The Mobile Sensing Platform: An Embedded Activity Recognition System

By Tanzeem Choudhury, Gaetano Borriello, Sunny Consolvo, Dirk Hähnel, Beverly L. Harrison, Bruce Hemingway, Jeffrey Hightower, Predrag V. Klasnja, Karl Koscher, Anthony LaMarca and 6 more. IEEE pervasive computing, 2008

649 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/672f12e4ee015308a240f32d6766fccfabd75bfc

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

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Citations in the corpus

Healthcare in the Pocket: Mapping the Space of Mobile-Phone Health Interventions (2011, positive): “UbiFit [8] [28] uses the Mobile Sensing Platform [45], a pager-sized wearable sensing device , to automatically detect walking, running, biking, using a stair machine, and using an elliptical trainer.”
A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “For instance, the values from air pressure and light intensity are helpful to determine whether the individual is outdoors or indoors [52].”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “For example, it would be burdensome, expensive, and time-consuming, if not impossible, to i g a every minute of GPS or accelerometry data collected throughout a day (Chapelle et al. 2006).”
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].”
The Smartphone Psychology Manifesto (2012, methodology): “Future smartphones could easily incorporate digital sensors for temperature, humidity, infrared light, ultraviolet light, ionizing radiation, carbon dioxide, and pollution, giving more insight into how such environmental variables influence, and are influenced by, behavior (Choudhury et al., 2008; H”
Sensor-Based Activity Recognition (2012, background): “HUMAN activity recognition is one of the most promising research topics for a variety of areas, including pervasive and mobile computing [1], [2], surveillance-based security [3]–[10], context-aware computing [41], [83], [152] and ambient assistive living [76], [93], [103], [126], [146].”
Activity recognition using cell phone accelerometers (2011, methodology): “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].”
Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016, background): “On one hand, ubiquitous computing tasks have extensively exploited different physical sensors, including wearable sensors (Lara and Labrador 2013), GPS traces (Lin and Hsu 2014), WiFi (Wang et al. 2014), barometer, temperature, and humidity sensors (Choudhury et al. 2008), etc.”
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “Choudhury et al. [17] designed a system to address the requirements for a practical context recognition system, including unobtrusive lightweight devices, long battery life and multi-modal sensing.”
Tracking Depression Dynamics in College Students Using Mobile Phone and Wearable Sensing (2018, background): “In [59] the authors use an early mobile sensing platform device [16] equipped with multiple embedded sensors to track a 8 older adults living in a continuing care retirement community.”