Using Auditory Features for WiFi Channel State Information Activity Recognition

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.