Yekta Said Can
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Abstract
IoT devices generate massive amounts of biomedical data with increased digitalization and development of the state-of-the-art automated clinical data collection systems. When combined with advanced machine learning algorithms, the big data could be useful to improve the health systems for decision-making, diagnosis, and treatment. Mental healthcare is also attracting attention, since most medical problems can be associated with mental states. Affective computing is among the emerging biomedical informatics fields for automatically monitoring a person’s mental state in ambulatory environments by using physiological and physical signals. However, although affective computing applications are promising to improve our daily lives, before analyzing physiological signals, privacy issues and concerns need to be dealt with. Federated learning is a promising candidate for developing high-performance models while preserving the privacy of individuals. It is a privacy protection solution that stores model parameters instead of the data itself and abides by the data protection laws such as EU General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA). We applied federated learning to heart activity data collected with smart bands for stress-level monitoring in different events. We achieved encouraging results for using federated learning in IoT-based wearable biomedical monitoring systems by preserving the privacy of the data.
- Sabyasachi Dash, Sushil Kumar Shakyawar, Mohit Sharma, and Sandeep Kaushik. 2019. Big data in healthcare: Management, analysis and future prospects. J. Big Data 6, 1 (2019), 54.Google Scholar
Cross Ref
- Jie Xu and Fei Wang. 2019. Federated learning for healthcare informatics. arXiv preprint arXiv:1911.06270 (2019).Google Scholar
- S. Greene, H. Thapliyal, and A. Caban-Holt. 2016. A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Cons. Electron. Mag. 5, 4 (Oct. 2016), 44--56. DOI:http://dx.doi.org/10.1109/MCE.2016.2590178Google ScholarCross Ref
- Leandro Y. Mano, Bruno S. Faiçal, Luis H. V. Nakamura, Pedro H. Gomes, Giampaolo L. Libralon, Rodolfo I. Meneguete, Geraldo P. R. Filho, Gabriel T. Giancristofaro, Gustavo Pessin, Bhaskar Krishnamachari, and J. Ueyama. 2016. Exploiting IoT technologies for enhancing Health Smart Homes through patient identification and emotion recognition. Comput. Commun. 89--90 (2016), 178--190.Google Scholar
- Yekta Said Can, Bert Arnrich, and Cem Ersoy. 2019. Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. J. Biomed. Inf. 92 (2019), 103139.Google ScholarDigital Library
- Ashima Anand and Amit Kumar Singh. 2020. An improved DWT-SVD domain watermarking for medical information security. Comput. Commun. 152 (2020), 72--80.Google ScholarCross Ref
- Wikipedia. 2019. Information privacy law. Retrieved from: https://en.wikipedia.org/wiki/Information_privacy_law.Google Scholar
- George J. Annas et al. 2003. HIPAA regulations—A new era of medical-record privacy? New Eng. J. Med. 348, 15 (2003), 1486--1490.Google ScholarCross Ref
- Paul Voigt and Axel Von dem Bussche. 2017. The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st ed. Springer International Publishing, Cham.Google Scholar
- Lydia de la Torre. 2018. A guide to the California Consumer Privacy Act of 2018. Retrieved from SSRN 3275571 (2018).Google Scholar
- Jack Wagner. 2017. China’s Cybersecurity Law: What you need to know. The Diplomat 1 (2017).Google Scholar
