Research Article
Wireless Sensing for Human Activity Recognition Using USRP
@INPROCEEDINGS{10.1007/978-3-030-95593-9_5, author={William Taylor and Syed Aziz Shah and Kia Dashtipour and Julien Le Kernec and Qammer H. Abbasi and Khaled Assaleh and Kamran Arshad and Muhammad Ali Imran}, title={Wireless Sensing for Human Activity Recognition Using USRP}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Wireless sensing Healthcare RF sensing}, doi={10.1007/978-3-030-95593-9_5} }- William Taylor
Syed Aziz Shah
Kia Dashtipour
Julien Le Kernec
Qammer H. Abbasi
Khaled Assaleh
Kamran Arshad
Muhammad Ali Imran
Year: 2022
Wireless Sensing for Human Activity Recognition Using USRP
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_5
Abstract
Artificial Intelligence (AI) in tandem wireless technologies is providing state-of-the-art techniques human motion detection for various applications including intrusion detection, healthcare and so on. Radio Frequency (RF) signal when propagating through the wireless medium encounters reflection and this information is stored when signals reach the receiver side as Channel State information (CSI). This paper develops an intelligent wireless sensing prototype for healthcare that can provide quasi-real time classification of CSI carrying various human activities obtained using USRP wireless devices. The dataset is collected from the CSI of USRP devices when a volunteer sits down or stands up as a test case. A model is created from this dataset for making predictions on unknown data. Random forest was able to provide the best results with an accuracy result to 96.70% and used for the model. A wearable device dataset was used as a benchmark to provide a comparison in performance of the USRP dataset.
