Abstract
Developing an approach to predict happiness based on individual conditions and actions could enable us to select daily behaviors for enhancing well-being in life. Therefore, we propose a novel approach of applying machine learning, a branch of the field of artificial intelligence, to a variety of information concerning people’s lives (i.e., a lifelog). We asked a participant (a healthy young man) to record 55 lifelog items (e.g., positive mood, negative events, sleep time etc.) in his daily life for about eight months using smartphone apps and a smartwatch. We then constructed a predictor to estimate the degree of happiness from the multimodal lifelog data using a support vector machine, which achieved 82.6% prediction accuracy. This suggests that our approach can predict the behaviors that increase individuals’ happiness in their daily lives, thereby contributing to improvement in their happiness. Future studies examining the usability and clinical applicability of this approach would benefit from a larger and more diverse sample size.
Highlights
Happiness has been considered an important factor for healthy human lives
We propose a novel approach of applying machine learning, a branch of the field of artificial intelligence, to a variety of information concerning people’s lives
To examine the usefulness of this approach, we constructed a predictor to estimate the degree of happiness from lifelog data using a support vector machine (SVM) (Vapnik, 1995), a specific algorithm of supervised machine learning
Summary
Tanto, proponemos un novedoso enfoque de aplicación del aprendizaje automático, una rama del campo de la Inteligencia Artificial, a una variedad de información de la vida de las personas (es decir, un lifelog). Se le pidió a un participante (un sujeto joven sano) que registrara 55 elementos de lifelog (por ejemplo, humor positivo, eventos negativos, tiempo de sueño etc.) en su vida diaria, durante aproximadamente ocho meses, usando aplicaciones de teléfonos inteligentes, y un reloj inteligente. Posteriormente, construimos un predictor para estimar el grado de felicidad, a partir de los datos lifelog multimodales, utilizando un equipo de vectores de soporte, que logró una precisión de predicción del 82.6%. Estos datos sugieren que nuestro enfoque, puede predecir los comportamientos que incrementan la felicidad de las personas en su vida diaria, contribuyendo así, a una mejora en su felicidad. Palabras-clave: aprendizaje automático, log de vida, inteligencia artificial, felicidad
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