- Cong Peng, He Debiao, Chen Jianhua, Neeraj Kumar, and Muhammad Khurram Khan. 2020. EPRT: An efficient privacy-preserving medical service recommendation and trust discovery scheme. ACM Trans. Internet Technol. 0, ja (2020), 1. DOI:http://dx.doi.org/10.1145/3397678Google Scholar
- Jakub Konečnỳ, H. Brendan McMahan, Felix X. Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492 (2016).Google Scholar
- Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage. 2018. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018).Google Scholar
- B. Liu, L. Wang, and M. Liu. 2019. Lifelong federated reinforcement learning: A learning architecture for navigation in cloud robotic systems. IEEE Robot. Autom. Lett. 4, 4 (Oct. 2019), 4555--4562. DOI:http://dx.doi.org/10.1109/LRA.2019.2931179Google ScholarCross Ref
- X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen. 2019. In-Edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning. IEEE Netw. 33, 5 (Sep. 2019), 156--165. DOI:http://dx.doi.org/10.1109/MNET.2019.1800286Google ScholarDigital Library
- Swaroop Ramaswamy, Rajiv Mathews, Kanishka Rao, and Françoise Beaufays. 2019. Federated learning for emoji prediction in a mobile keyboard. arXiv preprint arXiv:1906.04329 (2019).Google Scholar
- D. Leroy, A. Coucke, T. Lavril, T. Gisselbrecht, and J. Dureau. 2019. Federated learning for keyword spotting. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). 6341--6345. DOI:http://dx.doi.org/10.1109/ICASSP.2019.8683546Google Scholar
- S. Samarakoon, M. Bennis, W. Saad, and M. Debbah. 2020. Distributed federated learning for ultra-reliable low-latency vehicular communications. IEEE Trans. Commun. 68, 2 (2020), 1146--1159. DOI:http://dx.doi.org/10.1109/TCOMM.2019.2956472Google ScholarCross Ref
- Theodora S. Brisimi, Ruidi Chen, Theofanie Mela, Alex Olshevsky, Ioannis Ch. Paschalidis, and Wei Shi. 2018. Federated learning of predictive models from federated Electronic Health Records. Int. J. Med. Inf. 112 (2018), 59--67.Google ScholarCross Ref
- H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera Arcas. 2017. Communication-efficient learning of deep networks from decentralized data. In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (Proceedings of Machine Learning Research), Vol. 54. PMLR, Fort Lauderdale, FL, 1273--1282.Google Scholar
- Y. S. Can, N. Chalabianloo, D. Ekiz, J. Fernández-Álvarez, C. Repetto, G. Riva, H. Iles-Smith, and C. Ersoy. 2020. Real-life stress-level monitoring using smart bands in the light of contextual information. IEEE Sens. J. 20, 15 (2020), 8721--8730.Google ScholarCross Ref
- Giulia Regalia, Francesco Onorati, Matteo Lai, Chiara Caborni, and Rosalind W. Picard. 2019. Multimodal wrist-worn devices for seizure detection and advancing research: Focus on the Empatica wristbands. Epilep. Res. 153 (2019), 79--82.Google ScholarCross Ref
- E. H. Nirjhar, A. Behzadan, and T. Chaspari. 2020. Exploring bio-behavioral signal trajectories of state anxiety during public speaking. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’20). 1294--1298.Google Scholar
- Bahareh Nakisa. 2019. Emotion Classification Using Advanced Machine Learning Techniques Applied to Wearable Physiological Signals Data. Ph.D. Dissertation. Queensland University of Technology.Google Scholar
- Gregory A. Peters, Matthew L. Wong, and Leon D. Sanchez. 2020. Pedometer-measured physical activity among emergency physicians during shifts. Amer. J. Emerg. Med. 38, 1 (2020), 118--121.Google ScholarCross Ref
- Casey A. Cole, Dien Anshari, Victoria Lambert, James F. Thrasher, and Homayoun Valafar. 2017. Detecting smoking events using accelerometer data collected via smartwatch technology: Validation study. JMIR Mhealth Uhealth 5, 12 (13 Dec. 2017), e189.Google ScholarCross Ref
