Recognizing Human Activities from Smartphone Sensor Signals
Arindam Ghosh, Giuseppe Riccardi · 2014
In context-aware computing, Human Activity Recognition (HAR) aims to understand the current activity of users from their connected sensors. Smartphones with their various sensors are opening a new frontier in building human-centered applications for understanding users' personal and world contexts. While in-lab and controlled activity recognition systems have yielded very good results, they do not perform well under in-the-wild scenarios. The objective of this paper is to 1) Investigate how audio signal can complement and improve other on-board sensors (accelerometer and gyroscope) for activity recognition; 2) Design and evaluate the fusion of such multiple signal streams to optimize performance and sampling rate. We show that fusion of these signal streams, including audio, achieves high performance even at very low sampling rates; 3) Evaluate the performance of the multi-stream human activity recognition under different real end-user activity conditions.