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Human Activity recognition systems need sensor deployment to acquire activity information. How to make good use of smartphones as mobile sensing devices to develop a real-time recognition system for context-aware applications has become an active research topic in recent years. In this study, we implemented a mobile activity monitoring system and compared the performance of some well-known machine learning approaches when sensor data collected from practical smartphone users is limited. The evaluation is based on the records of accelerometer and gyroscope measurements collected from six different activities of daily living, including sitting, lying, standing, walking, going upstairs, and going downstairs.

Implementation of In-Home Activity Recognition Based on Web Service and Machine Learning Approaches