23 Papers
205 Citations
Kun Li is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Computer science & Wearable computer. The author has an hindex of 13, co-authored 20 publications.
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Papers
The next generation of low-cost personal air quality sensors for quantitative exposure monitoring
Ricardo Piedrahita,Yun Xiang,N. Masson,John Ortega,A. M. Collier,Jiang Yifei,Kun Li,Robert P. Dick,Qin Lv,Michael P. Hannigan,Li Shang +10 more
TL;DR: In this article, a quantification system was developed to convert the metal oxide semiconductor (MOx) sensor signals into concentrations, and two types of sensors were used to measure CO, O3, NO2, and total VOCs.
Hallway based automatic indoor floorplan construction using room fingerprints
Jiang Yifei,Yun Xiang,Xin Pan,Kun Li,Qin Lv,Robert P. Dick,Li Shang,Michael P. Hannigan +7 more
- 08 Sep 2013
TL;DR: This paper describes an automatic indoor floorplan construction system that is implemented as a mobile middleware, which allows emerging mobile applications to generate, leverage, and share indoor floorplans.
MAQS: a personalized mobile sensing system for indoor air quality monitoring
Jiang Yifei,Kun Li,Lei Tian,Ricardo Piedrahita,Xiang Yun,Omkar Mansata,Qin Lv,Robert P. Dick,Michael P. Hannigan,Li Shang +9 more
- 17 Sep 2011
TL;DR: This paper describes MAQS, a personalized mobile sensing system for IAQ monitoring that incorporates an accurate temporal n-gram augmented Bayesian room localization method that requires few Wi-Fi fingerprints and a zone-based proximity detection method for collaborative sensing, which saves energy and enables data sharing among users.
Data sensing and analysis: Challenges for wearables
James Williamson,Qi Liu,Fenglong Lu,Wyatt Mohrman,Kun Li,Robert P. Dick,Li Shang +6 more
- 12 Mar 2015
TL;DR: The energy challenges for wearable sensing technologies are described, with a primary focus on the most widely used wearable sensors: MEMS-based inertial measurement units: MEMs IMU data sensing, analysis, and wireless communication.
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Personalized driving behavior monitoring and analysis for emerging hybrid vehicles
Kun Li,Man Lu,Fenglong Lu,Qin Lv,Li Shang,Dragan Maksimovic +5 more
- 18 Jun 2012
TL;DR: A personalized driving behavior monitoring and analysis system for emerging hybrid vehicles that captures precise driver---vehicle information through de-noise, calibration, synchronization, and disorientation compensation and provides quantitative driver-specific (P)HEV analysis through operation mode classification, energy use and fuel use modeling.
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