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  1. Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments (HealthNet '08)
  2. Hassle free fitness monitoring
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Mobile computing in healthcare: the dreams and wishes of clinicians
Augmenting motion sensing to improve detection of periods of unusual inactivity
SPA: a smart phone assisted chronic illness self-management system with participatory sensing
Human identification by gait analysis
Reducing energy consumption of multi-channel mobile medical monitoring algorithms
Body sensor networks based sensor fusion for cardiovascular biosignal predictions
Hassle free fitness monitoring
Ayushman: a secure, usable pervasive health monitoring system
Data-centric support of a smart walker in a ubiquitous-computing environment
Body sensor networks to evaluate standing balance: interpreting muscular activities based on inertial sensors
SmartBuckle: human activity recognition using a 3-axis accelerometer and a wearable camera
Exploring early evaluation techniques of ambient health promoting devices in home environments of senior citizens living independently
Ubiquitous personal assistive system for neuropathy
Smartphone driven healthcare system for rural communities in developing countries
Using lifelogging to support recollection for people with episodic memory impairment and their caregivers
Towards a covered and connected body sensor network for healthcare applications
Efficient long-term quality-of-inference (QoINF)-aware context determination in pervasive care environments

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Hassle free fitness monitoring

Content Provider ACM Digital Library
Author Schmid, Thomas Jea, David Liu, Jason Srivastava, Mani
Abstract Fitness monitoring is a fundamental service in pervasive healthcare, but finding a balance between usability and privacy is a hard challenge. To lessen users' anxieties in privacy concerns, we propose a new way of identification by only utilizing imprecise biometrics and existing information. Our solution is "hassle free" because it maintains the devices' original user interface without adding additional sensors and sacrificing user privacy. We demonstrate this idea with a fitness monitoring system for the healthy individuals in a workplace. The system uses collected physiological information (weight, blood pressure and heart rate) and context information (computer network activity) to identify a user. Our experiments show that we can achieve a correct user identification of up to 87%. We believe that our solution can serve as an easy addition to the simple interfaces of current technology by enhancing them with smart algorithms.
Starting Page 1
Ending Page 3
Page Count 3
File Format PDF
ISBN 9781605581996
DOI 10.1145/1515747.1515756
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2008-06-17
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Privacy Healthcare Imprecise biometrics
Content Type Text
Resource Type Article
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