Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Dealing with sensor displacement in motion-based onbody activity recognition systems
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Using acceleration signatures from everyday activities for on-body device location
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
On-body device localization for health and medical monitoring applications
PERCOM '11 Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications
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Activity recognition along with device position recognition can provide contextual cues suitable to infer user interruptibility and device accessibility. Our system fuses data from accelerometer and multiple light sensors to classify activities and device positions. Previously published results achieve robust activity recognition performance with multiple sensors attached to fixed body positions, a model suitable for use cases such as healthcare and fitness. We achieve comparable activity recognition performance using smartphones placed in unknown on-body positions including pocket, holster and hand. Results obtained from a diverse data set show that motion state and device position are classified with macro-averaged f-scores 92.6% and 66.8% respectively, over six activities and seven device positions. We demonstrate the performance of our classifier with an implementation running on the Android platform, that visitors can try out.