Spine versus Porcupine: A Study in Distributed Wearable Activity Recognition
ISWC '04 Proceedings of the Eighth International Symposium on Wearable Computers
An RFID based system for monitoring free weight exercises
Proceedings of the 6th ACM conference on Embedded network sensor systems
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Tracking free-weight exercises
UbiComp '07 Proceedings of the 9th international conference on Ubiquitous computing
ADACEM: automatic daily activity and calorie expenditure monitor on mobile phones
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
An event-based BSN middleware that supports seamless switching between sensor configurations
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Wireless sensor networks in the wild: three practical issues after a middleware deployment
Proceedings of the 6th International Workshop on Middleware Tools, Services and Run-time Support for Networked Embedded Systems
An event-based BSN middleware that supports seamless switching between sensor configurations
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Adaptive gym exercise counting for myHealthAssistant: poster abstract
Proceedings of the 6th International Conference on Body Area Networks
Will you have a good sleep tonight?: sleep quality prediction with mobile phone
Proceedings of the 7th International Conference on Body Area Networks
Exercise repetition detection for resistance training based on smartphones
Personal and Ubiquitous Computing
Fast, Accurate Event Classification on Resource-Lean Embedded Sensors
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Proper running posture guide: a wearable biomechanics capture system
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
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This paper presents a novel fitness and preventive health care system with a flexible and easy to deploy platform. By using embedded wearable sensors in combination with a smartphone as an aggregator, both daily activities as well as specific gym exercises and their counts are recognized and logged. The detection is achieved with minimal impact on the system's resources through the use of customized 3D inertial sensors embedded in fitness accessories with built-in pre-processing of the initial 100Hz data. It provides a flexible re-training of the classifiers on the phone which allows deploying the system swiftly. A set of evaluations shows a classification performance that is comparable to that of state of the art activity recognition, and that the whole setup is suitable for daily usage with minimal impact on the phone's resources.