Jordan J. Bird
ABSTRACT
The ability to autonomously detect a physical fall is one of the many enabling technologies towards better independent living. This work explores how genetic programming can be leveraged to develop machine learning pipelines for the classification of falls via EEG brainwave activity. Eleven physical activities (5 types of falls and 6 non-fall activities) are clustered into a binary classification problem of whether a fall has occurred or not. Wavelet features are extracted from the brainwaves before machine learning models are explored and tuned for better k-fold classification accuracy, precision, recall, and F1 score. Results show that solutions discovered through genetic programming can detect falls with a mean accuracy of 89.34%, precision of 0.883, recall of 0.908, and an F1-Score of 0.895 from EEG brainwave data alone. All three genetic programming solutions chose a further step of Principal Component Analysis for additional feature extraction from the computed wavelet features, each with iterated powers of 6, 3, and 7, and all with a randomised Singular Value Decomposition approach. The best model is finally analysed via the Receiver Operating Characteristic and Precision-Recall curves. Python code for each of the genetic programming pipelines are provided.
- Allan L Adkin, Sylvia Quant, Brian E Maki, and William E McIlroy. 2006. Cortical responses associated with predictable and unpredictable compensatory balance reactions. Experimental brain research 172, 1 (2006), 85–93.Google Scholar
- Anne Felicia Ambrose, Geet Paul, and Jeffrey M Hausdorff. 2013. Risk factors for falls among older adults: a review of the literature. Maturitas 75, 1 (2013), 51–61.Google ScholarCross Ref
- Valerio F Annese, Marco Crepaldi, Danilo Demarchi, and Daniela De Venuto. 2016. A digital processor architecture for combined EEG/EMG falling risk prediction. In 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 714–719.Google Scholar
- Jordan J Bird, Diego R Faria, Luis J Manso, Anikó Ekárt, and Christopher D Buckingham. 2019. A deep evolutionary approach to bioinspired classifier optimisation for brain-machine interaction. Complexity 2019(2019).Google Scholar
- Jeffrey Braithwaite, Russell Mannion, Yukihiro Matsuyama, Paul G Shekelle, Stuart Whittaker, Samir Al-Adawi, Kristiana Ludlow, Wendy James, Hsuen P Ting, Jessica Herkes, 2018. The future of health systems to 2030: a roadmap for global progress and sustainability. International Journal for Quality in Health Care 30, 10 (2018), 823–831.Google ScholarCross Ref
- Jay Chen, Karric Kwong, Dennis Chang, Jerry Luk, and Ruzena Bajcsy. 2006. Wearable sensors for reliable fall detection. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 3551–3554.Google Scholar
- Sameer Raju Dhole, Amith Kashyap, Animesh Narayan Dangwal, and Rajasekar Mohan. 2019. A novel helmet design and implementation for drowsiness and fall detection of workers on-site using EEG and Random-Forest Classifier. Procedia Computer Science 151 (2019), 947–952.Google ScholarDigital Library
- David Eibling. 2018. Balance disorders in older adults. Clinics in geriatric medicine 34, 2 (2018), 175–181.Google Scholar
- Dhavalkumar H Joshi, UK Jaliya, and Darshak Thakore. 2015. Raw EEG-based Fatigue and Drowsiness Detection: A Review. International Institute for Technological Research and Development 1, 1(2015).Google Scholar
- Sridhar Krishnan and Yashodhan Athavale. 2018. Trends in biomedical signal feature extraction. Biomedical Signal Processing and Control 43 (2018), 41–63.Google ScholarCross Ref
- Yun Li, KC Ho, and Mihail Popescu. 2012. A microphone array system for automatic fall detection. IEEE Transactions on Biomedical Engineering 59, 5 (2012), 1291–1301.Google ScholarCross Ref
- Fabien Lotte and Marco Congedo. 2016. EEG feature extraction. Brain–Computer Interfaces 1: Foundations and Methods (2016), 127–143.Google Scholar
- Kavita Mahajan, MR Vargantwar, and Sangita M Rajput. 2011. Classification of EEG using PCA, ICA and Neural Network. International Journal of Engineering and Advanced Technology 1, 1(2011), 80–83.Google Scholar
- Lourdes Martínez-Villaseñor, Hiram Ponce, Jorge Brieva, Ernesto Moya-Albor, José Núñez-Martínez, and Carlos Peñafort-Asturiano. 2019. UP-fall detection dataset: A multimodal approach. Sensors 19, 9 (2019).Google Scholar
- Muhammad Mubashir, Ling Shao, and Luke Seed. 2013. A survey on fall detection: Principles and approaches. Neurocomputing 100(2013), 144–152.Google ScholarDigital Library
