Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

[1]  M. Moy,et al.  Using Wearable Sensors to Monitor Physical Activities of Patients with COPD: A Comparison of Classifier Performance , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Patrick Boissy,et al.  The Use of Empirical Mode Decomposition-Based Algorithm and Inertial Measurement Units to Auto-Detect Daily Living Activities of Healthy Adults , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Ling Bao,et al.  Activity Recognition from User-Annotated Acceleration Data , 2004, Pervasive.

[5]  Didier Stricker,et al.  Introducing a modular activity monitoring system , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Michel Vacher,et al.  A wavelet-based pattern recognition algorithm to classify postural transitions in humans , 2009, 2009 17th European Signal Processing Conference.

[7]  Jeen-Shing Wang,et al.  A Wearable Sensor Module With a Neural-Network-Based Activity Classification Algorithm for Daily Energy Expenditure Estimation , 2012, IEEE Transactions on Information Technology in Biomedicine.

[8]  E. Wang,et al.  Analysis of natural mineral earthquake and blast based on Hilbert–Huang transform (HHT) , 2016 .

[9]  Gerhard Tröster,et al.  Recognition of dietary activity events using on-body sensors , 2008, Artif. Intell. Medicine.

[10]  Bernt Schiele,et al.  Scalable Recognition of Daily Activities with Wearable Sensors , 2007, LoCA.

[11]  Yu Lei,et al.  Hilbert-Huang Based Approach for Structural Damage Detection , 2004 .

[12]  I. Jánosi,et al.  Empirical mode decomposition and correlation properties of long daily ozone records. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Aykut Bultan,et al.  A four-parameter atomic decomposition of chirplets , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

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

[15]  Yoonseon Song,et al.  A basic study of activity type detection and energy expenditure estimation for children and youth in daily life using 3-axis accelerometer and 3-stage cascaded artificial neural network , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Alexandros Iosifidis,et al.  Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[17]  Y C Fung,et al.  Engineering analysis of biological variables: an example of blood pressure over 1 day. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[19]  Johannes Peltola,et al.  Activity classification using realistic data from wearable sensors , 2006, IEEE Transactions on Information Technology in Biomedicine.

[20]  Ray Ruichong Zhang,et al.  Characterizing and quantifying earthquake-induced site nonlinearity , 2006 .

[21]  John J. Soraghan,et al.  Empirical mode decomposition-based monopulse processor for enhanced radar tracking in the presence of high-power interference , 2011 .

[22]  Zina M. Ibrahim,et al.  Advances in Artificial Intelligence , 2003, Lecture Notes in Computer Science.

[23]  Didier Stricker,et al.  Creating and benchmarking a new dataset for physical activity monitoring , 2012, PETRA '12.

[24]  Didier Stricker,et al.  Introducing a New Benchmarked Dataset for Activity Monitoring , 2012, 2012 16th International Symposium on Wearable Computers.

[25]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[26]  Bernt Schiele,et al.  ADL recognition based on the combination of RFID and accelerometer sensing , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[27]  Shuo Ma,et al.  Hilbert-Huang transform analysis of dynamic and earthquake motion recordings , 2003 .

[28]  Shuo Ma,et al.  Signatures of the Seismic Source in EMD-Based Characterization of the 1994 Northridge, California, Earthquake Recordings , 2003 .

[29]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[30]  Peter H. Veltink,et al.  Ambulatory Monitoring of Activities and Motor Symptoms in Parkinson's Disease , 2010, IEEE Transactions on Biomedical Engineering.

[31]  K. Vasudevan,et al.  Empirical mode skeletonization of deep crustal seismic data: Theory and applications , 2000 .

[32]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[33]  Xinguo Yu.  Approaches and principles of fall detection for elderly and patient , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[34]  Charu C. Aggarwal,et al.  Data Streams - Models and Algorithms , 2014, Advances in Database Systems.

[35]  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.

[36]  Kent Larson,et al.  Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[37]  Gang Zhou,et al.  RadioSense: Exploiting Wireless Communication Patterns for Body Sensor Network Activity Recognition , 2012, 2012 IEEE 33rd Real-Time Systems Symposium.

[38]  Stephanie Schuckers,et al.  Automatic Recognition of postures and activities in stroke patients , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Ig-Jae Kim,et al.  Activity Recognition Using Wearable Sensors for Elder Care , 2008, 2008 Second International Conference on Future Generation Communication and Networking.

[40]  Feng Jiang,et al.  An Activity Recognition Algorithm Based on Multi-feature Fuzzy Cluster , 2016 .

[41]  Norbert Gyorbíró,et al.  An Activity Recognition System For Mobile Phones , 2009, Mob. Networks Appl..

[42]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[43]  B. Hannaford,et al.  Short Time Fourier Analysis of the Electromyogram: Fast Movements and Constant Contraction , 1986, IEEE Transactions on Biomedical Engineering.

[44]  Yajie Qin,et al.  The application of EMD in activity recognition based on a single triaxial accelerometer. , 2015, Bio-medical materials and engineering.

[45]  Marcin Rudzki,et al.  Artificial neural networks in accelerometer-based human activity recognition , 2015, 2015 22nd International Conference Mixed Design of Integrated Circuits & Systems (MIXDES).

[46]  Yuxin Peng,et al.  Complex activity recognition using time series pattern dictionary learned from ubiquitous sensors , 2016, Inf. Sci..

[47]  Tao Liu,et al.  Wearable Sensor Systems for Infants , 2015, Sensors.

[48]  Lei He,et al.  BP Neural Network for Human Activity Recognition in Smart Home , 2012, 2012 International Conference on Computer Science and Service System.

[49]  Tim Dallas,et al.  Feature Selection and Activity Recognition System Using a Single Triaxial Accelerometer , 2014, IEEE Transactions on Biomedical Engineering.

[50]  Nigel H. Lovell,et al.  Accelerometry based classification of gait patterns using empirical mode decomposition , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.