Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous 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
On using existing time-use study data for ubiquitous computing applications
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
What did you do today?: discovering daily routines from large-scale mobile data
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Methodologies for Continuous Cellular Tower Data Analysis
Pervasive '09 Proceedings of the 7th International Conference on Pervasive Computing
Using rhythm awareness in long-term activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
GroupUs: Smartphone Proximity Data and Human Interaction Type Mining
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Unsupervised Activity Recognition with User's Physical Characteristics Data
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Combining wearable and environmental sensing into an unobtrusive tool for long-term sleep studies
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Prior knowledge of human activities from social data
Proceedings of the 2013 International Symposium on Wearable Computers
Using time use with mobile sensor data: a road to practical mobile activity recognition?
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
Human activity recognition using social media data
Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia
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Wearable sensing systems, through their proximity with their user, can be used to automatically infer the wearer's activity to obtain detailed information on availability, behavioural patterns and health. For this purpose, classifiers need to be designed and evaluated with sufficient training data from these sensors and from a representative set of users, which requires starting this procedure from scratch for every new sensing system and set of activities. To alleviate this procedure and optimize classification performance, the use of time use surveys has been suggested: These large databases contain typically several days worth of detailed activity information from a large population of hundreds of thousands of participants. This paper uses a strategy first suggested by [16] that utilizes time use diaries in an activity recognition method. We offer a comparison of the aforementioned North-American data with a large European database, showing that although there are several cultural differences, certain important features are shared between both regions. By cross-validating across the 5160 households in this new data with activity episodes of 13798 individuals, especially distinctive features turn out to be time and participant's location. Additionally, we identify for 11 different activities which features are most suited to be used for later on activity recognition.