- Abdallah Naser, Ahmad Lotfi, and Junpei Zhong. 2022. Multiple thermal sensor array fusion towards enabling privacy-preserving human monitoring applications. IEEE Internet of Things Journal(2022).Google ScholarCross Ref
- National Health Service. 2021. Falls. https://www.nhs.uk/conditions/falls/Google Scholar
- Office for National Statistics. 2021. Dataset: Vital statistics in the UK: births, deaths and marriages. https://www.ons.gov.uk/peoplepopulationandcommunity/populationandmigration/populationestimates/datasets/vitalstatisticspopulationandhealthreferencetablesGoogle Scholar
- Randal S Olson, Nathan Bartley, Ryan J Urbanowicz, and Jason H Moore. 2016. Evaluation of a tree-based pipeline optimization tool for automating data science. In Proceedings of the genetic and evolutionary computation conference 2016. 485–492.Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825–2830.Google ScholarDigital Library
- Parisa Rashidi and Alex Mihailidis. 2012. A survey on ambient-assisted living tools for older adults. IEEE journal of biomedical and health informatics 17, 3(2012), 579–590.Google Scholar
- Caroline Rougier, Jean Meunier, Alain St-Arnaud, and Jacqueline Rousseau. 2007. Fall detection from human shape and motion history using video surveillance. In 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), Vol. 2. IEEE, 875–880.Google ScholarDigital Library
- Mridu Sahu, Praveen Shukla, Aditya Chandel, Saloni Jain, and Shrish Verma. 2021. Eye Blinking Classification Through NeuroSky MindWave Headset Using EegID Tool. In International Conference on Innovative Computing and Communications. Springer, 789–799.Google Scholar
- A Hasan Sapci and H Aylin Sapci. 2019. Innovative assisted living tools, remote monitoring technologies, artificial intelligence-driven solutions, and robotic systems for aging societies: systematic review. JMIR aging 2, 2 (2019), e15429.Google ScholarCross Ref
- Robert E Schapire. 2013. Explaining adaboost. In Empirical inference. Springer, 37–52.Google Scholar
- Judy A Stevens, Phaedra S Corso, Eric A Finkelstein, and Ted R Miller. 2006. The costs of fatal and non-fatal falls among older adults. Injury prevention 12, 5 (2006), 290–295.Google Scholar
- Abdulhamit Subasi. 2007. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications 32, 4 (2007), 1084–1093.Google ScholarDigital Library
- Abdulhamit Subasi and M Ismail Gursoy. 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert systems with applications 37, 12 (2010), 8659–8666.Google Scholar
- Mary E Tinetti, Mark Speechley, and Sandra F Ginter. 1988. Risk factors for falls among elderly persons living in the community. New England journal of medicine 319, 26 (1988), 1701–1707.Google Scholar
- Elif Derya Übeyli. 2009. Combined neural network model employing wavelet coefficients for EEG signals classification. Digital Signal Processing 19, 2 (2009), 297–308.Google ScholarDigital Library
- Yuxi Wang, Kaishun Wu, and Lionel M Ni. 2016. Wifall: Device-free fall detection by wireless networks. IEEE Transactions on Mobile Computing 16, 2 (2016), 581–594.Google ScholarDigital Library
- Xinyang Yu, Pharino Chum, and Kwee-Bo Sim. 2014. Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik 125, 3 (2014), 1498–1502.Google ScholarCross Ref
- Biao Zhang, Jianjun Wang, and Thomas Fuhlbrigge. 2010. A review of the commercial brain-computer interface technology from perspective of industrial robotics. In 2010 IEEE international conference on automation and logistics. IEEE, 379–384.Google ScholarCross Ref
Index Terms
- EEG Wavelet Classification for Fall Detection with Genetic Programming
Recommendations
Machine learning-based EEG signals classification model for epileptic seizure detection
AbstractThe detection of epileptic seizures by classifying electroencephalography (EEG) signals into ictal and interictal classes is a demanding challenge, because it identifies the seizure and seizure-free states of an epileptic patient. In previous ...
Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns
UbiComp '18: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable ComputersFalling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of ...
EEG-based motor imagery classification in BCI system by using unscented Kalman filter
This paper presents the unscented Kalman filter UKF to the BCI signal processing to classify the EEG-based motor imagery signals. UKF is applied to the common spatio-spectral pattern CSSP filters to improve the feature data extracted from the system. ...
Comments