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RFID-based techniques for human-activity detection

Published:2005-09 Issue:9 Volume:48 Page:39-44
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Smith Joshua R.¹,Fishkin Kenneth P.²,Jiang Bing³,Mamishev Alexander³,Philipose Matthai⁴,Rea Adam D.⁴,Roy Sumit³,Sundara-Rajan Kishore⁵

Affiliation:

1. Intel Research Seattle, Seattle, WA, and University of Washington, Seattle

2. Intel Research Seattle, Seattle, WA, and University of Washington, Seattle, WA

3. University of Washington, Seattle, WA

4. Intel Research Seattle, Seattle, WA

5. University of Washington, Seattle, WA, and Intel Research Seattle, Seattle, WA

Abstract

The iBracelet and the Wireless Identification and Sensing Platform promise the ability to infer human activity directly from sensor readings.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/1081992.1082018

Reference8 articles.

1. Inventing wellness systems for aging in place

Cited by 132 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. WiFi-Based Indoor Human Activity Sensing: A Selective Sensing Strategy and a Multilevel Feature Fusion Approach;IEEE Internet of Things Journal;2024-09-15

2. A Fuzzy Logic-Based Directional Charging Scheme for Wireless Rechargeable Sensor Networks;Sensors;2024-08-05

3. Human activity recognition: A comprehensive review;Expert Systems;2024-07-27

4. Tagnoo: Enabling Smart Room-Scale Environments with RFID-Augmented Plywood;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

5. WiLDAR: WiFi Signal-Based Lightweight Deep Learning Model for Human Activity Recognition;IEEE Internet of Things Journal;2024-01-15

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