- Iván García-Magariño, Carlos Medrano, Inmaculada Plaza, and Bárbara Oliván. 2016. A smartphone-based system for detecting hand tremors in unconstrained environments. Person. Ubiq. Comput. 20, 6 (2016), 959--971.Google ScholarDigital Library
- Hangsik Shin and Jaegeol Cho. 2014. Unconstrained snoring detection using a smartphone during ordinary sleep. Biomed. Eng. Online 13, 1 (2014), 116.Google ScholarCross Ref
- Upender Kalwa, Christopher Legner, Taejoon Kong, and Santosh Pandey. 2019. Skin cancer diagnostics with an all-inclusive smartphone application. Symmetry 11, 6 (2019), 790.Google ScholarCross Ref
- Youngjun Cho, Simon J. Julier, and Nadia Bianchi-Berthouze. 2019. Instant stress: Detection of perceived mental stress through smartphone photoplethysmography and thermal imaging. JMIR Ment. Health 6, 4 (9 Apr. 2019), e10140.Google Scholar
- J. He, K. Li, X. Liao, P. Zhang, and N. Jiang. 2019. Real-time detection of acute cognitive stress using a convolutional neural network from electrocardiographic signal. IEEE Access 7 (2019), 42710--42717.Google ScholarCross Ref
- Francisco de Arriba-Pérez, Juan M. Santos-Gago, Manuel Caeiro-Rodríguez, and Mateo Ramos-Merino. 2019. Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables. J. Amb. Intell. Hum. Comput. 10, 12 (2019), 4925--4945.Google ScholarCross Ref
- Elena Vildjiounaite, Johanna Kallio, Vesa Kyllönen, Mikko Nieminen, Ilmari Määttänen, Mikko Lindholm, Jani Mäntyjärvi, and Georgy Gimel’farb. 2018. Unobtrusive stress detection on the basis of smartphone usage data. Person. Ubiq. Comput. 22, 4 (2018), 671--688.Google ScholarDigital Library
- Yekta Said Can, Niaz Chalabianloo, Deniz Ekiz, and Cem Ersoy. 2019. Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19, 8 (2019).Google Scholar
- Y. Chen, X. Qin, J. Wang, C. Yu, and W. Gao. 2020. FedHealth: A federated transfer learning framework for wearable healthcare. IEEE Intell. Syst. 35, 4 (2020), 83--93.Google ScholarCross Ref
- Yekta Said Can, Niaz Chalabianloo, Deniz Ekiz, and Cem Ersoy. 2019. Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19, 8 (2019). DOI:http://dx.doi.org/10.3390/s19081849Google Scholar
- R. W. Picard. 2016. Automating the recognition of stress and emotion: From lab to real-world impact. IEEE MultiMedia 23, 3 (July 2016), 3--7.Google ScholarDigital Library
- Martin Gjoreski, Mitja Luštrek, Matjaž Gams, and Hristijan Gjoreski. 2017. Monitoring stress with a wrist device using context. J. Biomed. Inf. 73 (2017), 159--170.Google ScholarDigital Library
- Mika P. Tarvainen, Juha-Pekka Niskanen, Jukka A. Lipponen, Perttu O. Ranta-Aho, and Pasi A. Karjalainen. 2014. Kubios HRV--heart rate variability analysis software. Comput. Meth. Prog. Biomed. 113, 1 (2014), 210--220.Google ScholarDigital Library
- Ane Alberdi, Asier Aztiria, and Adrian Basarab. 2016. Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review. J. Biomed. Inf. 59 (2016), 49--75.Google ScholarDigital Library
- Burcu Cinaz, Bert Arnrich, Roberto Marca, and Gerhard Tröster. 2013. Monitoring of mental workload levels during an everyday life office-work scenario. Person. Ubiq. Comput. 17, 2 (Feb. 2013), 229--239.Google Scholar
- Marcus Vollmer. 2019. HRVTool—An open-source Matlab toolbox for analyzing HRV. In Computing in Cardiology 2019, Vol. 46. IEEE.Google Scholar
- S. Greene, H. Thapliyal, and A. Caban-Holt. 2016. A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Consum. Electron. Mag. 5, 4 (Oct. 2016), 44--56.Google ScholarCross Ref
- Nicholas R. Lomb. 1976. Least-squares frequency analysis of unequally spaced data. Astrophys. Space Sci. 39, 2 (1976), 447--462.Google ScholarCross Ref
