Data Fusion on Motion and Magnetic Sensors embedded on Mobile Devices for the Identification of Activities of Daily Living

Several types of sensors have been available in off-the-shelf mobile devices, including motion, magnetic, vision, acoustic, and location sensors. This paper focuses on the fusion of the data acquired from motion and magnetic sensors, i.e., accelerometer, gyroscope and magnetometer sensors, for the recognition of Activities of Daily Living (ADL) using pattern recognition techniques. The system developed in this study includes data acquisition, data processing, data fusion, and artificial intelligence methods. Artificial Neural Networks (ANN) are included in artificial intelligence methods, which are used in this study for the recognition of ADL. The purpose of this study is the creation of a new method using ANN for the identification of ADL, comparing three types of ANN, in order to achieve results with a reliable accuracy. The best accuracy was obtained with Deep Learning, which, after the application of the L2 regularization and normalization techniques on the sensors data, reports an accuracy of 89.51%.

[1]  Haiyong Luo,et al.  An hidden Markov model based complex walking pattern recognition algorithm , 2016, UPINLBS.

[2]  Maria Bulgheroni,et al.  Free context smartphone based application for motor activity levels recognition , 2016, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[3]  Sheikh Iqbal Ahamed,et al.  A Novel Activity Detection System Using Plantar Pressure Sensors and Smartphone , 2015, 2015 IEEE 39th Annual Computer Software and Applications Conference.

[4]  Christiane Gresse von Wangenheim,et al.  A Systematic Literature Review on Usability Heuristics for Mobile Phones , 2013, Int. J. Mob. Hum. Comput. Interact..

[5]  A. Ng.  Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.

[6]  Roberto Setola,et al.  Fall-detection solution for mobile platforms using accelerometer and gyroscope data , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[7]  Nuno M. Garcia.  A Roadmap to the Design of a Personal Digital Life Coach , 2015, ICT Innovations.

[8]  Nuno M. Garcia,et al.  Identification of Activities of Daily Living Using Sensors Available in off-the-shelf Mobile Devices: Research and Hypothesis , 2016, ISAmI.

[9]  Zhenghua Chen,et al.  Smartphone-based Human Activity Recognition in buildings using Locality-constrained Linear Coding , 2015, 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA).

[10]  Yufei Chen,et al.  Performance Analysis of Smartphone-Sensor Behavior for Human Activity Recognition , 2017, IEEE Access.

[11]  Paul J. M. Havinga,et al.  Towards Physical Activity Recognition Using Smartphone Sensors , 2013, 2013 IEEE 10th International Conference on Ubiquitous Intelligence and Computing and 2013 IEEE 10th International Conference on Autonomic and Trusted Computing.

[12]  Héctor Pomares,et al.  On the Use of Sensor Fusion to Reduce the Impact of Rotational and Additive Noise in Human Activity Recognition , 2012, Sensors.

[13]  Chen Wei,et al.  Up and down buses activity recognition using smartphone accelerometer , 2016, 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference.

[14]  Nuno M. Garcia,et al.  Pattern Recognition Techniques for the Identification of Activities of Daily Living using Mobile Device Accelerometer , 2017, PeerJ Prepr..

[15]  Fu-Shan Jaw,et al.  Smartphone-based fall detection algorithm using feature extraction , 2016, 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).

[16]  Bin Liu,et al.  A two-layer and multi-strategy framework for human activity recognition using smartphone , 2016, 2016 IEEE International Conference on Communications (ICC).

[17]  Hamed Haddadi,et al.  SensingKit: Evaluating the Sensor Power Consumption in iOS Devices , 2016, 2016 12th International Conference on Intelligent Environments (IE).

[18]  Thomas George,et al.  An effective approach for human activity recognition on smartphone , 2015, 2015 IEEE International Conference on Engineering and Technology (ICETECH).

[19]  Yafeng Yin,et al.  A Context Aware Energy-Saving Scheme for Smart Camera Phones Based on Activity Sensing , 2015, 2015 IEEE 12th International Conference on Mobile Ad Hoc and Sensor Systems.

[20]  Vir V. Phoha,et al.  An empirical evaluation of activities and classifiers for user identification on smartphones , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[21]  Gabi Nakibly,et al.  Mobile Device Identification via Sensor Fingerprinting , 2014, ArXiv.

[22]  Manolis Tsiknakis,et al.  The MobiAct Dataset: Recognition of Activities of Daily Living using Smartphones , 2016, ICT4AgeingWell.

[23]  Nadeem Javaid,et al.  Evaluation of Human Activity Recognition and Fall Detection Using Android Phone , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[24]  Laurent Itti,et al.  Situation awareness via sensor-equipped eyeglasses , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  Reza Malekian,et al.  Fall detection using machine learning algorithms , 2016, 2016 24th International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

[26]  Luo Haiyong,et al.  An hidden Markov model based complex walking pattern recognition algorithm , 2016, 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS).

[27]  Md Taufeeq Uddin,et al.  Random forests based recognition of human activities and postural transitions on smartphone , 2016, 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV).

[28]  Sung-Bae Cho,et al.  Human activity recognition using smartphone sensors with two-stage continuous hidden Markov models , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[29]  Nuno M. Garcia,et al.  From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices , 2016, Sensors.

[30]  Vivek Jilla,et al.  Quantifiable fitness tracking using wearable devices , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[31]  N. Garcia,et al.  Multi-sensor data fusion techniques for the identification of activities of daily living using mobile devices , 2015 .

[32]  Tian Ya,et al.  MEMS-based human activity recognition using smartphone , 2016, 2016 35th Chinese Control Conference (CCC).

[33]  Yi-Leh Wu,et al.  Activity Recognition with sensors on mobile devices , 2014, 2014 International Conference on Machine Learning and Cybernetics.

[34]  W. Wagner,et al.  Improving runoff prediction through the assimilation of the ASCAT soil moisture product , 2010 .

[35]  Chris Van Hoof,et al.  Self-calibration of walking speed estimations using smartphone sensors , 2014, 2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS).

[36]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[37]  Mark A. Clements,et al.  Multimodal monitoring for neurological disorders , 2014, 2014 40th Annual Northeast Bioengineering Conference (NEBEC).

[38]  Ehsan Valavi,et al.  Microsoft Word-Final.docx , 2010 .

[39]  Aboelmagd Noureldin,et al.  Using portable device sensors to recognize height changing modes of motion , 2014, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[40]  Yufei Chen,et al.  On motion-sensor behavior analysis for human-activity recognition via smartphones , 2016, 2016 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA).

[41]  Yu Wang,et al.  An Automatic User-Adapted Physical Activity Classification Method Using Smartphones , 2017, IEEE Transactions on Biomedical Engineering.

[42]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..