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A privacy-preserved IoMT-based mental stress detection framework with federated learning

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Abstract

Internet of Medical Things (IoMT) can be leveraged for periodic sensing and recording of different health parameters using sensors, wireless communications, and computation platforms. Health care systems can be enhanced by using IoMT for remote patient monitoring and data-driven diagnosis powered by machine learning algorithms. In the context of IoMT, federated learning (FL) is an excellent choice to manage machine learning (ML) algorithms to drive this analysis. This is because FL models can be trained in a distributed manner on local heterogeneous datasets that all contribute to the "collective wisdom". The model parameters can be regulated and shared without sharing the actual health data, ensuring confidentiality and security. This paper makes a case for the viability of FL-based analysis of data acquired via IoMT by presenting some use cases and recent work in this area and proposing a novel framework for data analysis using FL specifically in the context of mental stress detection. It shows that FL-based methods can significantly reduce the required communication overhead for each local device from 10.02MB/day up to only 754B/day as compared to non-FL techniques.

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References

  1. Khan WU, Javed MA, Nguyen TN, Khan S, Elhalawany BM (2021) Energy-efficient resource allocation for 6g backscatter-enabled noma iov networks. IEEE Trans Intell Transp Syst 5:1–11. https://doi.org/10.1109/TITS.2021.3110942

    Article  Google Scholar 

  2. Javed MA, Nguyen TN, Mirza J, Ahmed J, Ali B (2022) Reliable communications for cybertwin driven 6g iovs using intelligent reflecting surfaces. IEEE Trans Indust Inform 1:1–1. https://doi.org/10.1109/TII.2022.3151773

    Article  Google Scholar 

  3. Dash TK, Chakraborty C, Mahapatra S, Panda G (2022) Gradient boosting machine and efficient combination of features for speech-based detection of covid-19. IEEE J Biomed Health Inform 25:1–8. https://doi.org/10.1109/JBHI.2022.3197910

    Article  Google Scholar 

  4. Rathee G, Saini H, Kerrache CA, Herrera-Tapia J (2022) A computational framework for cyber threats in medical iot systems. Electronics 11:1156

    Article  Google Scholar 

  5. Chen D, Zhuang Y, Huai J, Sun X, Yang X, Awais Javed M, Brown J, Sheng Z, Thompson J (2021) Coexistence and interference mitigation for wpans and wlans from traditional approaches to deep learning: A review. IEEE Sens J 21(22):25561–25589. https://doi.org/10.1109/JSEN.2021.3117399

    Article  Google Scholar 

  6. Čolaković A, Hadžialić M (2018) Internet of things (iot): A review of enabling technologies, challenges, and open research issues. Comput Netw 144:17–39

    Article  Google Scholar 

  7. Shafique K, Khawaja BA, Sabir F, Qazi S, Mustaqim M (2020) Internet of things (iot) for next-generation smart systems: A review of current challenges, future trends and prospects for emerging 5g-iot scenarios. IEEE Access 8:23022–23040

    Article  Google Scholar 

  8. Saba T, Haseeb K, Shah AA, Rehman A, Tariq U, Mehmood Z (2021) A machine-learning-based approach for autonomous iot security. IT Professional 23(3):69–75

    Article  Google Scholar 

  9. Haseeb K, Rehman A, Saba T, Bahaj SA, Lloret J (2022) Device-to-device (d2d) multi-criteria learning algorithm using secured sensors. Sensors 22(6):2115

    Article  Google Scholar 

  10. Yan Z, Wicaksana J, Wang Z, Yang X, Cheng K-T (2021) Variation-aware federated learning with multi-source decentralized medical image data. IEEE J Biomed Health Inf 25(7):2615–2628. https://doi.org/10.1109/JBHI.2020.3040015

    Article  Google Scholar 

  11. Zhang W, Zhou T, Lu Q, Wang X, Zhu C, Sun H, Wang Z, Lo SK, Wang F-Y (2021) Dynamic-fusion-based federated learning for covid-19 detection. IEEE Internet Things J 8(21):15884–15891. https://doi.org/10.1109/JIOT.2021.3056185

    Article  Google Scholar 

  12. Xu Z, Guo Y, Chakraborty C, Hua Q, Chen S, Yu K (2022) A simple federated learning-based scheme for security enhancement over internet of medical things. IEEE J Biomed Health Inform 5:1–13. https://doi.org/10.1109/JBHI.2022.3187471

    Article  Google Scholar 

  13. Barka E, Dahmane S, Kerrache CA, Khayat M, Sallabi F (2021) Sthm: A secured and trusted healthcare monitoring architecture using sdn and blockchain. Electronics 10, (15)

