A framework of energy efficient mobile sensing for automatic user state recognition

By Yi Wang, Jialiu Lin, Murali Annavaram, Quinn Jacobson, Jason I. Hong, Bhaskar Krishnamachari, Norman M. Sadeh. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2009

523 citations recorded by the source corpus.

Subfields: Computer Science Applications, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/05a8d344c5ee924b8137f532d6900e3de6b6833d

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In Computer Science Applications

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

Citations in the corpus

Activity recognition with smartphone sensors (2014, methodology): “Reference [34] uses decision tree to build the hierarchical classification model for background sound analysis.”
Harnessing Context Sensing to Develop a Mobile Intervention for Depression (2011, background): “Adopting duty cycling techniques, which manage the sleep cycle of sensors to balance the sensing frequency and battery consumption, will promote technical efficiency [49].”
A Survey of Wearable Devices and Challenges (2017, background): “[210] proposed an energy efficient mobile sensing system (EEMSS) which not only”
A Survey of Online Activity Recognition Using Mobile Phones (2015, methodology): “In our analysis, we found two out of 30 studies [41,42] that considered such a dynamic and adaptive approach for sensor selection in their implementations.”
SurroundSense: mobile phone localization via ambience fingerprinting (2009, background): “Independent research is in progress on energyefficient localization and sensing [6,15,29] – we believe SurroundSense will benefit from these works.”
SensLoc: sensing everyday places and paths using less energy (2010, background): “EEMSS employs low power sensors to detect user states and context, and triggers high power sensors only when necessary [32].”
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research (2010, background): “Recently, EEMSS, an energy efficient system for user activity recognition, has been presented in [30].”
Improving energy efficiency of location sensing on smartphones (2010, background): “Many approaches have been proposed to combine the in formation obtained from sensors including Bluetooth, acceler om ter, audio, camera and GPS [16,20,24,28,35].”
Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof (2012, background): “, for specific applications on mobile systems [56, 57], for a specific protocol [58, 59], via offloading [39, 40], and via delaying communication [60].”
Energy-efficient rate-adaptive GPS-based positioning for smartphones (2010, background): “Our measurements of the power consumption1 (shown in Figure 2) agrees with the results published by others [26, 3] and confirms that the internal GPS on Nokia N95 smartphones uses around 0.”
A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems (2020, background): “studied user activity recognition via the physical data [9].”
The Smartphone Psychology Manifesto (2012, methodology): “Limited battery power may constrain how many hours per day a psych app can gather data, especially for energy-hungry GPS sampling, so psych apps need to maximize energy efficiency through context-aware sampling rates and logging intervals and through piggybacking on data already logged by the smartp”
A survey of mobile phone sensing (2010, unknown): “Other techniques rely on adopting a variety of duty cycling techniques that manage the sleep cycle of sensing components on the phone in order to trade off the amount of battery consumed against sensing fidelity and latency [27].”