Using Auditory Features for WiFi Channel State Information Activity Recognition

Abstract

Activity recognition has gained significant attention recently, due to the availability of smartphones and smartwatches with movement sensors which facilitate the collection and processing of relevant measurements, by almost everyone. Using the device-embedded sensors, there is no need of carrying dedicated equipment (inertia measurement units or accelerometers) and use complex software to process the data. This approach though, has the disadvantage of needing to carry a device during the monitoring time. WiFi channel state information (CSI) offers a passive, device-free opportunity, for monitoring activities of daily living in non-line-of-sight conditions. In this paper, Mel frequency cepstral coefficient (MFCC) feature extraction, used successfully for audio signals, is proposed for CSI time-series classification. The applicability of the proposed features in activity recognition has been evaluated using three classification methods, convolutional neural networks (CNN), long short term memory recurrent neural networks and Hidden Markov models, in comparison to currently used feature extraction methods (discrete wavelet transform, short time fourier transform). MFCC feature extraction achieves higher accuracy in activity classification than the compared methods, as verified by evaluation with two activity datasets, in particular 95% accuracy precision achieved in activity recognition using MFCC features in combination with CNN classifier.

SHARP: Environment and Person Independent Activity Recognition With Commodity IEEE 802.11 Access Points

“…HAR through CSI data from COTS Wi-Fi devices was first studied in, e.g., E-eyes [14], CARM [15]. More recently, several articles showed the effectiveness of machine learning techniques in building algorithms that distinguish human activities based on CSI features [11], [16], [17], [18]. However, these works do not focus on the robustness to environmental changes and on the generalization capability to previously unseen environments and subjects, which are key enablers for the successful development of Wi-Fibased sensing systems [8].…”

Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms

“…However, it was designed and tested on only one user, and due to the need for multiple receivers to achieve precise activity detection, their model was not considered to be cost-effective. In [ 18 ], the authors used CSI and CNN classifiers to detect five different activities with an accuracy of 78%. However, it was designed and tested on one user.…”

“…First, the camera can work only in a line-of-sight (LOS) environment with sufficient lighting [ 8 , 9 , 10 , 11 , 12 ]. Second, it does not preserve privacy concerns [ 8 , 9 , 10 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. Finally, it cannot track activities or gestures through walls.…”

Device-Free Human Activity Recognition Based on GMM-HMM Using Channel State Information

“…In addition, we also compared the system with references [22] in terms of boxing, empty, walking, pushing, waving. Reference [22] 2, which represent confusion matrices and comparison results respectively, the identification accuracy of our system is 99.0%, which is superior to other methods when using ITI datasets. When an action occurs, the position of the person changes on the vertical plane and the horizontal plane.…”

“…The authors would like to thank Mr. Ilias Kalamaras and Mr. Konstantinos Votis [22] for useful discussions and for providing us the ITI datasets. We would also like to thank the authors in Reference [21] for sharing their datasets as open source.…”