A Novel Framework for Human Activity Recognition with Time Labelled Real Time Sensor Data

ABSTRACT Human activity recognition is an effective approach for identifying the characteristics of historical data. In the past decades, different shallow classifiers and handcrafted features were used to identify the activities from the sensor data. These approaches are configured for offline processing and are not suitable for sequential data. This article proposes an adaptive framework for human activity recognition using a deep learning mechanism. This deep learning approach forms the deep belief network (DBN), which contains a visible layer and hidden layers. The processing of raw sensor data is performed by these layers and the activity is identified at the top most layers. The DBN is tested using the real time environment with the help of mobile devices that contain an accelerometer, a magnetometer, and a gyroscope. The results are analyzed with the metrics of precision, recall, and the F1-score. The results proved that the proposed method has a higher F1_score when compared to the existing approach.

[1]  Werner Kurschl,et al.  Modeling Situation-Aware Ambient Assisted Living Systems for Eldercare , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[2]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[3]  Luca Benini,et al.  Activity Recognition from On-Body Sensors: Accuracy-Power Trade-Off by Dynamic Sensor Selection , 2008, EWSN.

[4]  Billur Barshan,et al.  Human Activity Recognition Using Inertial/Magnetic Sensor Units , 2010, HBU.

[5]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[6]  S. C. Mukhopadhyay,et al.  Wireless Sensor Network Based Home Monitoring System for Wellness Determination of Elderly , 2012, IEEE Sensors Journal.

[7]  Sanja Dogramadzi,et al.  MOBISERV: An integrated intelligent home environment for the provision of health, nutrition and mobility services to the elderly , 2010 .

[8]  Matthias Budde,et al.  ActiServ: Activity Recognition Service for mobile phones , 2010, International Symposium on Wearable Computers (ISWC) 2010.

[9]  Chris D. Nugent,et al.  An Ontology-Based Hybrid Approach to Activity Modeling for Smart Homes , 2014, IEEE Transactions on Human-Machine Systems.

[10]  Nader Mohamed,et al.  Middleware: middleware challenges and approaches for wireless sensor networks , 2006, IEEE Distributed Systems Online.

[11]  Diane J. Cook,et al.  Transfer learning for activity recognition: a survey , 2013, Knowledge and Information Systems.

[12]  L. Curtis,et al.  Unit Costs of Health and Social Care 2016 , 2015 .

[13]  Mangal Sain,et al.  Middleware for ubiquitous Healthcare Information system , 2009, 2009 11th International Conference on Advanced Communication Technology.

[14]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

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

[16]  Khashayar Niki Maleki,et al.  A brief survey of software architecture concepts and service oriented architecture , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.

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

[18]  André Laurindo Maitelli,et al.  Wireless Monitoring of Patient’s Vital Signs , 2012 .

[19]  Werner Kurschl,et al.  A Simulator for Generating and Visualizing Sensor Data for Ambient Intelligence Environments , 2011, ANT/MobiWIS.

[20]  Patrick Olivier,et al.  Feature Learning for Activity Recognition in Ubiquitous Computing , 2011, IJCAI.

[21]  Jan Schäfer.  A Middleware for Self-Organising Distributed Ambient Assisted Living Applications , 2010, Electron. Commun. Eur. Assoc. Softw. Sci. Technol..

[22]  Shinji Kusumoto,et al.  Detecting Service Chains and Feature Interactions in Sensor-Driven Home Network Services , 2012, Sensors.

[23]  Jeffrey M. Hausdorff,et al.  Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom , 2010, IEEE Transactions on Information Technology in Biomedicine.

[24]  Juan M. Corchado,et al.  Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems , 2013, Inf. Sci..

[25]  Subhas Chandra Mukhopadhyay,et al.  Wireless sensors network based safe home to care elderly people: behaviour detection , 2011 .

[26]  L. Curtis,et al.  Unit Costs of Health and Social Care 2016 , 2015 .