Classification of Daily Activities Based on the Amount of Social and Physical Activity for Behavioral Change Toward Wellbeing
Article No.: 1, Pages 1 - 9
Abstract
In this study, we classify daily activities by focusing on the relationship between social and physical activities. By self-reflection through lifelogging, we aim to discover ways of spending and indicators that lead to physical and mental health, such as increased satisfaction and reduced fatigue. We classify daily activities based on the amount of physical activity obtained from a smartwatch and the amount of social activity obtained from a neck-worn face-counting device, and obtain insights into the relationship between social and physical activities. First, we conduct a hierarchical clustering of daily activities (8 types, 37 scenes, 10 minutes each) of an individual, which are then are classified into four groups. Then, the amount of physical activity and social activity are plotted on a two-dimensional plane to analyze the tendency. In addition, we plot the activities of a group of three participants on the same space and analyzed the tendency of each activity. Although there are differences in the amount of physical activity and social activity among the individuals, they tend to fall into the groups. Moreover, we visualize the transition of the amount of physical activity and social activity of one participant throughout a half-day. We are interested in the time-series rhythm and balance of the person’s activities. We believe that our visualization will suggest activities for individuals based on their state of physical and social (mental) health.
References
[1]
Barbara E Ainsworth, William L Haskell, Stephen D Herrmann, Nathanael Meckes, David R Bassett, Catrine Tudor-Locke, Jennifer L Greer, Jesse Vezina, Melicia C Whitt-Glover, and Arthur S Leon. 2011. 2011 Compendium of Physical Activities: a second update of codes and MET values. Med Sci Sports Exerc 43, 8 (2011), 1575–1581.
[2]
Barbara E Ainsworth, William L Haskell, Melicia C Whitt, Melinda L Irwin, Ann M Swartz, Scott J Strath, WILLIAM L O Brien, David R Bassett, Kathryn H Schmitz, Patricia O Emplaincourt, 2000. Compendium of physical activities: an update of activity codes and MET intensities. Medicine and science in sports and exercise 32, 9; SUPP/1 (2000), S498–S504.
[3]
Arduino. 2021. map() - Arduino Reference. https://www.arduino.cc/reference/en/language/functions/math/map/
[4]
Jeremy N Bailenson. 2021. Nonverbal overload: A theoretical argument for the causes of Zoom fatigue. Technology, Mind, and Behavior 2, 1 (2021).
[5]
Stuart JH Biddle and Mavis Asare. 2011. Physical activity and mental health in children and adolescents: a review of reviews. British journal of sports medicine 45, 11 (2011), 886–895.
[6]
Tanzeem Choudhury and Alex Pentland. 2003. Sensing and Modeling Human Networks Using the Sociometer. In Proceedings of the 7th IEEE International Symposium on Wearable Computers(ISWC ’03). IEEE Computer Society, Washington, DC, USA, 216–222. http://dl.acm.org/citation.cfm?id=946249.946901
[7]
Fitbit. 2018. Fitbit Versa Lite Edition. https://www.fitbit.com/jp/versa-lite
[8]
Fangfang Guo, Yu Li, Mohan S. Kankanhalli, and Michael S. Brown. 2013. An Evaluation of Wearable Activity Monitoring Devices. In Proceedings of the 1st ACM International Workshop on Personal Data Meets Distributed Multimedia(Barcelona, Spain) (PDM ’13). ACM, New York, NY, USA, 31–34. https://doi.org/10.1145/2509352.2512882
[9]
James S House, Karl R Landis, and Debra Umberson. 1988. Social relationships and health. Science 241, 4865 (1988), 540–545.
[10]
Ichiro Kawachi and Lisa F Berkman. 2001. Social ties and mental health. Journal of Urban health 78, 3 (2001), 458–467.
[11]
Nicholas D Lane, Mashfiqui Mohammod, Mu Lin, Xiaochao Yang, Hong Lu, Shahid Ali, Afsaneh Doryab, Ethan Berke, Tanzeem Choudhury, and Andrew Campbell. 2011. Bewell: A smartphone application to monitor, model and promote wellbeing. In 5th international ICST conference on pervasive computing technologies for healthcare, Vol. 10.
[12]
K. Nakamura, S. Yeung, A. Alahi, and L. Fei-Fei. 2017. Jointly Learning Energy Expenditures and Activities Using Egocentric Multimodal Signals. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6817–6826.
[13]
Akane Okuno and Yasuyuki Sumi. 2019. Social Activity Measurement by Counting Faces Captured in First-Person View Lifelogging Video. In Proceedings of the 10th Augmented Human International Conference 2019 (Reims, France) (AH2019). ACM, New York, NY, USA, Article 19, 9 pages. https://doi.org/10.1145/3311823.3311846
[14]
Akane Okuno and Yasuyuki Sumi. 2020. Lifelog Visualization Based on Social and Physical Activities. In Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers(Virtual Event, Mexico) (UbiComp-ISWC ’20). Association for Computing Machinery, New York, NY, USA, 94–97. https://doi.org/10.1145/3410530.3414377
[15]
Daniel Olguín, Benjamin N Waber, Taemie Kim, Akshay Mohan, Koji Ara, and Alex Pentland. 2009. Sensible organizations: Technology and methodology for automatically measuring organizational behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 39, 1(2009), 43–55.
[16]
Alessandro Ortis, Giovanni Maria Farinella, Valeria D’Amico, Luca Addesso, Giovanni Torrisi, and Sebastiano Battiato. 2016. Organizing Egocentric Videos for Daily Living Monitoring. In Proceedings of the First Workshop on Lifelogging Tools and Applications (Amsterdam, The Netherlands) (LTA ’16). ACM, New York, NY, USA, 45–54. https://doi.org/10.1145/2983576.2983578
[17]
Yoshitake Oshima, Kaori Kawaguchi, Shigeho Tanaka, Kazunori Ohkawara, Yuki Hikihara, Kazuko Ishikawa-Takata, and Izumi Tabata. 2010. Classifying household and locomotive activities using a triaxial accelerometer. Gait & Posture 31, 3 (2010), 370–374. https://doi.org/10.1016/j.gaitpost.2010.01.005
[18]
Sandipan Pal and Charith Abhayaratne. 2015. Video-based Activity Level Recognition for Assisted Living Using Motion Features. In Proceedings of the 9th International Conference on Distributed Smart Cameras (Seville, Spain) (ICDSC ’15). ACM, New York, NY, USA, 62–67. https://doi.org/10.1145/2789116.2789140
[19]
Frank J Penedo and Jason R Dahn. 2005. Exercise and well-being: a review of mental and physical health benefits associated with physical activity. Current opinion in psychiatry 18, 2 (2005), 189–193.
[20]
Robert R Sokal. 1958. A statistical method for evaluating systematic relationships.Univ. Kansas, Sci. Bull. 38 (1958), 1409–1438.
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Published In
May 2022
56 pages
ISBN:9781450396592
DOI:10.1145/3532525
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Published: 26 May 2022
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