UC San Diego
Classifying Human Behaviors, Activities and Contexts from Mobile Sensor Data
- Author(s): Ellis, Katherine
- Advisor(s): Lanckriet, Gert
- et al.
This dissertation concerns applications of machine learning to time series classification. In particular, it investigates methods for classifying human behaviors, activities, and contexts from mobile sensor data. First, it presents a system for classifying five behaviors (sitting, standing, walking/running, riding in a vehicle, and bicycling) from body-worn accelerometers and GPS devices. The advantages and disadvantages of placing the accelerometer on the subjects wrist or hip are discussed. The system is based on a two-level classifier consisting of a random forest and hidden Markov model. Second, it presents a system for automatic classification of a variety of rich context labels using the sensors built into mobile phones and smartwatches. This system uses multilabel classification and investigates several methods of sensor fusion.