A context aware system for personalized services using wearable biological signal sensors

Kang, Dong‐oh; Ha, Kiryong; Lee, Jeonwoo

doi:10.1109/iccas.2008.4694618

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Determining user preferences or interests is increasingly done through inference by observing, for instance, the services chosen or places visited [107], [113], [122], users' web browsing activities, documents viewed, applications used [113], and stress levels [112], and by exploring users' social network profiles [127], social web content [51], social ties [122], and the content of e-mails and calendars [113]. However, also explicit inputs from users are widely used, e.g., through user ratings [107], [111], [122], [123].…”

Section: ) Personal Profiles In Existing Systemsmentioning

confidence: 99%

Rethinking Health: ICT-Enabled Services to Empower People to Manage Their Health

Honka

Kaipainen

Hietala

et al. 2011

IEEE Rev. Biomed. Eng.

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Lifestyle is a key determinant in the prevention and management of chronic diseases. If we would exercise regularly, eat healthy, control our weight, sleep enough, manage stress, not smoke and use alcohol only moderately, 90% of type II diabetes, 80% of coronary heart disease, and 70% of stroke could be prevented. Health statistics show that lifestyle related diseases are increasing at an alarming rate. Public health promotion campaigns and healthcare together are not effective enough to stop this "tsunami". The solution that is offered is to empower people to manage their health with the assistance of ICT-enabled services. A lot of R&D and engineering effort is being invested in Personal Health Systems. Although some progress has been made, the market for such systems has not yet emerged. The aim of this critical review is to identify the barriers which are holding back the growth of the market. It looks into the theoretical foundations of behavior change support, the maturity of the technologies for behavior change support, and the business context in which behavior change support systems are used.

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Section: ) Personal Profiles In Existing Systemsmentioning

confidence: 99%

Rethinking Health: ICT-Enabled Services to Empower People to Manage Their Health

Honka

Kaipainen

Hietala

et al. 2011

IEEE Rev. Biomed. Eng.

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“…Using similar devices, Liu [7] monitored stress levels of cell phone users to detect the best moment to interrupt and provide them new information without increasing their stress levels. In a different study, Kang et al [5] measured the levels of stress of cell phone users when using several services in order to create a personalized and more optimal one. Our system, in contrast, uses the stressfulness of the responses to pair them with the events in a journal, and provide them to the users for their own usage and reflection.…”

Section: Related Workmentioning

confidence: 99%

Feel

Ayzenberg¹,

Rivera²,

Picard

2012

CHI '12 Extended Abstracts on Human Factors in Computing Systems

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This work proposes a system for the automatic annotation and monitoring of cell phone activity and stress responses of users. While mobile phone applications (e.g., e-mail, voice, calendar) are used to non-intrusively extract the context of social interactions, a non-intrusive and comfortable biosensor is used to measure the electrodermal activity (EDA). Then, custom stress recognition software analyses the streams of data in real-time and associates stress levels to each event. Both contextual data and stress levels are aggregated in a searchable journal where the user can reflect on his/her physiological responses.

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“…In a different study, Kang et al [15] measured the levels of stress of cell phone users when using several services in order to create a personalized and more optimal service. Intille et al [16] developed a system for context-aware experience sampling.…”

Section: B Context-aware Experience Samplingmentioning

confidence: 99%

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

Ayzenberg

Picard

2014

IEEE J. Biomed. Health Inform.

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Abstract-The wide availability of low-cost wearable biophysiological sensors enables us to measure how the environment and our experiences impact our physiology. This creates a challenge: in order to interpret the longitudinal data, we require the matching contextual information as well. Collecting continuous biophysiological data makes it unfeasible to rely solely on our memory for contextual information. In this paper, we first present an architecture and implementation of a system for the acquisition, processing, and visualization of biophysiological signals and contextual information. Next, we present the results of a user study: users wore electrodermal activity wrist sensors that measured their autonomic arousal. These users uploaded the sensor data at the end of each day. At first, they annotated their events at the end of each day; then, after a two-day break, they annotated the data from two days earlier. One group of users had access to both the signal and the contextual information collected by the mobile phone and the other group could only access the biophysiological signal. At the end of the study, the users filled in a system usability scale and user experience surveys. Our results show that the system enables the users to annotate biophysiological signals at a greater effectiveness than the current state of the art while also providing very good usability.

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A context aware system for personalized services using wearable biological signal sensors

Cited by 3 publications

References 7 publications

Rethinking Health: ICT-Enabled Services to Empower People to Manage Their Health

Rethinking Health: ICT-Enabled Services to Empower People to Manage Their Health

Feel

FEEL: A System for Frequent Event and Electrodermal Activity Labeling

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