Building distributed heterogeneous smart phone Java applications an evaluation from a development perspective

The advances in mobile phone technology have enabled such devices to be programmed to run general-purpose applications using a special edition of the Java programming language. Java is designed to be a heterogeneous programming language targeting different platforms. Such ability when coupled with the provision of high-speed mobile Internet access would open the door for a new breed of distributed mobile applications. This paper explores the capabilities and limitations of this technology and addresses the considerations that must be taken when designing and developing such distributed applications. Our findings are verified by building a test client-server system where the clients in this system are mobile phones behaving as active processing elements not just mere service requesters

mHealth Research Applied to Regulated and Unregulated Behavioral Health Sciences

Behavioral scientists are developing new methods and frameworks that leverage mobile health technologies to optimize individual level behavior change. Pervasive sensors and mobile apps allow researchers to passively observe human behaviors “in the wild” 24/7 which supports delivery of personalized interventions in the real-world environment. This is all possible because these technologies contain an incredible array of sensors that allow applications to constantly record user location and can contextualize current environmental conditions through barometers, thermometers, and ambient light sensors and can also capture audio and video of the user and their surroundings through multiple integrated high-definition cameras and microphones. These tools are a game changer in behavioral health research and, not surprisingly, introduce new ethical, regulatory/legal and social implications described in this article

Canine-centered interface design: supporting the work of diabetes alert dogs

Many people with Diabetes live with the continuous threat of hypoglycaemic attacks and the danger of going into coma. Diabetic Alert Dogs are trained to detect the onset of an attack before the human handler they are paired with deteriorates, giving them time to take action. We investigated requirements for designing an alert system allowing dogs to remotely call for help when their human falls unconscious before being able to react to an alert. Through a multispecies ethnographic approach we focus on teasing out the requirements for a physical canine user interface, involving both dogs, their handlers and trainers in the design. We discuss tensions between the requirements for the canine and the human users, argue the need for increased sensitivity towards the needs of individual dogs that goes beyond breed specific physical characteristics and reflect on how we can move from designing for dogs to designing with dogs

Visions and Challenges in Managing and Preserving Data to Measure Quality of Life

Health-related data analysis plays an important role in self-knowledge, disease prevention, diagnosis, and quality of life assessment. With the advent of data-driven solutions, a myriad of apps and Internet of Things (IoT) devices (wearables, home-medical sensors, etc) facilitates data collection and provide cloud storage with a central administration. More recently, blockchain and other distributed ledgers became available as alternative storage options based on decentralised organisation systems. We bring attention to the human data bleeding problem and argue that neither centralised nor decentralised system organisations are a magic bullet for data-driven innovation if individual, community and societal values are ignored. The motivation for this position paper is to elaborate on strategies to protect privacy as well as to encourage data sharing and support open data without requiring a complex access protocol for researchers. Our main contribution is to outline the design of a self-regulated Open Health Archive (OHA) system with focus on quality of life (QoL) data.Comment: DSS 2018: Data-Driven Self-Regulating System