The Internet of Medical Things (IoMT) aims to exploit the Internet-of-Things (IoT) techniques to provide better medical treatment scheme for patients with smart, automatic, timely, and emotion-aware clinical services. One of the IoMT instances is applying IoT techniques to sleep-aware smartphones or wearable devices’ applications to provide better sleep healthcare services. As we all know, sleep is vital to our daily health. What is more, studies have shown a strong relationship between sleep difficulties and various diseases such as COVID-19. Therefore, leveraging IoT techniques to develop a longer lifetime sleep healthcare IoMT system, with a tradeoff between data transferring/processing speed and battery energy efficiency, to provide longer time services for bad sleep condition persons, especially the COVID-19 patients or survivors, is a meaningful research topic. In this study, we propose an IoT-enabled sleep data fusion networks (SDFN) module with a star topology Bluetooth network to fuse data of sleep-aware applications. A machine learning model is built to detect sleep events through an audio signal. We design two data reprocessing mechanisms running on our IoT devices to alleviate the data jam problem and save the IoT devices’ battery energy. The experiments manifest that the presented module and mechanisms can save the energy of the system and alleviate the data jam problem of the device.

Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial & ontology (logical) structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1 K tables in which 20.4 K are in English and 17.7 K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial & logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation.