Recognizing daily activities with RFID-based sensors

By Michael Buettner, Richa Prasad, Matthai Philipose, David Wetherall. Ubiquitous Computing, 2009

323 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/89ce1f135babec1ad0b5131ebdbb65cfdbb8532c

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

1. Bin Guo
2. Shibo He
3. Jiming Chen

In Computer Vision and Pattern Recognition

1. C. Nugent
2. Kevin Fu
3. Guoliang Xing
4. Diane Joyce Cook
5. Zhiwen Yu
6. Bin Guo
7. Chris Harrison
8. Jesse Hoey

In Electrical and Electronic Engineering

1. Benjamin Ransford
2. C. Nugent
3. Youxian Sun
4. Guoliang Xing
5. Diane Joyce Cook
6. Shah Atiqur Rahman
7. Yuxiao Huang
8. Christopher A. Merck

Citations in the corpus

• A Survey on Ambient Intelligence in Health Care (2013, unknown): “In response, researchers have explored the usage of RFID tags [53], [54] and accelerometers or shake sensors [55] for tagging these objects and using the data for activity recognition.”
• Deep Learning for Sensor-based Human Activity Recognition (2020, methodology): “ used for identifying object usage. When acting as object sensors rather than ambient sensors, RFID tags are needed to be attached to the target objects such as mugs, books, computers, and toothpaste [20]. In the detection phase, a worn RFID reader is also needed. Considering both the convenience an”
• ViBand: High-Fidelity Bio-Acoustic Sensing Using Commodity Smartwatch Accelerometers (2016, background): “Most approaches rely on markers [7, 20, 26, 40, 42] or special-purpose tags [28, 57, 58].”
• Mementos: system support for long-running computation on RFID-scale devices (2011, background): “Previously proposed applications for transiently powered computers include environmental monitoring [17], activity recognition [6], and cryptographic protocols [9].”
• Unintrusive eating recognition using Google Glass (2015, background): “The use of acoustic sensors (to detect chewing sounds) ([9], [2]) or cameras [12] raises major privacy concerns and may face challenges due to lighting or background noise, while environmental sensors [3] have limited generalizability as they depend on a controlled and tagged environment.”
• Simultaneous Plan Recognition and Monitoring (SPRAM) for Robot Assistants (2013, background): “A large number of approaches for activity recognition heavily rely on different variations of the Hidden Markov Model (HMM) [3, 10], Hierarchical Conditional Random Fields (CRFs) [13] or Hierarchical Maximum Entropy Markov Models (MEMM) [16].”
• Sensor-Based Activity Recognition (2012, methodology): “As a result of the wave of intensive investigation, there have seen a plethora of impressive works on sensor-based activity recognition in the past several years [57], [58], [62], [64], [76]–[80], [82], [83], [85], [87], [97].”
• Energy provisioning in wireless rechargeable sensor networks (2011, methodology): “It is shown that the WISP approach is advantageous over RFID-based approaches for the application [2].”