360 Quantified Self

Wearable devices with a wide range of sensors have contributed to the rise of the Quantified Self movement, where individuals log everything ranging from the number of steps they have taken, to their heart rate, to their sleeping patterns. Sensors do not, however, typically sense the social and ambient environment of the users, such as general life style attributes or information about their social network. This means that the users themselves, and the medical practitioners, privy to the wearable sensor data, only have a narrow view of the individual, limited mainly to certain aspects of their physical condition. In this paper we describe a number of use cases for how social media can be used to complement the check-up data and those from sensors to gain a more holistic view on individuals' health, a perspective we call the 360 Quantified Self. Health-related information can be obtained from sources as diverse as food photo sharing, location check-ins, or profile pictures. Additionally, information from a person's ego network can shed light on the social dimension of wellbeing which is widely acknowledged to be of utmost importance, even though they are currently rarely used for medical diagnosis. We articulate a long-term vision describing the desirable list of technical advances and variety of data to achieve an integrated system encompassing Electronic Health Records (EHR), data from wearable devices, alongside information derived from social media data.Comment: QCRI Technical Repor

Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop

Communication Theoretic Data Analytics

Widespread use of the Internet and social networks invokes the generation of big data, which is proving to be useful in a number of applications. To deal with explosively growing amounts of data, data analytics has emerged as a critical technology related to computing, signal processing, and information networking. In this paper, a formalism is considered in which data is modeled as a generalized social network and communication theory and information theory are thereby extended to data analytics. First, the creation of an equalizer to optimize information transfer between two data variables is considered, and financial data is used to demonstrate the advantages. Then, an information coupling approach based on information geometry is applied for dimensionality reduction, with a pattern recognition example to illustrate the effectiveness. These initial trials suggest the potential of communication theoretic data analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan. 201

How to design an inclusive care, based on individuals’ social cognition capacities to improve quality of life for people with dementia?

Social isolation is one of the consequences of dementia. By progression of dementia the ability to talk, remember, and orient oneself in space gradually reduces and the need for assistance with daily tasks increases. These physical and mental abatement causes decline in social, behavioural, and emotional capabilities

Using Mhealth Data To Improve The Management Of Chronic Pain

Chronic pain is widespread and mHealth provides a novel solution to the management of pain through the use of smartphone technology. The purpose of this research is to determine whether mobile health data is useful for clinicians who are frequently involved in the management of chronic pain, and to assess their data needs. We selected orthopedic surgeons and physical therapists as a population likely to be interested in the management of chronic pain. We conducted semi-structured interviews with physical therapists and orthopedic surgeons to better understand the gaps in needs and knowledge. Qualitative thematic analysis was performed using the interview transcripts to inductively determine themes in the data. Thematic analysis of the data revealed significantly different data needs between physical therapists and orthopedic surgeons, increasing focus on functionality and outcomes, and the importance of compliance and efficiency. Overall, physical therapists responded enthusiastically to the use of smartphone interventions in their practice. The promise of mHealth presents a great opportunity for patient management when patients are in their everyday contexts, rather than solely in the clinic

Advancing psychological therapies for chronic pain

There is a strong tradition of therapy development and evaluation in the field of psychological interventions for chronic pain. However, despite this research production, the effects of treatments remain uncertain, and treatment development has stalled. This review summarises the current evidence but focusses on promising areas for improvement. Advancing psychological therapies for chronic pain will come from a radical re-imagining of the content, delivery, place, and control of therapy. The next generation of therapeutic interventions will also need alternative methods of measurement and evaluation, and options are discussed

Construction of A Personalized Service Model of ABC Community Fresh E-commerce Based on Small Data

The homogenization of products and services in the development of community fresh e-commerce is more serious, and providing personalized services to community customers has become a new development key point. From the perspective of small data research, this paper proposes an e-commerce service model that uses small data to provide personalized fresh products for community customers. Taking ABC community convenience store as the research object, the paper analyzes the application of community customer small data in the personalized service of community fresh convenience store to improve the loyalty and satisfaction of community customers and promote the development of community fresh e-commerce

Advancing Models and Theories for Digital Behavior Change Interventions

To be suitable for informing digital behavior change interventions, theories and models of behavior change need to capture individual variation and changes over time. The aim of this paper is to provide recommendations for development of models and theories that are informed by, and can inform, digital behavior change interventions based on discussions by international experts, including behavioral, computer, and health scientists and engineers. The proposed framework stipulates the use of a state-space representation to define when, where, for whom, and in what state for that person, an intervention will produce a targeted effect. The "state" is that of the individual based on multiple variables that define the "space" when a mechanism of action may produce the effect. A state-space representation can be used to help guide theorizing and identify crossdisciplinary methodologic strategies for improving measurement, experimental design, and analysis that can feasibly match the complexity of real-world behavior change via digital behavior change interventions