Human Activity Classification With Transmission and Reflection Coefficients of On-Body Antennas Through Deep Convolutional Neural Networks

We propose to classify human activities based on transmission coefficient (<inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula>) and reflection coefficient (<inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula>) of on-body antennas with deep convolutional neural networks (DCNNs). It is shown that spectrograms of <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> exhibit unique time-varying signatures for different body motion activities that can be used for classification purposes. DCNN, a deep learning approach, is applied to spectrograms to learn the necessary features and classification boundaries. It is found that DCNN can achieve classification accuracies of 98.8% using <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> and 97.1% using <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula>. The effects of operating frequency and antenna location on the accuracy have been investigated.

[1]  Yang Hao,et al.  Exploring Physiological Parameters in Dynamic WBAN Channels , 2014, IEEE Transactions on Antennas and Propagation.

[2]  Youngwook Kim,et al.  Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Youngwook Kim,et al.  Detection of Eye Blinking Using Doppler Sensor With Principal Component Analysis , 2015, IEEE Antennas and Wireless Propagation Letters.

[4]  Matthew Keally,et al.  AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks , 2013, 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS).

[5]  Shuangquan Wang,et al.  A review on radio based activity recognition , 2015, Digit. Commun. Networks.

[6]  Paolo Barsocchi,et al.  Limb Movements Classification Using Wearable Wireless Transceivers , 2011, IEEE Transactions on Information Technology in Biomedicine.

[7]  Youngwook Kim,et al.  Application of Linear Predictive Coding for Human Activity Classification Based on Micro-Doppler Signatures , 2014, IEEE Geoscience and Remote Sensing Letters.

[8]  Victor C. M. Leung,et al.  Enabling technologies for wireless body area networks: A survey and outlook , 2009, IEEE Communications Magazine.

[9]  Tara N. Sainath,et al.  FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .

[10]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[11]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[12]  C. Parini,et al.  Antennas and propagation for on-body communication systems , 2007, IEEE Antennas and Propagation Magazine.

[13]  Youngwook Kim,et al.  Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[14]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[15]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[16]  Tharaka A. Lamahewa,et al.  Propagation Models for Body-Area Networks: A Survey and New Outlook , 2013, IEEE Antennas and Propagation Magazine.

[17]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[18]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[19]  Ram M. Narayanan,et al.  Classification of human motions using empirical mode decomposition of human micro-Doppler signatures , 2014 .