Wake on wireless: an event driven energy saving strategy for battery operated devices
Proceedings of the 8th annual international conference on Mobile computing and networking
Turducken: hierarchical power management for mobile devices
Proceedings of the 3rd international conference on Mobile systems, applications, and services
Design of a Pressure Sensitive Floor for Multimodal Sensing
IV '05 Proceedings of the Ninth International Conference on Information Visualisation
SATIRE: a software architecture for smart AtTIRE
Proceedings of the 4th international conference on Mobile systems, applications and services
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Energy consumption in mobile phones: a measurement study and implications for network applications
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Location-Aware Fall Detection System for Medical Care Quality Improvement
MUE '09 Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering
Energy-efficient rate-adaptive GPS-based positioning for smartphones
Proceedings of the 8th international conference on Mobile systems, applications, and services
Mobile phone-based pervasive fall detection
Personal and Ubiquitous Computing
Your Floor Knows Where You Are: Sensing and Acquisition of Movement Data
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 02
Location-independent fall detection with smartphone
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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Mobile fall-detection systems that use accelerometers (as the ADXL 345) with data pre-processing capabilities, enable processors to remain longer in low power modes and therefore can achieve extended device lifetimes. Since fall-detection on these accelerometers is partially executed in hardware, the development and comparison of fall-detection algorithms requires direct evaluation on the hardware and increases complexity. We introduce a fall-detection simulator for the development and comparison of fall-detection algorithms for accelerometers with and without partial in-hardware pre-processing. In addition comprehensive records of fall-situations and daily living activities were generated for the simulator from recording movements. With the help of the simulator, the sensitivity of a given fall-detection algorithm could be improved from 33% to 93%.