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The proposed model and its empirical development and validation are described in this paper. Our findings indicate that by analyzing accelerometer and gyroscope data, it is possible to make efficient predictions of a user's emotional state. The validation study demonstrates a high prediction of 87.90% for enjoyment and 89.45% for frustration. 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. 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. 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. To this end, mobile sensing can facilitate the data retrieval process in a non-intrusive way without disturbing the user's experience. The wide range of Smartphones with accelerometers, microphones, GPSs, gyroscopes, and more motivate researchers to explore the automatic emotion detection through Smartphone sensors. Emotion recognition is essential for assessing human emotional states and predicting user behavior to provide appropriate and personalized feedback.

Accelerometer gyroscope