  14. Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM (2022) Federated machine learning for detection of skin diseases and enhancement of internet of medical things (iomt) security. IEEE J Biomed Health Inform 2:1–1. https://doi.org/10.1109/JBHI.2022.3149288

    Article  Google Scholar 

  15. Yang Y, Wang X, Ning Z, Rodrigues JJPC, Jiang X, Guo Y (2021) Edge learning for internet of medical things and its covid-19 applications: A distributed 3c framework. IEEE Internet Things Mag 4(3):18–23. https://doi.org/10.1109/IOTM.0100.2000154

    Article  Google Scholar 

  16. Rodrigues JJ, Segundo DBDR, Junqueira HA, Sabino MH, Prince RM, Al-Muhtadi J, De Albuquerque VHC (2018) Enabling technologies for the internet of health things. IEEE Access 6:13129–13141

    Article  Google Scholar 

  17. Javed MA, Zeadally S (2018) Repguide: Reputation-based route guidance using internet of vehicles. IEEE Commun Stand Mag 2(4):81–87. https://doi.org/10.1109/MCOMSTD.2018.1800040

    Article  Google Scholar 

  18. Razdan S, Sharma S (2021) Internet of medical things (iomt): Overview, emerging technologies, and case studies. IETE Tech Rev 2:1–14

    Google Scholar 

  19. Wang Y, Cang S, Yu H (2019) A survey on wearable sensor modality centred human activity recognition in health care. Expert Syst Appl 137:167–190

    Article  Google Scholar 

  20. Hernandez L, Cao H, Wachowicz M (2017) Implementing an edge-fog-cloud architecture for stream data management. In: 2017 IEEE Fog World Congress (FWC), pp. 1–6. IEEE

  21. Motti VG (2020) Introduction to wearable computers, 1–39

  22. Mutlag AA, Abd Ghani MK, Na Arunkumar, Mohammed MA, Mohd O (2019) Enabling technologies for fog computing in healthcare iot systems. Future Gen Comput Syst 90:62–78

    Article  Google Scholar 

  23. Elhadad A, Alanazi F, Taloba AI, Abozeid A (2022) Fog computing service in the healthcare monitoring system for managing the real-time notification. J Healthc Eng 2022:5337733

    Article  Google Scholar 

  24. Farahani B, Barzegari M, Aliee FS, Shaik KA (2020) Towards collaborative intelligent iot ehealth: From device to fog, and cloud. Microprocess Microsyst 72:102938

    Article  Google Scholar 

  25. Moghadas E, Rezazadeh J, Farahbakhsh R (2020) An iot patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase. Internet Things 11:100251

    Article  Google Scholar 

  26. Karthick G, Pankajavalli P (2020) Architecting iot based healthcare systems using machine learning algorithms: cloud-oriented healthcare model, streaming data analytics architecture, and case study, 40–66 (2020)

  27. AlShorman O, AlShorman B, Alkhassaweneh M, Alkahtani F (2020) A review of internet of medical things (iomt)-based remote health monitoring through wearable sensors: A case study for diabetic patients. Indones J Electr Eng Comput Sci 20(1):414–422

    Google Scholar 

  28. Syed L, Jabeen S, Manimala S, Alsaeedi A (2019) Smart healthcare framework for ambient assisted living using iomt and big data analytics techniques. Futur Gen Comput Syst 101:136–151

    Article  Google Scholar 

  29. Awotunde JB, Ogundokun RO, Misra S (2021) Cloud and iomt-based big data analytics system during covid-19 pandemic, 181–201

  30. Bharathi M, Amsaveni A (2021) Machine learning with iomt: Opportunities and research challenges, 235–252

  31. Yu Z, Amin SU, Alhussein M, Lv Z (2021) Research on disease prediction based on improved deepfm and iomt. IEEE Access 9:39043–39054

    Article  Google Scholar 

  32. Awotunde JB, Ajagbe SA, Idowu IR, Ndunagu JN (2021) An enhanced cloud-iomt-based and machine learning for effective covid-19 diagnosis system, 55–76

  33. Ghantasala GP, Kumari NV, Patan R (2021) Cancer prediction and diagnosis hinged on hcml in iomt environment, 179–207

  34. Zamkah A, Hui T, Andrews S, Dey N, Shi F, Sherratt RS (2020) Identification of suitable biomarkers for stress and emotion detection for future personal affective wearable sensors. Biosensors 10(4):40

    Article  Google Scholar 

  35. Costa A, Rincon JA, Carrascosa C, Julian V, Novais P (2019) Emotions detection on an ambient intelligent system using wearable devices. Futur Gener Comput Syst 92:479–489

