Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

By Emiliano Miluzzo, Nicholas Donald Lane, K. Fodor, Ronald A. Peterson, Hong Lu, Mirco Musolesi, Shane B. Eisenman, Xiao Zheng, Andrew T. Campbell. ACM International Conference on Embedded Networked Sensor Systems, 2008

1,219 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Transportation

View Paper: https://www.semanticscholar.org/paper/eadfeaf5e0a3947029a0a373aa1f923ad83454fe

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

In Computer Vision and Pattern Recognition

In Transportation

Citations in the corpus

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “collected data is sent to the backend [59].”
The Smartphone Psychology Manifesto (2012, background): “…can reveal whether people are standing still or walking, exercising or dancing, feeling calm or nervous, and so on (Ermes, Parkka, Mantyjarvi, & Korhonen, 2008; Miluzzo et al., 2008); barometers (now sensitive to about ±0.5 m in altitude) can reveal whether people are walking up stairs or down.”
Mercury: a wearable sensor network platform for high-fidelity motion analysis (2009, methodology): “In recent years there has been increased interest in body sensor networks for wearable applications as diverse as elder care [12, 46], emergency response [28, 9], studying athletic performance [1, 32], gait analysis [38, 40, 44], and activity classification [18, 33].”
Accelerometer-based transportation mode detection on smartphones (2013, background): “A number of early systems used the embedded accelerometer for detecting different pedestrian and non-motorised modalities, such as walking and running [20, 12], ascending or descending stairs [4] or cycling [2].”
Activity recognition using cell phone accelerometers (2011, background): “Additional studies have similarly focused on how one can use a variety of accelerometerbased devices to identify a range of user activities [4-7, 9-16, 21].”
Tapprints: your finger taps have fingerprints (2012, background): “Although TapPrints should be only deemed as an early step towards the use of mobile devices’ motion sensors for tap and letter inference, it exposes a latent security threat: An attacker could silently tap into the motion sensor data stream using a background running application with little effect o”
The BikeNet mobile sensing system for cyclist experience mapping (2007, background): “For example, our initial experience [Miluzzo et al. 2008a] with the Nokia N95 is in line with that of other researchers [Gaonkar et al.”
Understanding the Internet of Things: definition, potentials, and societal role of a fast evolving paradigm (2017, neutral): “A similar approach is proposed in the CenceMe application [62], which focuses on combining information on the presence of individuals obtained through off-the-shelf sensor-enabled mobile phones, with the user proﬁle in social networking platforms such as Facebook and MySpace.”
Measuring Large-Scale Social Networks with High Resolution (2014, methodology): “A large number of solutions for sensor-driven human data collection have been developed, ranging from dedicated software to complete platforms, notably ContextPhone [20], SocioXensor [21], MyExpe-rience [22], Anonysense [23], CenceMe [24], Cityware [25], Darwin phones [26], Vita [27], and Context-To”
Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors (2016, methodology): “They are implemented on mobile phones in various studies; i.e. , Naive Bayes in [37–39], decision tree in [40–42] and KNN in [31,32,39,43].”
StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones (2014, background): “There is a growing interest in using smartphone sensing [31, 9, 12, 11, 40, 8, 15] to infer human dynamics and behavioral health [7, 34, 25, 21, 18, 23, 35, 28, 30].”
Design of an intelligible mobile context-aware application (2011, positive): “While similar to CenseMe [13], it uses a slightly different set of contexts, aggregates them into an availability context through rules (rather than just showing all contexts as presence information), and is intelligible, such that it can explain its complex behavior.”
A survey of mobile phone sensing (2010, neutral): “For example, accelerometer data is capable of characterizing the physical movements of the user carrying the phone [2].”
Privacy behaviors of lifeloggers using wearable cameras (2014, background): “Researchers have examined lifelogging systems from a variety of perspectives [6, 18, 29], including how they may affect society [2], their potential for enhancing memory retention for people with memory handicaps [16], and their use in tracking infants’ milestones [15].”
Harnessing Context Sensing to Develop a Mobile Intervention for Depression (2011, background): “Although used for social networking rather than health care purposes, the CenceMe mobile phone application has achieved promising accuracy rates within a small sample in predicting socially relevant states such as whether the user was conversing [21].”
Cellular traffic offloading through opportunistic communications: a case study (2010, background): “…Ernst-Reuter-Platz 7 10587 Berlin, Germany Guanhong Pei Department of Computer Science and Virginia Bioinformatics Institute Virginia Tech Blacksburg, VA 24061, USA V. S. Anil Kumar Department of Computer Science and Virginia Bioinformatics Institute Virginia Tech Blacksburg, VA 24061,…”