BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
MyLifeBits: fulfilling the Memex vision
Proceedings of the tenth ACM international conference on Multimedia
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Gait analyzer based on a cell phone with a single three-axis accelerometer
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
ML-KNN: A lazy learning approach to multi-label learning
Pattern Recognition
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Random k-Labelsets: An Ensemble Method for Multilabel Classification
ECML '07 Proceedings of the 18th European conference on Machine Learning
Daily Routine Classification from Mobile Phone Data
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
SurroundSense: mobile phone localization via ambience fingerprinting
Proceedings of the 15th annual international conference on Mobile computing and networking
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
MULAN: A Java Library for Multi-Label Learning
The Journal of Machine Learning Research
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Using smart mobile devices for monitoring in assistive environments
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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This paper presents a diary system which reduces the time to keep and maintain a diary by assisting the user with automatic suggestions for possible events and activities. The system uses a smartphone app reading the internal sensors to detect the current situation. It interrupts the user when unsure in prediction of the current situation. No external hardware is used to assure a broad audience of later endusers. The overall goal is to create a multimedia enriched digital personal diary. A study has been conducted where 16 participants collected data over two weeks from the built-in sensors of an ordinary smartphone. The data has been annotated by the users with labels of daily activities. We applied different classification algorithms to test the feasibility to detect these activity labels from the collected sensor data only. The experiments show that a perfect classification of activities of daily living is not possible but a system like this is usable to assist the journaling by suggestions and semi-automatic logging. The digital diary keeping process should take less time and is more versatile than an old-fashioned handwritten diary. A diary like this can be used to revive past events and help people with dementia to remember the past.