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Activity Recognition in the Home Setting Using Simple and Ubiquitous Sensors
There are different approaches to human activity recognition.
Some of them involve the analysis of complex sensor signals such as video from cameras in computer vision and
audio from microphones in auditory scene analysis.
Activity recognition from these sensors is challenging not only because of the complexity in analizyng the signals (feature extraction) and the complexity involved in the pattern recognition and machine learning algorithms used (especially for real-ime performance). Furthermore, cameras and microphones are usually perceived as invasive by people and they don't want them installed in their homes. The objective of this experiment/thesis is to decompose human activities as a sequence of binary sensor activations by installing sensors that sense when everyday objects are being moved or used.
In this experiment, between 77 and 84 sensor data collection boards equipped with reed switch sensors where installed in two single-person apartments collecting data about human activity for two weeks. The sensors were installed in everyday objects such as drawers, refrigerators, containers, etc. to record opening-closing events (activation deactivation events) as the subject carried out everyday activities.
Some quick projects ideas to get you started thinking about the problem
1). Running different algorithms to recognize activities
2).Cluster the sensor activations to predict possible activities
3). Measure changes of human behavior from day to day (what is the right distance measure to use?)