SensorApp: A Digital Mirror Personality Recognition with Deep Learning Using a Mobile Usage Dataset
Keywords:
Personality Recognition, Mobile Sensors, Accelerometer and Gyroscope data, CNN, Deep Learning, LSTM, MLP, TransformerAbstract
Automatic personality recognition has become relevant due to the advancement of artificial intelligence and the widespread use of mobile devices. This study proposes predicting personality traits according to the OCEAN model by integrating mobile sensor data and self-reports using deep neural networks. Through a mobile application, sensor data and daily surveys related to device usage were collected. Four model architectures (MLP, CNN, LSTM and Transformer) were evaluated, finding that CNN is most effective with raw data, while the Transformer excels at including temporal and frequential attributes. These results represent a breakthrough in customized and empathic technologies in mobile data-based personality recognition.
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