Mobile Activity Recognition for a Whole Day: Recognizing Real Nursing Activity with a Big Dataset

In this paper, we provide a real nursing data set for mobile activity recognition that can be used for supervised machine learning, and big data combined the patient medical records and sensors attempted for 2 years, and also propose a method for recognizing activities for a whole day utilizing prior knowledge about the activity segments in a day. Furthermore, we demonstrate data mining by applying our method to the bigger data with additional hospital data. In the proposed method, we 1) convert a set of segment timestamps into a prior probability of the activity segment by exploiting the concept of importance sampling, 2) obtain the likelihood of traditional recognition methods for each local time window within the segment range, and, 3) apply Bayesian estimation by marginalizing the conditional probability of estimating the activities for the segment samples. By evaluating with the dataset, the proposed method outperformed the traditional method without using the prior knowledge by 25.81% at maximum by balanced classification rate. Moreover, the proposed method significantly reduces duration errors of activity segments from 324.2 seconds of the traditional method to 74.6 seconds at maximum. We also demonstrate the data mining by applying our method to bigger data in a hospital.2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), Sep. 7-11, Grand Front Osaka in Umeda, Osaka, Japa

How Do Communities Use a Participatory Public Health Approach to Build Resilience? The Los Angeles County Community Disaster Resilience Project.

Community resilience is a key concept in the National Health Security Strategy that emphasizes development of multi-sector partnerships and equity through community engagement. Here, we describe the advancement of CR principles through community participatory methods in the Los Angeles County Community Disaster Resilience (LACCDR) initiative. LACCDR, an initiative led by the Los Angeles County Department of Public Health with academic partners, randomized 16 community coalitions to implement either an Enhanced Standard Preparedness or Community Resilience approach over 24 months. Facilitated by a public health nurse or community educator, coalitions comprised government agencies, community-focused organizations and community members. We used thematic analysis of data from focus groups (n = 5) and interviews (n = 6 coalition members; n = 16 facilitators) to compare coalitions' strategies for operationalizing community resilience levers of change (engagement, partnership, self-sufficiency, education). We find that strategies that included bidirectional learning helped coalitions understand and adopt resilience principles. Strategies that operationalized community resilience levers in mutually reinforcing ways (e.g., disseminating information while strengthening partnerships) also secured commitment to resilience principles. We review additional challenges and successes in achieving cross-sector collaboration and engaging at-risk groups in the resilience versus preparedness coalitions. The LACCDR example can inform strategies for uptake and implementation of community resilience and uptake of the resilience concept and methods

Discovering the memory thief: MOOC participants’ personal experiences of dementia

Dementia is one of the greatest social issues of our time. There are brief explorations of experiences of dementia in the literature, however this study seeks to further explore the experiences of the general public in relation to dementia. This study aimed to characterise experiences of dementia in the general population. This characterisation was achieved by developing and opening a massive open online course on dementia, which collected information from participants who responded to the question “share your own personal experience of dementia”. This data was then analysed by two researchers using Framework Analysis. Four themes (the condition, caring, perception, and control) and indicative quotes are presented and discussed. Experiences of dementia are positive as well as negative. Findings update understanding of these experiences and demonstrate new insights

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition

The automatic, sensor-based assessment of challenging behavior of persons with dementia is an important task to support the selection of interventions. However, predicting behaviors like apathy and agitation is challenging due to the large inter- and intra-patient variability. Goal of this paper is to improve the recognition performance by making use of the observation that patients tend to show specific behaviors at certain times of the day or week. We propose to identify such segments of similar behavior via clustering the distributions of annotations of the time segments. All time segments within a cluster then consist of similar behaviors and thus indicate a behavioral predisposition (BPD). We utilize BPDs by training a classifier for each BPD. Empirically, we demonstrate that when the BPD per time segment is known, activity recognition performance can be substantially improved.Comment: Submitted to iWOAR 2022 - 7th international Workshop on Sensor-Based Activity Recognition and Artificial Intelligenc

AI in Learning: Designing the Future

AI (Artificial Intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This open access book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning. This book will be of use to a variety of readers, including researchers, AI users, companies and policy makers

A Light Weight Smartphone Based Human Activity Recognition System with High Accuracy

With the pervasive use of smartphones, which contain numerous sensors, data for modeling human activity is readily available. Human activity recognition is an important area of research because it can be used in context-aware applications. It has significant influence in many other research areas and applications including healthcare, assisted living, personal fitness, and entertainment. There has been a widespread use of machine learning techniques in wearable and smartphone based human activity recognition. Despite being an active area of research for more than a decade, most of the existing approaches require extensive computation to extract feature, train model, and recognize activities. This study presents a computationally efficient smartphone based human activity recognizer, based on dynamical systems and chaos theory. A reconstructed phase space is formed from the accelerometer sensor data using time-delay embedding. A single accelerometer axis is used to reduce memory and computational complexity. A Gaussian mixture model is learned on the reconstructed phase space. A maximum likelihood classifier uses the Gaussian mixture model to classify ten different human activities and a baseline. One public and one collected dataset were used to validate the proposed approach. Data was collected from ten subjects. The public dataset contains data from 30 subjects. Out-of-sample experimental results show that the proposed approach is able to recognize human activities from smartphones’ one-axis raw accelerometer sensor data. The proposed approach achieved 100% accuracy for individual models across all activities and datasets. The proposed research requires 3 to 7 times less amount of data than the existing approaches to classify activities. It also requires 3 to 4 times less amount of time to build reconstructed phase space compare to time and frequency domain features. A comparative evaluation is also presented to compare proposed approach with the state-of-the-art works