- Muhammad Atif Tahir, Josef Kittler, Krystian Mikolajczyk, and Fei Yan. 2009. A multiple expert approach to the class imbalance problem using inverse random under sampling. In Multiple Classifier Systems, Jón Atli Benediktsson, Josef Kittler, and Fabio Roli (Eds.). Springer Berlin, 82--91.Google Scholar
- François Chollet et al. 2015. Keras. Retrieved from: https://keras.io.Google Scholar
- Adrian Nilsson, Simon Smith, Gregor Ulm, Emil Gustavsson, and Mats Jirstrand. 2018. A performance evaluation of federated learning algorithms. In Proceedings of the Second Workshop on Distributed Infrastructures for Deep Learning (DIDL'18). Association for Computing Machinery, 1--8.Google ScholarDigital Library
- Federated Biomedical Informatics Github Repository. 2020. Retrieved from: https://github.com/ysaidcan/federated-biomedical-informatics.Google Scholar
- 2019. Google, TensorFlow Federated. https://www.tensorflow.org/federated.Google Scholar
- Ian R. Kleckner, Mallory J. Feldman, Matthew S. Goodwin, and Karen S. Quigley. 2020. Framework for selecting and benchmarking mobile devices in psychophysiological research. Behavior Research Methods. Springer, 1--18.Google Scholar
- Luca Menghini, Evelyn Gianfranchi, Nicola Cellini, Elisabetta Patron, Mariaelena Tagliabue, and Michela Sarlo. 2019. Stressing the accuracy: Wrist-worn wearable sensor validation over different conditions. Psychophysiology 56, 11 (2019), e13441.Google ScholarCross Ref
- R. K. Nath, H. Thapliyal, and A. Caban-Holt. 2020. Validating physiological stress detection model using cortisol as stress bio marker. In Proceedings of the IEEE International Conference on Consumer Electronics (ICCE’20). 1--5.Google Scholar
- Y. S. Can, N. Chalabianloo, D. Ekiz, J. Fernandez-Alvarez, G. Riva, and C. Ersoy. 2020. Personal stress-level clustering and decision-level smoothing to enhance the performance of ambulatory stress detection with smartwatches. IEEE Access (2020), 1--1. DOI:http://dx.doi.org/10.1109/ACCESS.2020.2975351Google Scholar
- Sandra G. Hart. 1986. NASA task load index (TLX); 20 years later. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 50. Sage publications Sage CA, 904--908.Google Scholar
- Clemens Kirschbaum, K.-M. Pirke, and Dirk H. Hellhammer. 1993. The “Trier Social Stress Test”—A tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 28, 1–2 (1993), 76--81.Google ScholarCross Ref
- Yekta Said Can, Dilara Gokay, Dilruba Reyyan Kılıç, Deniz Ekiz, Niaz Chalabianloo, and Cem Ersoy. 2020. How laboratory experiments can be exploited for monitoring stress in the wild: A bridge between laboratory and daily life. Sensors 20, 3 (2020).Google Scholar
- Sheldon Cohen, Tom Kamarck, and Robin Mermelstein. 1983. A global measure of perceived stress. J. Health Soc. Behav. 24, 4 (1983), 385--396.Google ScholarCross Ref
- K. Plarre, A. Raij, S. M. Hossain, A. A. Ali, M. Nakajima, M. Al’absi, E. Ertin, T. Kamarck, S. Kumar, M. Scott, D. Siewiorek, A. Smailagic, and L. E. Wittmers. 2011. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks. 97--108.Google Scholar
- Anirudh Kasturi, Anish Reddy Ellore, and Chittaranjan Hota. 2020. Fusion learning: A one shot federated learning. In Computational Science—ICCS 2020, Valeria V. Krzhizhanovskaya, Gábor Závodszky, Michael H. Lees, Jack J. Dongarra, Peter M. A. Sloot, Sérgio Brissos, and João Teixeira (Eds.). Springer International Publishing, Cham, 424--436.Google Scholar
Index Terms
- Privacy-preserving Federated Deep Learning for Wearable IoT-based Biomedical Monitoring
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