A human body posture recognition algorithm based on BP neural network for wireless body area networks

Human body posture recognition has attracted considerable attention in recent years in wireless body area networks (WBAN). In order to precisely recognize human body posture, many recognition algorithms have been proposed. However, the recognition rate is relatively low. In this paper, we apply back propagation (BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude (SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4 postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.

[1]  Alessandro Tognetti,et al.  Exploiting Wearable Goniometer Technology for Motion Sensing Gloves , 2014, IEEE Journal of Biomedical and Health Informatics.

[2]  Tobias Nef,et al.  Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data , 2012, Sensors.

[3]  Gearóid Ó Laighin,et al.  Comparing Supervised Learning Techniques on the Task of Physical Activity Recognition , 2013, IEEE Journal of Biomedical and Health Informatics.

[4]  Jian Lu,et al.  A hierarchical approach to real-time activity recognition in body sensor networks , 2012, Pervasive Mob. Comput..

[5]  Hongyi Li,et al.  An Incremental Learning Method Based on Probabilistic Neural Networks and Adjustable Fuzzy Clustering for Human Activity Recognition by Using Wearable Sensors , 2012, IEEE Transactions on Information Technology in Biomedicine.

[6]  Hiroomi Hikawa,et al.  Novel FPGA Implementation of Hand Sign Recognition System With SOM–Hebb Classifier , 2015, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  M. Kangas,et al.  Determination of simple thresholds for accelerometry-based parameters for fall detection , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Ali-young Jeon,et al.  Emergency Detection System Using PDA Based on Self-Response Algorithm , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[9]  Zongjian He,et al.  A wearable wireless body area network for human activity recognition , 2014, 2014 Sixth International Conference on Ubiquitous and Future Networks (ICUFN).

[10]  Fuad Bajaber,et al.  On Designing Thermal-Aware Localized QoS Routing Protocol for in-vivo Sensor Nodes in Wireless Body Area Networks , 2015, Sensors.

[11]  Liu Ming,et al.  A Wearable Acceleration Sensor System for Gait Recognition , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[12]  Sundaram Suresh,et al.  Parallel implementation of back-propagation algorithm in networks of workstations , 2005, IEEE Transactions on Parallel and Distributed Systems.

[13]  Daijin Kim,et al.  A Depth Video Sensor-Based Life-Logging Human Activity Recognition System for Elderly Care in Smart Indoor Environments , 2014, Sensors.

[14]  Ramón F. Brena,et al.  Long-Term Activity Recognition from Wristwatch Accelerometer Data , 2014, Sensors.

[15]  Hexi Li,et al.  The recognition of moving human body posture based on combined neural network , 2013, IEEE Conference Anthology.

[16]  Ilkka Korhonen,et al.  Detection of Daily Activities and Sports With Wearable Sensors in Controlled and Uncontrolled Conditions , 2008, IEEE Transactions on Information Technology in Biomedicine.

[17]  Giovanni Saggio,et al.  Modeling Wearable Bend Sensor Behavior for Human Motion Capture , 2014, IEEE Sensors Journal.

[18]  Özlem Durmaz Incel,et al.  Multimodal Wireless Sensor Network-Based Ambient Assisted Living in Real Homes with Multiple Residents , 2014, Sensors.