Tools for image annotation Using context-awareness, NFC and image clustering

Annotation of images is crucial for enabling keyword based imagesearch. However, the enormous amount of available digital photos makesmanual annotation impractical, and requires methods for automaticimage annotation. This paper describes two complementary approachesto automatic annotation of images depicting some public attraction. TheLoTagr system provides annotation information for already captured,geo-positioned images, by selecting nearby, previously tagged imagesfrom a source image collection, and subsequently collect the mostfrequently used tags from these images. The NfcAnnotate systemenables annotation at image capture time, by using NFC (Near FieldCommunication) and NFC information tags provided at the site ofthe attraction. NfcAnnotate enables clustering of topically relatedimages, which makes it possible annotate a set of images in oneannotation operation. In cases when NFC information tags are notavailable, NfcAnnotate image clustering can be combined with LoTagrto conveniently annotate every image in the cluster in a single operation

The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge

Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges

A Framework for AI-enabled Proactive mHealth with Automated Decision-making for a User’s Context

Health promotion is to enable people to take control over their health. Digital health with mHealth empowers users to establish proactive health, ubiquitously. The users shall have increased control over their health to improve their life by being proactive. To develop proactive health with the principles of prediction, prevention, and ubiquitous health, artificial intelligence with mHealth can play a pivotal role. There are various challenges for establishing proactive mHealth. For example, the system must be adaptive and provide timely interventions by considering the uniqueness of the user. The context of the user is also highly relevant for proactive mHealth. The context provides parameters as input along with information to formulate the current state of the user. Automated decision-making is significant with user-level decision-making as it enables decisions to promote well-being by technological means without human involvement. This paper presents a design framework of AI-enabled proactive mHealth that includes automated decision-making with predictive analytics, Just-in-time adaptive interventions and a P5 approach to mHealth. The significance of user-level decision-making for automated decision-making is presented. Furthermore, the paper provides a holistic view of the user's context with profile and characteristics. The paper also discusses the need for multiple parameters as inputs, and the identification of sources e.g., wearables, sensors, and other resources, with the challenges in the implementation of the framework. Finally, a proof-of-concept based on the framework provides design and implementation steps, architecture, goals, and feedback process. The framework shall provide the basis for the further development of AI-enabled proactive mHealth

Fit-Twin: A Digital Twin of a User with Wearables and Context as Input for Health Promotion

Digital health contributes to health promotion by empowering the user with the holistic view of their health. Health promotion is to enable the user to take control over their health. The availability of wearables has contributed to the shift in healthcare, that is more connected, predictive, and proactive. Proactive in healthcare is to predict and prevent a situation, beforehand. This shift in healthcare puts the user in charge of most healthrelated decisions. Innovative technologies like AI already contribute to the cause by applying reasoning and negotiation to the collected health data to provide timely interventions to the user. The availability of realtime data from sensors that the user wears all the time allows more opportunities with new health insights. One such prospect is the use of digital twins, which provides personalization and precision. Digital twins also allow risk-free modelling for more accurate outcomes. A user digital twin is not just a virtual replica, but it combines all the factors that can impact the user. The context of the user is a prominent factor in healthcare. The paper establishes the need for digital twins in health promotion. In this paper, a Fit-twin is presented that mimics a user with wearables and the user context as input. The Fit-twin is implemented using Azure digital twins, Fitbit charge, and local context API. This allows one-way communication between the user and the Fit-twin. The outcome is a user digital twin that can be used for health promotion by applying predictive capabilities

Data Analysis Techniques for Smart Nudging

Nudge principles and techniques are significant in communications, marketing, and groups’ motivation to improve personal health, wealth, and well-being. We make numerous decisions in online situations. People’s health and well-being have garnered widespread interest and concern in this wearable’s age. Smart nudging is defined as “digital nudging, where the guidance of user behavior is tailored to be relevant to the current situation of each user”. Emerging digital devices such as smartwatches, smart bands, and smartphones will continuously capture and analyze your activity and health-related data from individuals and communities in their everyday environment. Providing context-aware nudges in these digital health devices will help individuals identify and self-manage their health and physical activity. This study aims to provide data analysis techniques for smart nudging and examine it susability in developing a smart nudging system to provide context-based nudges that are more likely to succeed

