Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

[1]  Jaskaran Singh,et al.  Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis , 2020 .

[2]  Syed Aziz Shah,et al.  An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare , 2020, Sensors.

[3]  Dan Wang,et al.  Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting , 2019, International Journal of Electrical Power & Energy Systems.

[4]  Syed Aziz Shah,et al.  Human Activity Recognition : Preliminary Results for Dataset Portability using FMCW Radar , 2019, 2019 International Radar Conference (RADAR).

[5]  Nicoletta Noceti,et al.  Positive technology for elderly well-being: A review , 2020, Pattern Recognit. Lett..

[6]  Jacques Demongeot,et al.  A Novel Monitoring System for Fall Detection in Older People , 2018, IEEE Access.

[7]  Peijing Zhang,et al.  Research on KNN Algorithm in Malicious PDF Files Classification under Adversarial Environment , 2019, ICBDC 2019.

[8]  Akram Alomainy,et al.  Machine Learning Driven Approach Towards the Quality Assessment of Fresh Fruits Using Non-Invasive Sensing , 2020, IEEE Sensors Journal.

[9]  Boualem Boashash,et al.  Range-Doppler radar sensor fusion for fall detection , 2017, 2017 IEEE Radar Conference (RadarConf).

[10]  Lei Yang,et al.  3D depth image analysis for indoor fall detection of elderly people , 2016, Digit. Commun. Networks.

[11]  Kevin Bouchard,et al.  Activity Recognition in Smart Homes using UWB Radars , 2020, ANT/EDI40.

[12]  Sébastien Gaboury,et al.  Recognizing activities of daily living from UWB radars and deep learning , 2021, Expert Syst. Appl..

[13]  Anwesha Khasnobish,et al.  Activity Recognition using Ultra Wide Band Range-Time Scan , 2021, 2020 28th European Signal Processing Conference (EUSIPCO).

[14]  Nuno M. Garcia,et al.  An Efficient Machine Learning-based Elderly Fall Detection Algorithm , 2019, ArXiv.

[15]  Dacheng Xiu,et al.  Principal Component Analysis of High-Frequency Data , 2015, Journal of the American Statistical Association.

[16]  Boualem Boashash,et al.  Wideband radar based fall motion detection for a generic elderly , 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

[17]  Athanassios Skodras,et al.  A smartphone-based fall detection system for the elderly , 2017, Proceedings of the 10th International Symposium on Image and Signal Processing and Analysis.

[18]  Efkan Durmus,et al.  Optimizing the color-to-grayscale conversion for image classification , 2016, Signal Image Video Process..

[19]  Meng Chen,et al.  Human Posture Recognition in Intelligent Healthcare , 2020, Journal of Physics: Conference Series.

[20]  Margaret Lech,et al.  Using grayscale images for object recognition with convolutional-recursive neural network , 2016, 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE).

[21]  L. Sugrue,et al.  Hybrid 3D/2D Convolutional Neural Network for Hemorrhage Evaluation on Head CT , 2018, American Journal of Neuroradiology.

[22]  Akram Alomainy,et al.  Advances in Body-Centric Wireless Communication: Applications and State-of-the-art , 2016 .

[23]  Xiuping Li,et al.  Multi-Classification Algorithm for Human Motion Recognition Based on IR-UWB Radar , 2020, IEEE Sensors Journal.

[24]  Ahmad Diab,et al.  Elder Tracking and Fall Detection System Using Smart Tiles , 2017, IEEE Sensors Journal.

[25]  Masayuki Ikebe,et al.  Automatic Radiographic Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis Using POC , 2019, 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

[26]  Taekjin Han,et al.  IR-UWB Sensor Based Fall Detection Method Using CNN Algorithm , 2020, Sensors.

[27]  Branka Jokanovic,et al.  Radar fall motion detection using deep learning , 2016, 2016 IEEE Radar Conference (RadarConf).

[28]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Yixiong Pan,et al.  SPEECH EMOTION RECOGNITION USING SUPPORT VECTOR MACHINE , 2010 .

[30]  Laurent Besacier,et al.  Pervasive Attention: 2D Convolutional Neural Networks for Sequence-to-Sequence Prediction , 2018, CoNLL.

[31]  Luis G. Jaimes,et al.  Fall detection system for the elderly , 2017, 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC).

[32]  Moeness Amin,et al.  Generalized PCA Fusion for Improved Radar Human Motion Recognition , 2019, 2019 IEEE Radar Conference (RadarConf).

[33]  Ryu Kato,et al.  Fall detection and walking estimation using floor vibration for solitary elderly people , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).

[34]  Takuya Sakamoto,et al.  Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network , 2020, 2008.02182.

[35]  Yang Wang,et al.  Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. , 2018, Cancer genomics & proteomics.

[36]  Jorge Cadima,et al.  Principal component analysis: a review and recent developments , 2016, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[37]  Ibrahim Akduman,et al.  Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm , 2019, Comput. Biol. Medicine.

[38]  Jean Meunier,et al.  Elderly fall detection system based on multiple shape features and motion analysis , 2018, 2018 International Conference on Intelligent Systems and Computer Vision (ISCV).

[39]  Tone Bratteteig,et al.  A trajectory for technology-supported elderly care work , 2018, Computer Supported Cooperative Work (CSCW).

[40]  Ayesha Choudhary,et al.  Automated Fall Detection From a Camera Using Support Vector Machine , 2019, 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP).

[41]  N. A. Khovanova,et al.  Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation , 2017, Biomed. Signal Process. Control..

[42]  Tin Kam Ho,et al.  Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language , 2019, Journal of Educational and Behavioral Statistics.

[43]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[44]  Quoc V. Le,et al.  Unsupervised Data Augmentation , 2019, ArXiv.

[45]  Miguel Hernando,et al.  Home Camera-Based Fall Detection System for the Elderly , 2017, Sensors.

[46]  Yimin Zhang,et al.  Human motion recognition exploiting radar with stacked recurrent neural network , 2019, Digit. Signal Process..