Quantified-self Applications Research Articles

Facilitating Physical Activity through On-Site Quantified-Self Data Sharing

With the development of sensing technology and the popularization of quantified-self devices, there are increasing types of health-related data that can be sensed, visualized and presented to the user. However, most existing quantified-self applications are designed to support self-management and self-reflection; only a few studies so far have investigated the social aspect of quantified-self data. In this study, we investigated the social role of quantified-self data by introducing the design and evaluation of SocialBike—a digitally augmented bicycle that aims to increase the user’s intrinsic motivation in physical activity through on-site quantified-self data sharing. We conducted a controlled experiment on a cycling simulation system. Two forms of SocialBike’s on-bike display were evaluated with 36 participants. We used the Intrinsic Motivation Inventory to collect quantitative data about users’ intrinsic motivation in physical activity; the cycling simulation system recorded quantitative data about user behavior. Qualitative data was collected through semi-structured interviews. We conducted paired sample t-test to analyze both types of quantitative data; qualitative data were analyzed by the method of thematic analysis. The results show that SocialBike’s front display significantly increased users’ intrinsic motivation in physical activity. A total of nine themes were identified from the qualitative analysis, providing supplementary explanations for the quantitative results and additional insights into the overall design.

A Scientometric Analysis of Self-tracking in Relation to Artificial Intelligence and Big Data

As an increasing amount of personal data has been gathered by wearable and mobile devices, self-tracking, or the practice that people keep track, has become an important topic in Artificial Intelligence (AI) and big data applications. With the aim to provide a systematic review of the literature on self-tracking, this paper presents a scientometric analysis of 109 articles since 2000 collected from the Web of Science. Based on keyword co-occurrence network analysis, the paper has identified four major clusters: (1) wearables as quantified-self applications; (2) big data and critical theory; (3) data and privacy; (4) personal informatics. The further keywords-in-context (KWIC) analysis of the abstracts of the dataset clarifies the seemingly-interchangeable notions of “self-tracking” and “quantified-self”: While “self-tracking” refers to more general activities, practices, technologies, and applications of keeping tracks, “quantified-self” refers to the more conscious efforts and meaning-making outcomes of the self-tracking activities. Such clarification, along with the keyword network analysis, suggests that self-tracking has become a specific and major type of datafication of human conditions or existence and that quantified-self is the construction of self through such datafication. A more integrated conceptual framework is needed for future research to better understand what amounts to meaningful datafication of human conditions and existence, thereby helping researchers and designers to discern the classic notions of health, wellness, and happiness for better research and design outcomes.

Enhancing Intrinsic Motivation in Physical Activity through Quantified-self Data Sharing

INTRODUCTION: Quantified-self application is widely used in sports and health management; the type and amount of data that can be detected and fed back to the user are growing rapidly. However, only a few studies discussed the social attributes of quantified-self data, especially in the context of cycling.OBJECTIVES: To design and evaluate SocialBike - a digitally augmented bicycle that aims to increase user’s motivation in physical activity by showing their quantified-self data to each other.METHODS: We conducted a within-subject experiment on a cycling simulation system with 20 participants. The on-bike display of SocialBike was compared to a control display to evaluate its impact on two dependent variables, including user’s intrinsic motivation in physical activity and social relatedness with other cyclists nearby. Intrinsic Motivation Inventory (IMI) was used to measure users’ self-reported intrinsic motivation and social relatedness. The user’s cycling behaviour data were also recorded by the simulation system. We extracted six indicators about the two dependent variables from the behaviour data. Qualitative data were collected through semi-structured interviews. We conducted a paired sample T-test on both types of quantitative data. Qualitative data were analysed by the method of thematic analysis.RESULTS: The results of the Intrinsic Motivation Inventory show that SocialBike’s on-bike display has significant positive effects on four subscales, including intrinsic motivation, perceived competence, values/usefulness, and relatedness. The results of user cycling behaviour indicate that SocialBike increased user’s social relatedness with other cyclists nearby by significantly affecting three indicators. Three themes were identified from qualitative data, including motivation, social interaction, and data visualization. The qualitative result provides supplementary evidence for the quantitative result and additional insights into the overall design of SocialBike.CONCLUSION: SocialBike as a platform for sharing quantified-self data have positive effects in enhancing users’ intrinsic motivation in physical activity and social relatedness with other cyclists nearby.

Knowledge Discovery on IoT-Enabled mHealth Applications.

The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.