Data collection and analysis methods for smart nudging to promote physical activity: Protocol for a mixed methods study

New digital technologies like activity trackers, nudge concepts, and approaches can inspire and improve personal health. There is increasing interest in employing such devices to monitor people’s health and well-being. These devices can continually gather and examine health-related information from people and groups in their familiar surroundings. Context-aware nudges can assist people in self-managing and enhancing their health. In this protocol paper, we describe how we plan to investigate what motivates people to engage in physical activity (PA), what influences them to accept nudges, and how participant motivation for PA may be impacted by technology use

Employment Is Associated with the Health-Related Quality of Life of Morbidly Obese Persons

Published version of an article in the journal: Obesity Surgery. The original publication is available at Springerlink. http://dx.doi.org/10.1007/s11695-010-0289-6. Open AccessBackground  We aimed to investigate whether employment status was associated with health-related quality of life (HRQoL) in a population of morbidly obese subjects. Methods  A total of 143 treatment-seeking morbidly obese patients completed the Medical Outcome Study 36-Item Short-Form Health Survey (SF-36) and the Obesity and Weight-Loss Quality of Life (OWLQOL) questionnaires. The former (SF-36) is a generic measure of physical and mental health status and the latter (OWLQOL) an obesity-specific measure of emotional status. Multiple linear regression analyses included various measures of the HRQoL as dependent variables and employment status, education, marital status, gender, age, body mass index (BMI), type 2 diabetes, hypertension, obstructive sleep apnea, and treatment choice as independent variables. Results  The patients (74% women, 56% employed) had a mean (SD, range) age of 44 (11, 19–66) years and a mean BMI of 44.3 (5.4) kg/m2. The employed patients reported significantly higher HRQoL scores within all eight subscales of SF-36, while the OWLQOL scores were comparable between the two groups. Multiple linear regression confirmed that employment was a strong independent predictor of HRQoL according to the SF-36. Based on part correlation coefficients, employment explained 16% of the variation in the physical and 9% in the mental component summaries of SF-36, while gender explained 22% of the variation in the OWLQOL scores. Conclusion  Employment is associated with the physical and mental HRQoL of morbidly obese subjects, but is not associated with the emotional aspects of quality of life

The Impossible, the Unlikely, and the Probable Nudges: A Classification for the Design of Your Next Nudge

Nudging provides a way to gently influence people to change behavior towards a desired goal, e.g., by moving towards a healthier or more environmentally friendly lifestyle. Personalized and context-aware digital nudging (named smart nudging) can be a powerful tool for efficient nudging by tailoring nudges to the current situation of each individual user. However, designing smart nudges is challenging, as different users may need different supports to improve their behavior. Determining the next nudge for a specific user must be done based on the user’s current situation, abilities, and potential for improvement. In this paper, we focus on the challenge of designing the next nudge by presenting a novel classification of nudges that distinguishes between (i) nudges that are impossible for the user to follow, (ii) nudges that are unlikely to be followed, and (iii) probable nudges that the user can follow. The classification is tailored to individual users based on user profiles, current situations, and knowledge of previous behaviors. This paper describes steps in the nudge design process and a novel set of principles for designing smart nudges

Recommendations with a Nudge

In areas such as health, environment, and energy consumption, there is a need to do better. A common goal in society is to get people to behave in ways that are sustainable for the environment or support a healthier lifestyle. Nudging is a term known from economics and political theory, for influencing decisions and behavior using suggestions, positive reinforcement, and other non-coercive means. With the extensive use of digital devices, nudging within a digital environment (known as digital nudging) has great potential. We introduce smart nudging, where the guidance of user behavior is presented through digital nudges tailored to be relevant to the current situation of each individual user. The ethics of smart nudging and the transparency of nudging is also discussed. We see a smart nudge as a recommendation to the user, followed by information that both motivates and helps the user choose the suggested behavior. This paper describes such nudgy recommendations, the design of a smart nudge, and an architecture for a smart nudging system. We compare smart nudging to traditional models for recommender systems, and we describe and discuss tools (or approaches) for nudge design. We discuss the challenges of designing personalized smart nudges that evolve and adapt according to the user’s reactions to the previous nudging and possible behavioral change of the user