Skip to main content

Wireless Body Sensor Networks: Applications, Challenges, Patient Monitoring, Decision Making, and Machine Learning in Medical Applications

  • Chapter
  • First Online:
AI and IoT for Sustainable Development in Emerging Countries

Abstract

The power of the networks of Wireless Body Sensor are too huge to ignore. WBSNs have promised to enable their innovative, vast, simultaneous, and accurate monitoring applications in a variety of fields including health care, fitness and sport training, social interactions, and the monitoring of industrial workers. The objective of this paper is to lend some understanding on the scientific background of WBSNs and presenting recent advances in this field especially applications focus on remote monitoring for elderly and chronically diseases patients. In order to fulfillment the scientific concept of WBSN, a comprehensive study involving WBSNs architecture, challenges, healthcare applications and their requirements. Following, discussing the most important characteristics of the WBSN includes data collecting, fusion, risk evaluation and decision making. Moreover, shedding lights on machine learning techniques and their role in medical application. Finally, the paper recommends that the awareness of relevant issues and future development of WBSNs are regarded as a perfect solution to monitor the patient’s life.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gandhi V, Singh J (2020) An automated review of body sensor networks research patterns and trends. J Ind Inf Integr 18:100132

    Google Scholar 

  2. Gandhi V, Singh J (2020) WBSN based safe lifestyle: a case study of heartrate monitoring system. Int J Electr Comput Eng 10(3):2296

    Google Scholar 

  3. Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2018) Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Future Gener Comput Syst 78:641–658

    Google Scholar 

  4. Idrees AK, Al-Yaseen WL, Taam MA, Zahwe O (2018) Distributed data aggregation based modified k-means technique for energy conservation in periodic wireless sensor networks. In: 2018 IEEE Middle East and North Africa communications conference (MENACOMM). IEEE, pp 1–6

    Google Scholar 

  5. Malathy S, Rastogi R, Maheswar R, Kanagachidambaresan GR, Sundararajan TVP, Vigneswaran D (2019) A novel energy-efficient framework (NEEF) for the wireless body sensor network. J Supercomput 1–16

    Google Scholar 

  6. Luo K, Cai Z, Du K, Zou F, Zhang X, Li J (2018) A digital compressed sensing-based energy-efficient single-spot Bluetooth ECG node. J Healthc Eng

    Google Scholar 

  7. Al-Nassrawy KK, Al-Shammary D, Idrees AK (2020) High performance fractal compression for EEG health network traffic. Procedia Comput Sci 167:1240–1249

    Google Scholar 

  8. Azar J, Habib C, Darazi R, Makhoul A, Demerjian J (2018) Using adaptive sampling and DWT lifting scheme for efficient data reduction in wireless body sensor networks. In: 2018 14th International conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 1–8

    Google Scholar 

  9. Habib C, Makhoul A, Darazi R, Couturier R (2017) Real-time sampling rate adaptation based on continuous risk level evaluation in wireless body sensor networks. In: 2017 IEEE 13th International conference on wireless and mobile computing, networking and communications (WiMob). IEEE, pp 1–8

    Google Scholar 

  10. Mehrani M, Attarzadeh I, Hosseinzadeh M (2020) Sampling rate prediction of biosensors in wireless body area networks using deep-learning methods. Simul Model Pract Theory 105:102101

    Google Scholar 

  11. Rendon E, Alejo R, Castorena C, Isidro-Ortega FJ, Granda-Gutierrez EE (2020) Data sampling methods to deal with the big data multi-class imbalance problem. Appl Sci 10(4):1276

    Google Scholar 

  12. Johnson JM, Khoshgoftaar TM (2019) Deep learning and data sampling with imbalanced big data. In: 2019 IEEE 20th International conference on information reuse and integration for data science (IRI). IEEE, pp 175–183

    Google Scholar 

  13. Vitabile S, Marks M, Stojanovic D, Pllana S, Molina JM, Krzyszton M, Sikora A, Jarynowski A, Hosseinpour F, Jakobik A et al (2019) Medical data processing and analysis for remote health and activities monitoring. In: High-performance modelling and simulation for big data applications, pp 186–220. Springer, Cham

    Google Scholar 

  14. Scirè A, Tropeano F, Anagnostopoulos A, Chatzigiannakis I (2019) Fog-computing-based heartbeat detection and arrhythmia classification using machine learning. Algorithms 12(2):32

    Google Scholar 

  15. Dautov R, Distefano S, Buyya R (2019) Hierarchical data fusion for smart healthcare. J Big Data 6(1):1–23

    Google Scholar 

  16. Navarro J, Vidaña-Vila E, Alsina-Pagès RM, Hervás M (2018) Real-time distributed architecture for remote acoustic elderly monitoring in residential-scale ambient assisted living scenarios. Sensors 18(8):2492