Recommender systems for IoT enabled quantified-self applications

As an emerging trend in big data science, applications based on the Quantified-Self (QS) engage individuals in the self-tracking of any kind of biological, physical, behavioral, or environmental information as individuals or groups. There are new needs and opportunities for recommender systems to develop new models/approaches to support QS application users. Recommender systems can help to more easily identify relevant artifacts for users and thus improve user experiences. Currently recommender systems are widely and effectively used in the e-commerce domain (e.g., online music services, online bookstores). Next-generation QS applications could include more recommender tools for assisting the users of QS systems based on their personal self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. In this paper, we propose three new recommendation approaches for QS applications: Virtual Coach, Virtual Nurse, and Virtual Sleep Regulator which help QS users to improve their health conditions. Virtual Coach works like a real fitness coach to recommend personalized work-out plans whereas Virtual Nurse considers the medical history and health targets of a user to recommend a suitable physical activity plan. Virtual Sleep Regulator is specifically designed for insomnia (a kind of sleep disorder) patients to improve their sleep quality with the help of recommended physical activity and sleep plans. We explain how these proposed recommender technologies can be applied on the basis of the collected QS data to create qualitative recommendations for user needs. We present example recommendation results of Virtual Sleep Regulator on the basis of the dataset from a real world QS application.

You Are Sensing, but Are You Biased?

Mobile devices are becoming pervasive to our daily lives: they follow us everywhere and we use them for much more than just communication. These devices are also equipped with a myriad of different sensors that have the potential to allow the tracking of human activities, user patterns, location, direction and much more. Following this direction, many movements including sports, quantified self, and mobile health ones are starting to heavily rely on this technology, making it pivotal that the sensors offer high accuracy. However, heterogeneity in hardware manufacturing, slight substrate differences, electronic interference as well as external disturbances are just few of the reasons that limit sensor output accuracy which in turn hinders sensor usage in applications which need very high granularity and precision, such as quantified-self applications. Although, calibration of sensors is a widely studied topic in literature to the best of our knowledge no publicly available research exists that specifically tackles the calibration of mobile phones and existing methods that can be adapted for use in mobile devices not only require user interaction but they are also not adaptive to changes. Additionally, alternative approaches for performing more granular and accurate sensing exploit body-wide sensor networks using mobile phones and additional sensors; as one can imagine these techniques can be bulky, tedious, and not particularly user friendly. Moreover, existing techniques for performing data corrections post-acquisition can produce inconsistent results as they miss important context information provided from the device itself; which when used, has been shown to produce better results without a imposing a significant power-penalty. In this paper we introduce a novel multiposition calibration scheme that is specifically targeted at mobile devices Our scheme exploits machine learning techniques to perform an adaptive, power-efficient auto-calibration procedure with which achieves high output sensor accuracy when compared to state of the art techniques without requiring any user interaction or special equipment beyond device itself Moreover, the energy costs associated with our approach are lower than the alternatives (such as Kalman filter based solutions) and the overall power penalty is < 5% when compared against power usage that is exhibited when using uncalibrated traces, thus, enabling our technique to be used efficiently on a wide variety of devices Finally, our evaluation illustrates that calibrated signals offer a tangible benefit in classification accuracy, ranging from 3 to 10%, over uncalibrated ones when using state of the art classifiers, on the other hand when using simpler SVM classifiers the classification improvement is boosted ranging from 8% to 12% making lower performing classifiers much more reliable Additionally, we show that for similar activities which are hard to distinguish otherwise, we reach an accuracy of > 95% when using neural network classifiers and > 88% when using SVM classifiers where uncalibrated data classification only reaches ~ 85% and ~ 80% respectively This can be a make or break factor in the use of accelerometer and gyroscope data in applications requiring high accuracy e g sports, health, games and others

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery

A key contemporary trend emerging in big data science is the quantified self (QS)-individuals engaged in the self-tracking of any kind of biological, physical, behavioral, or environmental information as n=1 individuals or in groups. There are opportunities for big data scientists to develop new models to support QS data collection, integration, and analysis, and also to lead in defining open-access database resources and privacy standards for how personal data is used. Next-generation QS applications could include tools for rendering QS data meaningful in behavior change, establishing baselines and variability in objective metrics, applying new kinds of pattern recognition techniques, and aggregating multiple self-tracking data streams from wearable electronics, biosensors, mobile phones, genomic data, and cloud-based services. The long-term vision of QS activity is that of a systemic monitoring approach where an individual's continuous personal information climate provides real-time performance optimization suggestions. There are some potential limitations related to QS activity-barriers to widespread adoption and a critique regarding scientific soundness-but these may be overcome. One interesting aspect of QS activity is that it is fundamentally a quantitative and qualitative phenomenon since it includes both the collection of objective metrics data and the subjective experience of the impact of these data. Some of this dynamic is being explored as the quantified self is becoming the qualified self in two new ways: by applying QS methods to the tracking of qualitative phenomena such as mood, and by understanding that QS data collection is just the first step in creating qualitative feedback loops for behavior change. In the long-term future, the quantified self may become additionally transformed into the extended exoself as data quantification and self-tracking enable the development of new sense capabilities that are not possible with ordinary senses. The individual body becomes a more knowable, calculable, and administrable object through QS activity, and individuals have an increasingly intimate relationship with data as it mediates the experience of reality.