Monitoring and measuring physical activity and sedentary behaviour

Fergus, Paul; Attwood, Andrew; Dobbins, Chelsea; Stratton, Gareth; Hussain, Abir; Al‐Jumeily, Dhiya; Randles, Martin

doi:10.1504/ijhtm.2012.052548

Cited by 5 publications

(8 citation statements)

References 36 publications

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“…This implementation addresses the limitations of current indoor localisation technologies, by presenting a low-cost sensing platform that is capable of tracking a user indoors, whilst additionally also determining specific body positions [54]. The incorporation of the sensing platform, the ability to extract information, from distributed resources, and the QRS Complex detection algorithm and RRI calculation method enables a better-rounded representation of our lives to be constructed.…”

Section: Discussionmentioning

confidence: 99%

“…MoDM is capable of tracking a user's outside movements by using location and camera services. However, for indoor localisation, a sensor network has also been developed [54] that provides location tracking to room precision. The purpose of this was so that data, for the memory boxes, could start being collected in both indoor and outdoor situations.…”

Section: Methodsmentioning

confidence: 99%

“…Previous work [53] focused on only collecting outdoor information; therefore, when the user went inside the HDM stopped and there were significant gaps in their day. To overcome this challenge a wireless [54]. By incorporating this platform into DigMem, lifelogging can occur in both indoor and outdoor environments.…”

Section: Fig 3 Process Of Creating a Hdm Using Digmemmentioning

confidence: 99%

“…This aspect of the system is used to gather information in an outside environment. Physiological data will be gathered using our custom built sensor system [54]. This system works indoors and, using sensor networks, is able to track the location of people inside of their home and to determine specific body positions.…”

Section: Fig 3 Process Of Creating a Hdm Using Digmemmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting linked data to create rich human digital memories

Dobbins

Merabti

Fergus

et al. 2013

Computer Communications

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Fig 3 Process Of Creating a Hdm Using Digmemmentioning

confidence: 99%

Section: Fig 3 Process Of Creating a Hdm Using Digmemmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting linked data to create rich human digital memories

Dobbins

Merabti

Fergus

et al. 2013

Computer Communications

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“…For example, we might be more inactive in the winter, due to a drop in temperature, or during the Olympics, more sport was being played. Using these collected memories, our health can also be monitored [34][35][36][37]. Markers for an increase in sedentary behaviour can be identified.…”

Section: Discussionmentioning

confidence: 99%

Augmenting human digital memories with physiological data

Dobbins

Merabti

Fergus

et al. 2012

2012 IEEE 3rd International Conference on Networked Embedded Systems for Every Application (NESEA)

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Abstract-The area of human digital memories focuses on documenting our entire lifetime. Using this outlet, a diverse range of information can be brought together, such as photos, location, temperature and physiological information. Nowadays, we have access to a number of different data sources, thus allowing more dynamic and data rich memories to be created. In particular, the inclusion of physiological data offers a new insight into the augmentation of memories and provides a richer level of detail. This information can be used to determine how we were feeling, at any time, and, potentially, how we made others feel as well. Memories, created over a lifetime, can be retrieved, and we can see how our bodies have changed over time. This paper presents the DigMem system, which incorporates physiological data into the creation of human digital memories. A prototype has been successfully developed, which demonstrates the approach and evaluates the applicability of the research.

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Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research

Spruijt‐Metz

Hekler

Saranummi

et al. 2015

Behav. Med. Pract. Policy Res.

194

118

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Adverse and suboptimal health behaviors and habits are responsible for approximately 40 % of preventable deaths, in addition to their unfavorable effects on quality of life and economics. Our current understanding of human behavior is largely based on static "snapshots" of human behavior, rather than ongoing, dynamic feedback loops of behavior in response to ever-changing biological, social, personal, and environmental states. This paper first discusses how new technologies (i.e., mobile sensors, smartphones, ubiquitous computing, and cloud-enabled processing/computing) and emerging systems modeling techniques enable the development of new, dynamic, and empirical models of human behavior that could facilitate just-in-time adaptive, scalable interventions. The paper then describes concrete steps to the creation of robust dynamic mathematical models of behavior including: (1) establishing "gold standard" measures, (2) the creation of a behavioral ontology for shared language and understanding tools that both enable dynamic theorizing across disciplines, (3) the development of data sharing resources, and (4) facilitating improved sharing of mathematical models and tools to support rapid aggregation of the models. We conclude with the discussion of what might be incorporated into a "knowledge commons," which could help to bring together these disparate activities into a unified system and structure for organizing knowledge about behavior.

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Monitoring and measuring physical activity and sedentary behaviour

Cited by 5 publications

References 36 publications

Exploiting linked data to create rich human digital memories

Exploiting linked data to create rich human digital memories

Augmenting human digital memories with physiological data

Building new computational models to support health behavior change and maintenance: new opportunities in behavioral research

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