HalleyAssist: A Personalised Internet of Things Technology to Assist the Elderly in Daily Living

Ambient Assisted Living (AAL) research has received extensive attention in recent years. AAL applications combine aspects of Internet of Things (IoT), smart platform design and machine learning to produce an intelligent system. In this paper we describe a personalised IoT-based AAL system that enables an independent and safe life for elderly people within their own home via real-time monitoring and intervention. The system, HalleyAssist underpinned by smart home automation functions includes a novel approach for monitoring the wellbeing and detecting abnormal changes in behavioral patterns of an elderly person. The significance of the approach is in the use of machine learning models to automatically learn normal behavioral pattern for the person from IoT sensor data and using the models derived to detect significant changes in behavioral pattern should they occur. The architecture and developed proof of concept of the proposed system is presented along with discussion of how privacy and security concerns are addressed. We also report on outcomes of real-world in-home trials of an early version of the system where it was installed in four older people\u27s home for a period of six weeks. The response from the older people to the deployed system was very positive. Finally, the paper presents a discussion and an analysis of the results using the data collected during the in-home trials

Using Task Technology Fit Theory to Guide the Codesign of Mobile Clinical Decision Support Systems

A clinical decision support system (CDSS) is designed to assist health professionals in perioperative patient management. Robust CDSSs are vital to deliver enhanced healthcare services. Incorporating the latest advancements in digital technologies, mobile device based CDSSs are being introduced to healthcare settings at a considerable pace. However, given the nascency of this tech-health synergy, well-defined systematic approaches to be followed to design and develop mobile CDSSs to ensure developed technological solutions are of best fit-for-purpose, are lacking. To address this void, this study proposes an approach combining Task Technology Fit theory and Design Science Research Methodology, to guide the design and development of mobile CDSSs. The proposed approach is applied to a case study to design a mobile CDSS to assist perioperative optimization of surgery patients. The learnings from the case study are reported

ExpFinder: An Ensemble Expert Finding Model Integrating NN-gram Vector Space Model and μ\muCO-HITS

Finding an expert plays a crucial role in driving successful collaborations and speeding up high-quality research development and innovations. However, the rapid growth of scientific publications and digital expertise data makes identifying the right experts a challenging problem. Existing approaches for finding experts given a topic can be categorised into information retrieval techniques based on vector space models, document language models, and graph-based models. In this paper, we propose $\textit{ExpFinder}$, a new ensemble model for expert finding, that integrates a novel $N$-gram vector space model, denoted as $n$VSM, and a graph-based model, denoted as \textit{\muCO-HITS}, that is a proposed variation of the CO-HITS algorithm. The key of $n$VSM is to exploit recent inverse document frequency weighting method for $N$-gram words and $\textit{ExpFinder}$ incorporates $n$VSM into \textit{\muCO-HITS} to achieve expert finding. We comprehensively evaluate $\textit{ExpFinder}$ on four different datasets from the academic domains in comparison with six different expert finding models. The evaluation results show that $\textit{ExpFinder}$ is a highly effective model for expert finding, substantially outperforming all the compared models in 19% to 160.2%.Comment: 15 pages, 18 figures, "for source code on Github, see https://github.com/Yongbinkang/ExpFinder", "Submitted to IEEE Transactions on Knowledge and Data Engineering

A Nudge-Inspired AI-Driven Health Platform for Self-Management of Diabetes

Diabetes mellitus is a serious chronic disease that affects the blood sugar levels in individuals, with current predictions estimating that nearly 578 million people will be affected by diabetes by 2030. Patients with type II diabetes usually follow a self-management regime as directed by a clinician to help regulate their blood glucose levels. Today, various technology solutions exist to support self-management; however, these solutions tend to be independently built, with little to no research or clinical grounding, which has resulted in poor uptake. In this paper, we propose, develop, and implement a nudge-inspired artificial intelligence (AI)-driven health platform for self-management of diabetes. The proposed platform has been co-designed with patients and clinicians, using the adapted 4-cycle design science research methodology (A4C-DSRM) model. The platform includes (a) a cross-platform mobile application for patients that incorporates a macronutrient detection algorithm for meal recognition and nudge-inspired meal logger, and (b) a web-based application for the clinician to support the self-management regime of patients. Further, the platform incorporates behavioral intervention techniques stemming from nudge theory that aim to support and encourage a sustained change in patient lifestyle. Application of the platform has been demonstrated through an illustrative case study via two exemplars. Further, a technical evaluation is conducted to understand the performance of the MDA to meet the personalization requirements of patients with type II diabetes

Weed Detection Using Deep Learning: A Systematic Literature Review

Weeds are one of the most harmful agricultural pests that have a significant impact on crops. Weeds are responsible for higher production costs due to crop waste and have a significant impact on the global agricultural economy. The importance of this problem has promoted the research community in exploring the use of technology to support farmers in the early detection of weeds. Artificial intelligence (AI) driven image analysis for weed detection and, in particular, machine learning (ML) and deep learning (DL) using images from crop fields have been widely used in the literature for detecting various types of weeds that grow alongside crops. In this paper, we present a systematic literature review (SLR) on current state-of-the-art DL techniques for weed detection. Our SLR identified a rapid growth in research related to weed detection using DL since 2015 and filtered 52 application papers and 8 survey papers for further analysis. The pooled results from these papers yielded 34 unique weed types detection, 16 image processing techniques, and 11 DL algorithms with 19 different variants of CNNs. Moreover, we include a literature survey on popular vanilla ML techniques (e.g., SVM, random forest) that have been widely used prior to the dominance of DL. Our study presents a detailed thematic analysis of ML/DL algorithms used for detecting the weed/crop and provides a unique contribution to the analysis and assessment of the performance of these ML/DL techniques. Our study also details the use of crops associated with weeds, such as sugar beet, which was one of the most commonly used crops in most papers for detecting various types of weeds. It also discusses the modality where RGB was most frequently used. Crop images were frequently captured using robots, drones, and cell phones. It also discusses algorithm accuracy, such as how SVM outperformed all machine learning algorithms in many cases, with the highest accuracy of 99 percent, and how CNN with its variants also performed well with the highest accuracy of 99 percent, with only VGGNet providing the lowest accuracy of 84 percent. Finally, the study will serve as a starting point for researchers who wish to undertake further research in this area

VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment

Assessing learners’ understanding and competency in video-based digital learning is time-consuming and very difficult for educators, as it requires the generation of accurate and valid questions from pre-recorded learning videos. This paper demonstrates VideoDL, a video-based learning framework powered by Artificial Intelligence (AI) that supports automatic question generation and answer assessment from videos. VideoDL comprises of various AI algorithms, and an interactive web-based user interface (UI) developed using the principles of human-centred design. Our empirical evaluation using real-world videos from multiple domains demonstrates the effectiveness of VideoDL