    Google Scholar 

  17. Khan RA, Pathan A-SK (2018) The state-of-the-art wireless body area sensor networks: a survey. Int J Distrib Sens Netw 14(4):1550147718768994

    Google Scholar 

  18. Jaber AS, Idrees AK (2021) Energy-saving multisensor data sampling and fusion with decision-making for monitoring health risk using WBSNs. Softw Pract Exp 51(2):271–293

    Google Scholar 

  19. Jaber AS, Idrees AK (2020) Adaptive rate energy-saving data collecting technique for health monitoring in wireless body sensor networks. Int J Commun Syst 33(17):e4589

    Google Scholar 

  20. VTLAB (2021) Vitaltracer. https://vitaltracer.com. Accessed 16 Apr 2021

  21. CamNtech (2020) Camntech. https://www.camntech.com/about-us. Accessed 20 Apr 2021

  22. Hillrom Extended Care Solution (2021) Hillrom extended care solution. https://www.hillrom.com/en/products/hillrom-extended-care-solution/. Accessed 20 Apr 2021

  23. BiPS Medical (2018) Bips medical. https://www.bipsmed.com/. Accessed 16 Apr 2021

  24. Hexoskin (2021) The Hexoskin smart clothing monitor. https://www.hexoskin.com/. Accessed 19 Apr 2021

  25. Shimmer (2021) Shimmer wearable technology. http://www.shimmersensing.com/. Accessed 18 Apr 2021

  26. Yazdi FR, Hosseinzadeh M, Jabbehdari S (2017) A review of state-of-the-art on wireless body area networks. Int J Adv Comput Sci Appl 11:443–455

    Google Scholar 

  27. Shokeen S, Parkash D (2019) A systematic review of wireless body area network. In: 2019 International conference on automation, computational and technology management (ICACTM). IEEE, pp 58–62

    Google Scholar 

  28. Abualsaud K, Chowdhury MEH, Gehani A, Yaacoub E, Khattab T, Hammad J (2020) A new wearable ECG monitor evaluation and experimental analysis: proof of concept. In: 2020 International wireless communications and mobile computing (IWCMC). IEEE, pp 1885–1890

    Google Scholar 

  29. Almusallam M, Soudani A (2017) Feature-based ECG sensing scheme for energy efficiency in WBSN. In: 2017 International conference on informatics, health & technology (ICIHT). IEEE, pp 1–6

    Google Scholar 

  30. Ascioglu G, Senol Y (2020) Design of a wearable wireless multi-sensor monitoring system and application for activity recognition using deep learning. IEEE Access 8:169183–169195

    Google Scholar 

  31. Wang H, Yan W, Liu S (2019) Physical activity recognition using multi-sensor fusion and extreme learning machines. In: 2019 International joint conference on neural networks (IJCNN). IEEE, pp 1–7

    Google Scholar 

  32. Yildiz S, Opel RA, Elliott JE, Kaye J, Cao H, Lim MM (2019) Categorizing sleep in older adults with wireless activity monitors using LSTM neural networks. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, pp 3368–3372

    Google Scholar 

  33. Arulvallal S, Snekhalatha U, Rajalakshmi T (2019) Design and development of wearable device for continuous monitoring of sleep apnea disorder. In: 2019 International conference on communication and signal processing (ICCSP). IEEE, pp 0050–0053

    Google Scholar 

  34. Saadeh W, Butt SA, Bin Altaf MA (2019) A patient-specific single sensor IoT-based wearable fall prediction and detection system. IEEE Trans Neural Syst Rehabil Eng 27(5):995–1003

    Google Scholar 

  35. Desai K, Mane P, Dsilva M, Zare A, Shingala P, Ambawade D (2020) A novel machine learning based wearable belt for fall detection. In: 2020 IEEE International conference on computing, power and communication technologies (GUCON). IEEE, pp 502–505

    Google Scholar 

  36. Ghosh A, Rahman N, Awadalla N, Sagahyroon A, Aloul F, Dhou S (2020) Asthma diagnosis using neuro-fuzzy techniques. In: 2020 Advances in science and engineering technology international conferences (ASET). IEEE, pp 1–4

    Google Scholar 

  37. Tsang KCH, Pinnock H, Wilson AM, Shah SA (2020) Application of machine learning to support self-management of asthma with mhealth. In: 2020 42nd Annual international conference of the IEEE engineering in medicine & biology society (EMBC). IEEE, pp 5673–5677