    Article  Google Scholar 

  36. Gupta D, Bhatia M, Kumar A (2021) Resolving data overload and latency issues in multivariate time-series iomt data for mental health monitoring. IEEE Sens J 21(22):25421–25428

    Article  Google Scholar 

  37. Sundaravadivel P, Salvatore P, Indic P (2020) M-sid: an iot-based edge-intelligent framework for suicidal ideation detection. In: 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1–6. IEEE

  38. McDonald AD, Sasangohar F, Jatav A, Rao AH (2019) Continuous monitoring and detection of post-traumatic stress disorder (ptsd) triggers among veterans: a supervised machine learning approach. IISE Trans Healthc Syst Eng 9(3):201–211

    Article  Google Scholar 

  39. Carreiro S, Chintha KK, Shrestha S, Chapman B, Smelson D, Indic P (2020) Wearable sensor-based detection of stress and craving in patients during treatment for substance use disorder: A mixed methods pilot study. Drug Alcohol Depend 209:107929

    Article  Google Scholar 

  40. Can YS, Arnrich B, Ersoy C (2019) Stress detection in daily life scenarios using smart phones and wearable sensors: A survey. J Biomed Inform 92:103139

    Article  Google Scholar 

  41. Jameel F, Javed MA, Zeadally S, Jäsntti R (2022) Secure transmission in cellular v2x communications using deep q-learning. IEEE Trans Intell Transp Syst 4:1–10. https://doi.org/10.1109/TITS.2022.3165791

    Article  Google Scholar 

  42. Javed MA, Zeadally S, Hamid Z (2018) Trust-based security adaptation mechanism for vehicular sensor networks. Comput Netw 137:27–36

    Article  Google Scholar 

  43. Zhang C, Xie Y, Bai H, Yu B, Li W, Gao Y (2021) A survey on federated learning. Knowl Based Syst 216:106775

    Article  Google Scholar 

  44. Javed MA, Hamida EB, Al-Fuqaha A, Bhargava B (2018) Adaptive security for intelligent transport system applications. IEEE Intell Transp Syst Mag 10(2):110–120. https://doi.org/10.1109/MITS.2018.2806636

    Article  Google Scholar 

  45. Javed MA, Hamida EB, Znaidi W (2016) Security in intelligent transport systems for smart cities: From theory to practice. Sensors 16(6):879

    Article  Google Scholar 

  46. Papaioannou M, Karageorgou M, Mantas G, Sucasas V, Essop I, Rodriguez J, Lymberopoulos D (2022) A survey on security threats and countermeasures in internet of medical things (iomt). Trans Emerg Telecommun Technol 33(6):4049

    Article  Google Scholar 

  47. Anwar S, Mohamad Zain J, Zolkipli MF, Inayat Z, Khan S, Anthony B, Chang V (2017) From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms 10(2):39

    Article  Google Scholar 

  48. Koutras D, Stergiopoulos G, Dasaklis T, Kotzanikolaou P, Glynos D, Douligeris C (2020) Security in iomt communications: A survey. Sensors 20(17):4828

    Article  Google Scholar 

  49. Cao L, Jiang X, Zhao Y, Wang S, You D, Xu X (2020) A survey of network attacks on cyber-physical systems. IEEE Access 8:44219–44227

    Article  Google Scholar 

  50. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: Concept and applications. ACM Trans Intell Syst Technol TIST 10(2):1–19

    Article  Google Scholar 

  51. Alazab M, R M, SP, M P, Reddy P, Gadekallu TR, Pham Q-V (2021) Federated learning for cybersecurity: Concepts, challenges and future directions. IEEE Trans Indust Inform 1–1 https://doi.org/10.1109/TII.2021.3119038

  52. Nguyen DC, Ding M, Pathirana PN, Seneviratne A, Li J, Poor HV (2021) Federated learning for internet of things: A comprehensive survey. IEEE Commun Surveys Tutor 23(3):1622–1658

    Article  Google Scholar 

  53. Stress a major health problem in the U.S., warns APA. American Psychological Association. https://www.apa.org/news/press/releases/2007/10/stress

  54. Work-related ill health and occupational disease in Great Britain. https://www.hse.gov.uk/statistics/causdis/

  55. Alexander R. Illnesses caused by stress. https://www.everydayhealth.com/emotional-health/stress/illnesses-caused-stress/

  56. Stress: Signs, symptoms, management & prevention. https://my.clevelandclinic.org/health/articles/11874-stress

  57. Link, R.: The signs and symptoms of too much stress. Healthline Media (2021). https://www.healthline.com/nutrition/symptoms-of-stress

