TY - JOUR
T1 - Potential of a smartphone as a stress-free sensor of daily human behaviour
AU - Mimura, Koki
AU - Kishino, Hirohisa
AU - Karino, Genta
AU - Nitta, Etsuko
AU - Senoo, Aya
AU - Ikegami, Kentaro
AU - Kunikata, Tetsuya
AU - Yamanouchi, Hideo
AU - Nakamura, Shun
AU - Sato, Kan
AU - Koshiba, Mamiko
N1 - Funding Information:
We express our appreciation to Dr. Kenichiro Shimatani for his support in statistical mathematics. We also thank Dr. Yuka Shirakawa, Mr. Shimpei Ozawa, Ms. Saya Obara, Ms. Hitomi Sekihara, Mr. Yuta Fukushima, Mr. Takeshi Sagawa, and Ms. Natsuki Nojima of Tokyo University of Agriculture and Technology for their support. We thank Mr. Michael Luke Santamaria for his assistance in finalizing our manuscript. This work was supported mainly by JST-ALCA (2012–2013), and was partially supported by MEXT and JSPS KAKENHI ( 21200017 , 25119509 , 25282221 ), JSPS Research Fellow , DC2 -PD (2012–2014) in Japan.
Publisher Copyright:
© 2014 Elsevier B.V.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
KW - Accelerometer
KW - Geomagnetic sensor
KW - Gyroscope
KW - Seasonal modulation
KW - Thermal psychology
KW - Work efficiency
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U2 - 10.1016/j.bbr.2014.06.007
DO - 10.1016/j.bbr.2014.06.007
M3 - Article
C2 - 24933187
AN - SCOPUS:84908604132
SN - 0166-4328
VL - 276
SP - 181
EP - 189
JO - Behavioural Brain Research
JF - Behavioural Brain Research
ER -