Abstract
Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN, every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.
Similar content being viewed by others
References
Amazon Mechanical Turk. http://www.mturk.com
Google Nexus One. http://www.google.com/phone/detail/nexus-one
SF-36.org. A community for measuring health outcoming using SF tools. http://www.sf-36.org/tools/SF36.shtml
Amazon Elastic Cloud Computing. http://aws.amazon.com/ec2
Guo B, Zhang D, Yu Z, Zhou X (2012) Hybrid SN: interlinking opportunistic and online communities to augment information dissemination. In: Proceedings of the 9th IEEE international conference on ubiquitous intelligence and computing, UIC’12
Abdelzaher T, Anokwa Y, Boda P, Burke J, Estrin D, Guibas L, Kansal A, Madden S, Reich J (2007) Mobiscopes for human spaces. IEEE Pervasive Comput 6(2):20–29
Andoni A, Indyk P (2008) Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. Commun ACM 51(1):117–122
Ashbrook D, Starner T (2003) Using GPS to learn significant locations and predict movement across multiple users. Pers Ubiquitous Comput 7(5):275–286
Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New York
Burke J, Estrin D, Hansen M, Parker A, Ramanathan N, Reddy S, Srivastava MB (2006) Participatory sensing. In: Workshop on world-sensor-web: mobile device centric sensor networks and applications, WSW ’06, pp 117–134
Hattori Y, Inoue S, Hirakawa G. (2011) A large scale gathering system for activity data with mobile sensors. In: Proceedings of the 15th IEEE international symposium on wearable computers, (ISWC 2011)
Campbell AT, Eisenman SB, Lane ND, Miluzzo E, Peterson RA (2006) People-centric urban sensing. In: Proceedings of the 2nd annual international wireless internet conference, WICON ’06
Consolvo S, McDonald DW, Toscos T, Chen MY, Froehlich J, Harrison B, Klasnja P, LaMarca A, LeGrand L, Libby R, Smith I, Landay JA (2008) Activity sensing in the wild: a field trial of UbiFit Garden. In: Proceedings of the 26th annual SIGCHI conference on human factors in computing systems, CHI ’08, pp 1797–1806
Dipietro L, Caspersen C, Ostfeld A, Nadel E (1993) A survey for assessing physical activity among older adults. Med Sci Sports Exerc 25:628–628
Froehlich J, Dillahunt T, Klasnja P, Mankoff J, Consolvo S, Harrison B, Landay JA (2009) UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits. In: Proceedings of the 27th international conference on human factors in computing systems, CHI ’09, pp 1043–1052
Kapoor A, Horvitz E (2008) Experience sampling for building predictive user models: a comparative study. In: Proceedings of the 26th annual SIGCHI conference on human factors in computing systems, CHI ’08, pp 657–666
Lane ND, Xu Y, Lu H, Hu S, Choudhury T, Campbell AT, Zhao F (2011) Enabling large-scale human activity inference on smartphones using community similarity networks (CSN). In: Proceedings of the 13th international conference on ubiquitous computing, Ubicomp ’11, pp 355–364
Lane ND, Lu H, Eisenman SB, Campbell AT (2008) Cooperative techniques supporting sensor-based people-centric inferencing. In: Proceedings of the 6th international conference on pervasive computing, Pervasive ’08, pp 75–92
Lane ND, Xu Y, Lu H, Campbell AT, Choudhury T, Eisenman SB (2011) Exploiting social networks for large-scale human behavior modeling. Pervasive Comput 104:45–53
Lane ND, Mohammod M, Lin M, Yang X, Lu H, Ali S, Doryab A, Berke E, Choudhury T, Campbell AT (2011) BeWell: a smartphone application to monitor, model and promote wellbeing. In: Proceedings of the 5th international ICST conference on pervasive computing technologies for healthcare, Pervasive Health 11
Lester J, Choudhury T, Kern N, Borriello G, Hannaford B (2005) A hybrid discriminative/generative approach for modeling human activities. In: Proceedings of the international joint conference on artificial intelligence, IJCAI ’05, pp 766–772
Longstaff B, Reddy S, Estrin D (2010) Improving activity classification for health applications on mobile devices using active and semi-supervised learning. In: Proceedings of ICST conference on pervasive computing technologies for healthcare, PervasiveHealth ’10, pp 1–7
Lu H, Pan W, Lane ND, Choudhury T, Campbell AT (2009) Soundsense: scalable sound sensing for people-centric applications on mobile phones. In: Proceedings of the 7th international conference on mobile systems, applications, and services, Mobisys ’09, pp 165–178
Lu H, Yang J, Liu Z, Lane ND, Choudhury T, Campbell AT (2010) The Jigsaw continuous sensing engine for mobile phone applications. In: Proceedings of the 8th international conference on embedded networked sensor systems, Sensys ’10, pp 71–84
Mun M, Reddy S, Shilton K, Yau N, Burke J, Estrin D, Hansen M, Howard E, West R, Boda P (2009) PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th international conference on mobile systems, applications, and services, MobiSys ’09, pp 55–68
Oza NC, Russell S. (2001) Online bagging and boosting. In: Proceedings of the 8th international workshop on artificial intelligence and statistics, AISTAT ’01, pp 105–112
Miluzzo E, Oakley JM, Lu H, Lane ND, Peterson R, Campbell A. (2008) Evaluating the iPhone as a mobile platform for people-centric sensing applications. In: Proceedings of international workshop on urban, community, and social applications of networked sensing systems, UrbanSense 08
Peebles D, Lu H, Lane N, Choudhury T, Campbell A (2010) Community-guided learning: exploiting mobile sensor users to model human behavior. In: Proceedings of the 24th national conference on artificial intelligence, AAAI ’10, pp 1600–1606
Yan T, Kumar V, Ganesan D (2010) CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones. In: Proceedings of the 8th international conference on mobile systems, applications, and services, MobiSys ’10, pp 77–90
Rachuri KK, Musolesi M, Mascolo C, Rentfrow PJ, Longworth C, Aucinas A (2010) Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM international conference on ubiquitous computing, Ubicomp ’10, pp 281–290
Stikic M, Van Laerhoven K, Schiele B (2008) Exploring semi-supervised and active learning for activity recognition. In: Proceedings of the 12th IEEE international symposium on wearable computers, ISWC ’08, pp 81–88
von Ahn L, Maurer B, Mcmillen C, Abraham D, Blum M (2008) reCAPTCHA: human-based character recognition via web security measures. Science, pp 1465–1468
Rubner CTY, Guibas LJ (2000) The earth movers distance as a metric for image retrieval. IJCV 40(2):99–121
Zheng Y, Li Q, Chen Y, Xie X, Ma WY (2008) Understanding mobility based on GPS data. In: Proceedings of the 10th ACM international conference on ubiquitous computing, Ubicomp ’08, pp 312–321
Zheng Y, Zhang L, Xie X, Ma WY (2009) Mining interesting locations and travel sequences from GPS trajectories. In: Proceedings of the 18th international conference on world wide web, WWW ’09, pp 791–800
Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Morgan and Claypool, San Rafael
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lane, N.D., Xu, Y., Lu, H. et al. Community Similarity Networks. Pers Ubiquit Comput 18, 355–368 (2014). https://doi.org/10.1007/s00779-013-0655-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-013-0655-1