Activity recognition from RFID sensor data
Sunday, September 25, 2005, 10:30am - Sunday, September 25, 2005, 12:00pm
This is a difficult problem; most approaches have such poor performance that they are unusable. If the activities of daily life (ADL) can be accurately recognized then all of the previous work involving planning, care giver notification, etc can be realized. This experiment will identify the activities of daily living (ADL) through data collected from RFID tags. This work will build upon the work of Philipose et al. That team created a RFID tag reader built into a glove. They then put RFID tags on many items in the house. In this way they are able to determine what items the person touched. To understand the stream of RFID tags encountered by the reader the team used a combination of natural language processing and a Bayesian network where the probabilities were derived from the web via the Google API. In this experiment the ADLs will be modeled using either XML or RDF. The stream of RFID data will be interpreted by a trained classifier. I will try a variety of different classifiers: Hidden Markov Models (HMMs), neural networks, learning bayes networks, genetic algorithms or learned decision trees. This training could create a personal ADL recognition system. The customization may improve the accuracy of an ADL recognition system.