Context awareness via a single device-attached accelerometer during mobile computing
Proceedings of the 7th international conference on Human computer interaction with mobile devices & services
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
An activity recognition system for mobile phones
Mobile Networks and Applications
Eye movement analysis for activity recognition
Proceedings of the 11th international conference on Ubiquitous computing
Recognizing daily activities with RFID-based sensors
Proceedings of the 11th international conference on Ubiquitous computing
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The present document sheds light on a system that recognizes the activities performed by an individual carrying a mobile phone with a built-in accelerometer in the right front pocket of the pants. This accelerometer is used to gather/collect data from certain activities previously established. An analysis of certain learning algorithms is presented as well, containing decision trees, Bayesian method, decision rules, and SVM (Support Vector Machine), all aimed at finding which is better for the recognition of the activities chosen. In addition, certain unsupervised machine learning techniques were used for the analysis of data and actions selected. Results were very favorable, and exhibited a clear distinction of the four (4) types of activities recognized.