Feedback Control of Computing Systems
Feedback Control of Computing Systems
DJogger: a mobile dynamic music device
CHI '06 Extended Abstracts on Human Factors in Computing Systems
MPTrain: a mobile, music and physiology-based personal trainer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Automatic playlist generation based on tracking user's listening habits
Multimedia Tools and Applications
A compact, high-speed, wearable sensor network for biomotion capture and interactive media
Proceedings of the 6th international conference on Information processing in sensor networks
The Mobile Sensing Platform: An Embedded Activity Recognition System
IEEE Pervasive Computing
TripleBeat: enhancing exercise performance with persuasion
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
BALANCE: towards a usable pervasive wellness application with accurate activity inference
Proceedings of the 10th workshop on Mobile Computing Systems and Applications
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Heartphones: Sensor Earphones and Mobile Application for Non-obtrusive Health Monitoring
ISWC '09 Proceedings of the 2009 International Symposium on Wearable Computers
Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
An intelligent music playlist generator based on the time parameter with artificial neural networks
Expert Systems with Applications: An International Journal
Cooperative transit tracking using smart-phones
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Hijacking power and bandwidth from the mobile phone's audio interface
Proceedings of the First ACM Symposium on Computing for Development
An integrated music recommendation system
IEEE Transactions on Consumer Electronics
Septimu2 - earphones for continuous and non-intrusive physiological and environmental monitoring
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
Poster abstract: a mobile-cloud service for physiological anomaly detection on smartphones
Proceedings of the 12th international conference on Information processing in sensor networks
Auditeur: a mobile-cloud service platform for acoustic event detection on smartphones
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
Leveraging biosignal and collaborative filtering for context-aware recommendation
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
bHealthy: a physiological feedback-based mobile wellness application suite
Proceedings of the 4th Conference on Wireless Health
Proxemic interaction in a multi-room music system
Proceedings of the 25th Australian Computer-Human Interaction Conference: Augmentation, Application, Innovation, Collaboration
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MusicalHeart is a biofeedback-based, context-aware, automated music recommendation system for smartphones. We introduce a new wearable sensing platform, Septimu, which consists of a pair of sensor-equipped earphones that communicate to the smartphone via the audio jack. The Septimu platform enables the MusicalHeart application to continuously monitor the heart rate and activity level of the user while listening to music. The physiological information and contextual information are then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate. We provide empirical evidence that the measured heart rate is 75% -- 85% correlated to the ground truth with an average error of 7.5 BPM. The accuracy of the person-specific, 3-class activity level detector is on average 96.8%, where these activity levels are separated based on their differing impacts on heart rate. We demonstrate the practicality of MusicalHeart by deploying it in two real world scenarios and show that MusicalHeart helps the user achieve a desired heart rate intensity with an average error of less than 12.2%, and its quality of recommendation improves over time.