    Google Scholar 

  38. Hata R, Kato T, Yaku H, Morimoto T, Kawase Y, Yamamoto E, Inuzuka Y, Tamaki Y, Ozasa N, Yoshikawa Y et al (2021) Implantable cardioverter defibrillator therapy in patients with acute decompensated heart failure with reduced ejection fraction: an observation from the KCHF registry. J Cardiol 77(3):292–299

    Google Scholar 

  39. Hasan RR, Rahman MdA, Sinha S, Uddin MdN, Niloy T-SR (2019) In body antenna for monitoring pacemaker. In: 2019 International conference on automation, computational and technology management (ICACTM). IEEE, pp 99–102

    Google Scholar 

  40. El Kheshen H, Deni I, Baalbaky A, Dib M, Hamawy L, Ali MA (2018) Semi-automated self-monitore-syringe infusion pump. In: 2018 International conference on computer and applications (ICCA). IEEE, pp 331–335

    Google Scholar 

  41. Reza Pazhouhandeh M, Chang M, Valiante TA, Genov R (2020) Track-and-zoom neural analog-to-digital converter with blind stimulation artifact rejection. IEEE J Solid-State Circ 55(7):1984–1997

    Google Scholar 

  42. Islam MdM, Maniur SM (2019) Design and implementation of a wearable system for non-invasive glucose level monitoring. In: 2019 IEEE International conference on biomedical engineering, computer and information technology for health (BECITHCON), pp 29–32

    Google Scholar 

  43. Verner A, Butvinik D (2017) A machine learning approach to detecting sensor data modification intrusions in WBANs. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA). IEEE, pp 161–169

    Google Scholar 

  44. Welikala RA, Remagnino P, Lim JH, Chan CS, Rajendran S, Kallarakkal TG, Zain RB, Jayasinghe RD, Rimal J, Kerr AR et al (2020) Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access 8:132677–132693

    Google Scholar 

  45. Abtoy A, Touhafi A, Tahiri A et al (2020) Ambient assisted living system’s models and architectures: a survey of the state of the art. J King Saud Univ Comput Inf Sci 32(1):1–10

    Google Scholar 

  46. Gingras G, Adda M, Bouzouane A, Ibrahim H, Dallaire C (2020) IoT ambient assisted living: scalable analytics architecture and flexible process. Procedia Comput Sci 177:396–404

    Google Scholar 

  47. Wang T, Cook DJ (2020) Toward unsupervised multiresident tracking in ambient assisted living: methods and performance metrics. In: Assistive technology for the elderly. Elsevier, Amsterdam, pp 249–280

    Google Scholar 

  48. Kruse CS, Williams K, Bohls J, Shamsi W (2021) Telemedicine and health policy: a systematic review. Health Policy Technol 10(1):209–229

    Google Scholar 

  49. Field MJ et al (1996) Telemedicine: a guide to assessing telecommunications for health care

    Google Scholar 

  50. Garichev S, Klassen V, Natenzon M, Safin A, Sergeev S (2019) Mobile telemedicine systems with artificial medical intelligence. In: 2019 International conference on artificial intelligence: applications and innovations (IC-AIAI). IEEE, pp 8–83

    Google Scholar 

  51. Nasri F, Mtibaa A (2017) Smart mobile healthcare system based on WBSN and 5G. Int J Adv Comput Sci Appl 8(10):147–156

    Google Scholar 

  52. Amin R, Saha TS, Hassan MdFB, Anjum M, Tahmid MdI (2020) IoT based medical assistant for efficient monitoring of patients in response to covid-19. In: 2020 2nd International conference on advanced information and communication technology (ICAICT). IEEE, pp 83–87

    Google Scholar 

  53. Qureshi KN, Tayyab MQ, Rehman SU, Jeon G (2020) An interference aware energy efficient data transmission approach for smart cities healthcare systems. Sustain Cities Soc 62:102392

    Google Scholar 

  54. Almajed HN, Almogren AS, Altameem A (2019) A resilient smart body sensor network through pyramid interconnection. IEEE Access 7:51039–51046

    Google Scholar 

  55. Rateb AM (2020) A fast compressed sensing decoding technique for remote ECG monitoring systems. IEEE Access 8:197124–197133

    Google Scholar 

  56. Maheswar R, Maria AR, Sheriff N, Mahima V, Kanagachidambaresan GR, Lakshmi M (2019) Mobility aware next hop selection algorithm (MANSA) for wireless body sensor network. In: 2019 10th International conference on computing, communication and networking technologies (ICCCNT). IEEE, pp 1–5