  58. Can YS, Chalabianloo N, Ekiz D, Ersoy C (2019) Continuous stress detection using wearable sensors in real life: Algorithmic programming contest case study. Sensors 19(8):59565

    Article  Google Scholar 

  59. Schmidt P, Reiss A, Duerichen R, Marberger C, Van Laerhoven K (2018) Introducing wesad, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400–408

  60. Anusha A, Sukumaran P, Sarveswaran V, Shyam A, Akl TJ, Preejith S, Sivaprakasam M et al (2019) Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE J Biomed Health Inform 24(1):92–100

    Google Scholar 

  61. Shafiq G, Tatinati S, Ang WT, Veluvolu KC (2016) Automatic identification of systolic time intervals in seismocardiogram. Sci Rep 6(1):37524

    Article  Google Scholar 

  62. Shafiq G, Veluvolu KC (2017) Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications. Sci Data 4(1):1–12

    Google Scholar 

  63. Farhad A, Woolley S, Andras P (2021) Federated learning for ai to improve patient care using wearable and iomt sensors. In: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI), pp. 434–434. IEEE

  64. Samuel O, Omojo A, Onuja A, Sunday Y, Tiwari P, Gupta D, Hafeez G, Yahaya A, Fatoba O, Shamshirband S (2022) Iomt: A covid-19 healthcare system driven by federated learning and blockchain. IEEE J Biomed Health Inform

  65. Hossen MN, Panneerselvam V, Koundal D, Ahmed K, Bui FM, Ibrahim SM (2022) Federated machine learning for detection of skin diseases and enhancement of internet of medical things (iomt) security. IEEE J Biomed Health Inform

  66. Ali M, Naeem F, Tariq M, Kaddoum G (2022) Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey. arXiv preprint arXiv:2203.09702

  67. Pourmohammadi S, Maleki A (2020) Stress detection using ecg and emg signals: A comprehensive study. Computer Methods Programs Biomed 193:105482

    Article  Google Scholar 

  68. Lim WYB, Luong NC, Hoang DT, Jiao Y, Liang Y-C, Yang Q, Niyato D, Miao C (2020) Federated learning in mobile edge networks: A comprehensive survey. IEEE Commun Surveys Tutor 22(3):2031–2063

    Article  Google Scholar 

  69. Anusha A, Jose J, Preejith S, Jayaraj J, Mohanasankar S (2018) Physiological signal based work stress detection using unobtrusive sensors. Biomed Phys Eng Express 4(6):065001

    Article  Google Scholar 

  70. Zontone P, Affanni A, Bernardini R, Piras A, Rinaldo R (2019) Stress detection through electrodermal activity (eda) and electrocardiogram (ecg) analysis in car drivers. In: 2019 27th European Signal Processing Conference (EUSIPCO), pp. 1–5. IEEE

  71. Huang L, Yin Y, Fu Z, Zhang S, Deng H, Liu D (2020) Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data. Plos one 15(4):0230706

    Article  Google Scholar 

  72. Sarkar P, Etemad A (2020) Self-supervised ecg representation learning for emotion recognition. IEEE Trans Affect Comput 13(3):1541–1554

    Article  Google Scholar 

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Acknowledgement

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number 445-9-698.

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Authors and Affiliations

  1. Department of Computer Science, Taibah University, 42353, Medina, Saudi Arabia

    Abdulrahman Alahmadi, Mohammad Zubair Khan & Ahmed H. Alahmadi

  2. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, 45550, Pakistan

    Haroon Ahmed Khan, Ghufran Shafiq, Junaid Ahmed, Bakhtiar Ali & Muhammad Awais Javed

  3. Department of Electrical Engineering, Islamic University of Madinah, 41411, Madinah, Saudi Arabia

    Rayan Hamza Alsisi

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  1. Abdulrahman Alahmadi
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  2. Haroon Ahmed Khan
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  4. Junaid Ahmed
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Contributions

A.A., H.A., G.S., J.A., B.A., M.A.J., M.Z.K., R.H.A., A.H.A. developed the concept and framework A.A., H.A., G.S., J.A., B.A., M.A.J. carried out simulation work A.A., H.A., G.S., J.A., B.A., M.A.J. wrote the original draft M.Z.K., R.H.A., A.H.A. reviewed the manuscript and provided valuable comments.

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Correspondence to Abdulrahman Alahmadi, Mohammad Zubair Khan or Rayan Hamza Alsisi.

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Alahmadi, A., Khan, H.A., Shafiq, G. et al. A privacy-preserved IoMT-based mental stress detection framework with federated learning. J Supercomput (2023). https://doi.org/10.1007/s11227-023-05847-3

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