Behaviour is one of the most powerful objective signals that connotes psychological functions regulated by neuronal network systems. This study searched for simple behaviours using smartphone sensors with three axes for measuring acceleration, angular speed and direction. We used quantitative analytic methodology of pattern recognition for work contexts, individual workers and seasonal effects in our own longitudinally recorded data. Our 13 laboratory members were involved in the care of common marmosets and domestic chicks, which lived in separate rooms. They attached a smartphone to their front waist-belts during feeding and cleaning in five care tasks. Behavioural characteristics such as speed, acceleration and azimuth, pitch, and roll angles were monitored. Afterwards, participants noted subjective scores of warmth sensation and work efficiency. The multivariate time series behavioral data were characterized by the subjective scores and environmental factors such as room temperature, season, and humidity, using the linear mixed model. In contrast to high-precision but stress-inducing sensors, the mobile sensors measuring daily behaviours allowed us to quantify the effects of the psychological states and environmental factors on the behavioural traits.