    Google Scholar 

  57. Liu Q, Mkongwa KG, Zhang C (2021) Performance issues in wireless body area networks for the healthcare application: a survey and future prospects. SN Appl Sci 3(2):1–1

    Google Scholar 

  58. Cho Y, Shin H, Kang K (2018) Scalable coding and prioritized transmission of ECG for low-latency cardiac monitoring over cellular M2M networks. IEEE Access 6:8189–8200

    Google Scholar 

  59. Ali HQ, Ghani S (2020) Multi-sensor based mk/hyperk/1/m queuing model for heterogeneous traffic. Comput Netw 181:107512

    Google Scholar 

  60. Manojprabu M, Sarma Dhulipala VR (2020) Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed architecture to remove artifact. Comput Commun 152:266–271

    Google Scholar 

  61. Arfaoui A, Kribeche A, Senouci SM, Hamdi M (2019) Game-based adaptive anomaly detection in wireless body area networks. Comput Netw 163:106870

    Google Scholar 

  62. Sari A, Alzubi A (2018) Path loss algorithms for data resilience in wireless body area networks for healthcare framework. In: Security and resilience in intelligent data-centric systems and communication networks. Elsevier, Amsterdam, pp 285–313

    Google Scholar 

  63. International Commission on Non-ionizing Radiation Protection et al (2020) Guidelines for limiting exposure to electromagnetic fields (100 kHz to 300 gHz). Health Phys 118(5):483–524

    Google Scholar 

  64. Hasan K, Biswas K, Ahmed K, Nafi NS, Islam MdS (2019) A comprehensive review of wireless body area network. J Netw Comput Appl 143:178–198

    Google Scholar 

  65. Karaboytcheva M (2020) Effects of 5g wireless communication on human health. Eur Parliam Res Serv PE 646:172

    Google Scholar 

  66. Federal Communications Commission (2019) Radio frequency safety. https://www.fcc.gov/general/radio-frequency-safety-0. Accessed 2 Apr 2021

  67. Asam M, Ajaz A, Jamal T, Adeel M, Hassan A, Butt SA, Gulzar M (2019) Challenges in wireless body area network. Proc Int J Adv Comput Sci Appl 10(11)

    Google Scholar 

  68. Selem E, Fatehy M, El-Kader SMA (2021) mobTHE (mobile temperature heterogeneity energy) aware routing protocol for WBAN IoT health application. IEEE Access 9:18692–18705

    Google Scholar 

  69. Salayma M, Al-Dubai A, Romdhani I, Nasser Y (2017) Wireless body area network (WBAN) a survey on reliability, fault tolerance, and technologies coexistence. ACM Comput Surv (CSUR) 50(1):1–38

    Google Scholar 

  70. El Salamouny MY (2018) Fault tolerance in WBAN applications

    Google Scholar 

  71. Georgopoulos VC, Stylios CD (2017) Fuzzy cognitive maps for decision making in triage of non-critical elderly patients. In: 2017 International conference on intelligent informatics and biomedical sciences (ICIIBMS). IEEE, pp 225–228

    Google Scholar 

  72. Dhanvijay MM, Patil SC (2019) Internet of things: a survey of enabling technologies in healthcare and its applications. Comput Netw 153:113–131

    Google Scholar 

  73. Samanta A, Li Y, Chen S (2018) QoS-aware heuristic scheduling with delay-constraint for WBSNs. In: 2018 IEEE International conference on communications (ICC). IEEE, pp 1–7

    Google Scholar 

  74. Abidi B, Jilbab A, Mohamed EH (2020) Wireless body area networks: a comprehensive survey. J Med Eng Technol 44(3):97–107

    Google Scholar 

  75. Kim S, Iravantchi Y, Gajos K (2019) SwellFit: developing a wearable sensor for monitoring peripheral edema. In: Proceedings of the 52nd Hawaii international conference on system sciences

    Google Scholar 

  76. Senthil Kumar K, Amutha R, Palanivelan M, Gururaj D, Richard Jebasingh S, Anitha Mary M, Anitha S, Savitha V, Priyanka R, Balachandran A et al (2018) Receive diversity based transmission data rate optimization for improved network lifetime and delay efficiency of wireless body area networks. Plos One 13(10):e0206027

    Google Scholar 

  77. Sodhro AH, Chen L, Sekhari A, Ouzrout Y, Wu W (2018) Energy efficiency comparison between data rate control and transmission power control algorithms for wireless body sensor networks. Int J Distrib Sens Netw 14(1):1550147717750030

    Google Scholar 

  78. Velez FJ, Miyandoab FD (2019) Wearable technologies and wireless body sensor networks for healthcare. Institution of Engineering and Technology

