Novel Approaches to Activity Recognition Based on Vector Autoregression and Wavelet Transforms

The recognition of daily activities has been a long-running research domain, which has received increasing attention over the past few years. This is due to the proliferation of personal devices which are capable of reporting the physical signals generated during these activities. Being a classification problem, the primary focus is on suitable modalities for feature extraction and proper choice of classifiers. In this work we investigate the performance of two novel approaches to feature extraction based on Vector Autoregression and Wavelet Transforms together with four different classifiers. The results indicate that the two proposed feature extraction methods are suitable for this domain. In addition, the Canonical Correlation Forests classifier has been found to be a promising candidate for inference in the domain of Activity Recognition.

[1]  Prateek Jain,et al.  ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices , 2017, ICML.

[2]  Frank D. Wood,et al.  Canonical Correlation Forests , 2015, ArXiv.

[3]  Zhenyu He,et al.  Activity recognition from accelerometer signals based on Wavelet-AR model , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

[4]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[5]  Kimiaki Shirahama,et al.  Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors , 2018, Sensors.

[6]  Billur Barshan,et al.  Comparative study on classifying human activities with miniature inertial and magnetic sensors , 2010, Pattern Recognit..

[7]  Yu Guan,et al.  Deep Learning for Human Activity Recognition in Mobile Computing , 2018, Computer.

[8]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[9]  Miguel A. Labrador,et al.  A Survey on Human Activity Recognition using Wearable Sensors , 2013, IEEE Communications Surveys & Tutorials.

[10]  D. Lee Fugal,et al.  Conceptual wavelets in digital signal processing : an in-depth, practical approach for the non-mathematician , 2009 .

[11]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[12]  Davide Anguita,et al.  Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.

[13]  Thomas Seidl,et al.  Activity recognition from sensors using dyadic wavelets and Hidden Markov Model , 2014, 2014 IEEE 10th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[14]  Helmut Ltkepohl,et al.  New Introduction to Multiple Time Series Analysis , 2007 .

[15]  Sara Ashry Mohammed,et al.  ADL Classification Based on Autocorrelation Function of Inertial Signals , 2017, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA).