Emotion Detection through Smartphone's Accelerometer and Gyroscope Sensors

Abstract

Emotion recognition is essential for assessing human emotional states and predicting user behavior to provide appropriate and personalized feedback. The wide range of Smartphones with accelerometers, microphones, GPSs, gyroscopes, and more motivate researchers to explore the automatic emotion detection through Smartphone sensors. To this end, mobile sensing can facilitate the data retrieval process in a non-intrusive way without disturbing the user's experience. This study seeks to contribute to the field of non-intrusive mobile sensing for emotion recognition by detecting user emotions via accelerometer and gyroscope sensors in Smartphones. A prototype gaming app was designed and a sensor log app for Android OS was used to monitor the users’ sensor data while interacting with the game. The recorded data from 40 users was processed and used to train different classifiers for two emotions: a positive (enjoyment) and a negative (frustration) one. The validation study demonstrates a high prediction of 87.90% for enjoyment and 89.45% for frustration. Our findings indicate that by analyzing accelerometer and gyroscope data, it is possible to make efficient predictions of a user's emotional state. The proposed model and its empirical development and validation are described in this paper.