    Google Scholar 

  79. Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Eugene Stanley H (2000) Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

    Google Scholar 

  80. Hook-Podhorniak G, Acharya S (2019) Effectual emergency severity adaptation for improved triage care operations. In: 2019 IEEE International conference on bioinformatics and biomedicine (BIBM). IEEE, pp 2628–2633

    Google Scholar 

  81. Bu X, Lu L, Zhan Z, Qin Z, Yan Z (2020) A general outpatient triage system based on dynamic uncertain causality graph. IEEE Access 8:93249–93263

    Google Scholar 

  82. Khan TR, Hossein KM, Maruf KRI, Fukuda A, Ahmed A (2017) Measurement of illness and wellness score of non-communicable disease patients. In: TENCON 2017-2017 IEEE Region 10 conference. IEEE, pp 2253–2257

    Google Scholar 

  83. Aileni RM, Valderrama AC, Strungaru R (2017) Wearable electronics for elderly health monitoring and active living. In: Ambient assisted living and enhanced living environments. Elsevier, pp 247–269

    Google Scholar 

  84. Gravina R, Alinia P, Ghasemzadeh H, Fortino G (2017) Multi-sensor fusion in body sensor networks: state-of-the-art and research challenges. Inf Fusion 35:68–80

    Google Scholar 

  85. Alameen A, Gupta A (2020) Optimization driven deep learning approach for health monitoring and risk assessment in wireless body sensor networks. Int J Bus Data Commun Netw (IJBDCN) 16(1):70–93

    Google Scholar 

  86. Dou H (2019) Applications of machine learning in the field of medical care. In: 2019 34rd Youth academic annual conference of Chinese association of automation (YAC). IEEE, pp 176–179

    Google Scholar 

  87. Jashwanth Reddy D, Mounika B, Sindhu S, Pranayteja Reddy T, Sagar Reddy N, Jyothsna Sri G, Swaraja K, Meenakshi K, Kora P (2020) Predictive machine learning model for early detection and analysis of diabetes. Mater Today Proc

    Google Scholar 

  88. Ouyang F, Guo B, Ouyang L, Liu Z, Lin S, Meng W, Huang X, Chen H, Qiu-Gen H, Yang S (2019) Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules. Eur J Radiol 113:251–257

    Google Scholar 

  89. Katarya R, Srinivas P (2020) Predicting heart disease at early stages using machine learning: a survey. In: 2020 International conference on electronics and sustainable communication systems (ICESC). IEEE, pp 302–305

    Google Scholar 

  90. Hao S, Junye B, Liu H, Wang L, Liu T, Lin C, Luo X, Gao J, Zhao J, Li H et al (2020) Comparison of machine learning tools for the prediction of AMD based on genetic, age, and diabetes-related variables in the Chinese population. Regenerative Therapy 15:180–186

    Google Scholar 

  91. Kumar P, Garg S, Garg A (2020) Assessment of anxiety, depression and stress using machine learning models. Procedia Comput Sci 171:1989–1998

    Google Scholar 

  92. Gnana Sheela K, Varghese AR (2020) Machine learning based health monitoring system. Mater Today Proc 24:1788–1794

    Google Scholar 

  93. Li X, Zhao P, Wu M, Chen Z, Zhang L (2021) Deep learning for human activity recognition. Neurocomputing 444:214–216

    Google Scholar 

  94. Ma X (2018) Using classification and regression trees: a practical primer. IAP

    Google Scholar 

  95. Gocheva-Ilieva SG, Voynikova DS, Stoimenova MP, Ivanov AV, Iliev IP (2019) Regression trees modeling of time series for air pollution analysis and forecasting. Neural Comput Appl 31(12):9023–9039

    Google Scholar 

  96. Matondo SB, Owolawi PA (2019) FSO rain attenuation prediction using non-linear least square regression. In: 2019 International multidisciplinary information technology and engineering conference (IMITEC). IEEE, pp 1–5

    Google Scholar 

  97. Torgo L (2017) Regression trees

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Kadhum Idrees .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jaber, A.S., Idrees, A.K. (2022). Wireless Body Sensor Networks: Applications, Challenges, Patient Monitoring, Decision Making, and Machine Learning in Medical Applications. In: Boulouard, Z., Ouaissa, M., Ouaissa, M., El Himer, S. (eds) AI and IoT for Sustainable Development in Emerging Countries. Lecture Notes on Data Engineering and Communications Technologies, vol 105. Springer, Cham. https://doi.org/10.1007/978-3-030-90618-4_20

Download citation

Publish with us

Policies and ethics