including accelerome-ters, gyroscopes, and magnetometers), IoMT infrastructure, and AI algorithms, elderly activities can be recognized and their heart rate variability over time can be monitored. The proposed AAL system is expected to be beneficial during crucial situations such as the pandemics to remotely monitor elderly patients and their health-related status or risks. The paper “ Predicting dose-volume histogram of organ-at-risk using spatial geometric-encoding network for esophageal treatment planning ” by Nian et al. proposes a spatial geometric-encoding