Discovery of the potential role of sensors in a personal emergency response system: what can we learn from a single workshop?

Capturing knowledge from domain experts is important to effectively integrate novel technological support in existing care processes. In this paper, we present our experiences in using a specific type of workshop, which we identified as a decision-tree workshop, to determine the process and information exchange during the usage of a Personal Emergency Response System (PERS). We conducted the workshop with current and possible future users of a PERS system to investigate the potential of context-and social awareness for such a system. We discuss the workshop format as well as the results and reflection on this workshop

Context Aware Mobile Knowledge Management System in Healthcare

Clinical practitioners need to have the right information, at the right time, at the right place; which is possible by mobile healthcare. It is not only important for them to have the right information, but they have to manage this right information with proper knowledge, which requires the skills for efficient knowledge management in a mobile healthcare setting. Utilization of mobile devices in healthcare to share knowledge may not only improve the decision taking time, but also reduces the medical errors and costs involved. Need for this information sharing may often be required across various hospital staff that play variety of roles (as practitioners, nurses, administrative staff etc.) and works in multiple schedules (work shifts: night, day etc.), resulting in need for proper ‘context aware’ knowledge. This research paper is an attempt to develop a context aware model which can help the healthcare organizations in efficiently practicing the knowledge management in a context based mobile healthcare setting

Context Mining with Machine Learning Approach: Understanding, Sensing, Categorizing, and Analyzing Context Parameters

Context is a vital concept in various fields, such as linguistics, psychology, and computer science. It refers to the background, environment, or situation in which an event, action, or idea occurs or exists. Categorization of context involves grouping contexts into different types or classes based on shared characteristics. Physical context, social context, cultural context, temporal context, and cognitive context are a few categories under which context can be divided. Each type of context plays a significant role in shaping our understanding and interpretation of events or actions. Understanding and categorizing context is essential for many applications, such as natural language processing, human-computer interaction, and communication studies, as it provides valuable information for interpretation, prediction, and decision-making. In this paper, we will provide an overview of the concept of context and its categorization, highlighting the importance of context in various fields and applications. We will discuss each type of context and provide examples of how they are used in different fields. Finally, we will conclude by emphasizing the significance of understanding and categorizing context for interpretation, prediction, and decision-making

Signal Processing of Multimodal Mobile Lifelogging Data towards Detecting Stress in Real-World Driving

Stress is a negative emotion that is part of everyday life. However, frequent episodes or prolonged periods of stress can be detrimental to long-term health. Nevertheless, developing self-awareness is an important aspect of fostering effective ways to self-regulate these experiences. Mobile lifelogging systems provide an ideal platform to support self-regulation of stress by raising awareness of negative emotional states via continuous recording of psychophysiological and behavioural data. However, obtaining meaningful information from large volumes of raw data represents a significant challenge because these data must be accurately quantified and processed before stress can be detected. This work describes a set of algorithms designed to process multiple streams of lifelogging data for stress detection in the context of real world driving. Two data collection exercises have been performed where multimodal data, including raw cardiovascular activity and driving information, were collected from twenty-one people during daily commuter journeys. Our approach enabled us to 1) pre-process raw physiological data to calculate valid measures of heart rate variability, a significant marker of stress, 2) identify/correct artefacts in the raw physiological data and 3) provide a comparison between several classifiers for detecting stress. Results were positive and ensemble classification models provided a maximum accuracy of 86.9% for binary detection of stress in the real-world

An Intelligent Multi-stage Channel Acquisition Model for CR-WBANs: A Context Aware Approach

Cognitive Radio (CR) came as a solution to mitigate challenges that wireless body area networks (WBANs) suffer from. CR is an intelligence-based technology that senses, observes, and learns from its operating environment to access licensed bands in the spectrum when they are not being utilized by primary users. Deploying a CR technology in WBANs applications, enhances spectrum scalability, increases system robustness, and decreases latency. Accordingly, CR-WBANs help in building a more efficient and reliable ubiquitous healthcare system than conventional WBANs do. However, CR-WBANs are still evolving, and many challenges need to be investigated, in particular, is how to acquire a channel and prioritize data streams among multiple CR-users (i.e., multiple patients) based on the severity of their health status, in a manner to decrease network latency and increase network scalability. To address this challenge, this work proposes a novel intelligent channel acquisition model for multiple CR-WBANs within ubiquitous healthcare system, whereby contextual data, namely, channel properties, intra-node characteristics, and patients’ profile information, is integrated in channel acquisition decision process. The proposed work is a multi-stage fusion system that is composed of local and global decisions units. A fuzzy logic system is utilized to make decisions in the local unit, which are sensing the channel availability and assessing the severity of patients' health status. Moreover, a neural network is employed as a global sensing decision center, whereby local sensing decisions, channel properties, and intra-node characteristics are augmented in the decision process. Furthermore, a cluster-based heuristic algorithm is formulated, in the global decision unit, to prioritize data streams among CR-users based on the criticality of their health conditions (i.e., acute, urgent, and normal). Patients' local health assessments and avatars (e.g., age, medical history, etc.) are exploited in the prioritization process