Recognition rate difference between real-time and offline human activity recognition

The appearance of the Internet of Things topic has a huge impact on several research fields including human activity recognition (HAR) where wearable sensors provide the raw information about the physical activity and functional ability of an observed person. Previous studies have shown that HAR can be seen as a general machine learning problem with a particular data pre-processing stage. In the last years, several researchers reached high recognition rates on public data sets or in laboratory environment but their solutions have not tested yet in real-life. Therefore, this paper investigates the efficiency of previously used machine learning strategies in real environment by an Android-base, self-learning HAR application which has been designed according to the latest HAR solutions. The result of this study shows a significant recognition rate difference between the “online” (real-time) and “offline” cases.

[1]  Charu C. Aggarwal,et al.  Neural Networks and Deep Learning , 2018, Springer International Publishing.

[2]  Lei Gao,et al.  Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems. , 2014, Medical engineering & physics.

[3]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[4]  Teddy Mantoro,et al.  A Comparison Study of Classifier Algorithms for Mobile-phone's Accelerometer Based Activity Recognition , 2012 .

[5]  Ioannis N. Kouris,et al.  Application of Data Mining Techniques to Efficiently Monitor Chronic Diseases Using Wireless Body Area Networks and Smartphones , 2013 .

[6]  Nigel H. Lovell,et al.  Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring , 2006, IEEE Transactions on Information Technology in Biomedicine.

[7]  Davide Anguita,et al.  A Public Domain Dataset for Human Activity Recognition using Smartphones , 2013, ESANN.

[8]  Arnaldo J. Abrantes,et al.  Classification of Physical Activities Using a Smartphone: Evaluation Study Using Multiple Users , 2014 .

[9]  Tae-Seong Kim,et al.  A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer , 2010, IEEE Transactions on Information Technology in Biomedicine.

[10]  Daniel P. Siewiorek,et al.  Activity recognition and monitoring using multiple sensors on different body positions , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

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

[12]  Allen Y. Yang,et al.  Distributed recognition of human actions using wearable motion sensor networks , 2009, J. Ambient Intell. Smart Environ..

[13]  G. Sebestyen,et al.  Monitoring Human Activity through Portable Devices , 2012 .

[14]  David Howard,et al.  A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data , 2009, IEEE Transactions on Biomedical Engineering.

[15]  Jozsef Suto,et al.  Activity Recognition in Adaptive Assistive Systems Using Artificial Neural Networks , 2016 .

[16]  Stefan Oniga,et al.  Optimal Recognition Method of Human Activities Using Artificial Neural Networks , 2015 .

[17]  Guy Carrault,et al.  Advanced classification of ambulatory activities using spectral density distances and heart rate , 2017, Biomed. Signal Process. Control..

[18]  Zhaozheng Yin,et al.  Human Activity Recognition Using Wearable Sensors by Deep Convolutional Neural Networks , 2015, ACM Multimedia.

[19]  Petrica C. Pop,et al.  Feature Analysis to Human Activity Recognition , 2016, Int. J. Comput. Commun. Control.

[20]  Stefan Oniga,et al.  Wireless data acquisition system for IoT applications , 2013 .

[21]  G. ÓLaighin,et al.  Direct measurement of human movement by accelerometry. , 2008, Medical engineering & physics.

[22]  Min Sheng,et al.  Short-time activity recognition with wearable sensors using convolutional neural network , 2016, VRCAI.

[23]  Jeen-Shing Wang,et al.  Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers , 2008, Pattern Recognit. Lett..

[24]  Hongnian Yu,et al.  Elderly activities recognition and classification for applications in assisted living , 2013, Expert Syst. Appl..

[25]  Min Sheng,et al.  The Recognition of Human Daily Actions with Wearable Motion Sensor System , 2016, Trans. Edutainment.

[26]  Duc A. Tran,et al.  The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) A Study on Human Activity Recognition Using Accelerometer Data from Smartphones , 2014 .

[27]  Paul J. M. Havinga,et al.  A Survey of Online Activity Recognition Using Mobile Phones , 2015, Sensors.

[28]  József Sütő,et al.  Real time human activity monitoring , 2015 .