Aristotle Said "Happiness is a State of Activity" -- Predicting Mood through Body Sensing with Smartwatches

TL;DR

This study uses smartwatch sensor data to predict mood states like Happiness and Activation, revealing correlations with physical activity, environmental factors, and personality traits, and aims to develop automated mood tracking.

Contribution

It introduces a novel method combining smartwatch sensors and personality data to predict mood states, advancing automated mood monitoring systems.

Findings

Happiness and Activation are negatively correlated with heart rate and light levels.

People are happier during weekends and when moving more.

Personality traits influence mood and can help improve prediction models.

Abstract

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people's geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies.