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IoT inspired smart environment for personal healthcare in gym

  • S.I. : Neural Computing for IOT based Intelligent Healthcare Systems
  • Published:
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Abstract

The Internet of Things (IoT) has the ability to collect health-related data from surroundings. As a result, the Cloud Centric IoT (CCIoT) Technology is used in this paper to measure a trainee’s health-related traits during fitness time in a gym. The proposed system can forecast a trainee’s probabilistic sensitivity to health status during workouts. Back-propagation based Artificial Neural Network (ANN) methodology is used as a prediction model for this purpose, and it is divided into 3 phases: Observation, Learning, and Prediction. In addition, the trainee’s health status is depicted in real-time using a colour scheme strategy that depicts the probabilistic vulnerability. The presented framework was tested by a 6 day trial in which five individuals were supervised at various gymnasiums. For assessing the general efficacy of the proposed framework, the outcomes are compared to various state-of-the-art approaches in terms of prediction efficiency, temporal prediction, and stability.

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References

  1. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (iot): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  2. Bhatia M, Sood SK (2016) Temporal informative analysis in smart-icu monitoring: m-healthcare perspective. J Med Syst 40(8):1–15

    Article  Google Scholar 

  3. Al-fuqaha Ala I, Guizani M, Mohammadi M, Aledhari M, Ayyash M (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2376

    Article  Google Scholar 

  4. Bhatia M, Sood SK (2017) A comprehensive health assessment framework to facilitate iot-assisted smart workouts: a predictive healthcare perspective. Comput Ind 92:50–66

    Article  Google Scholar 

  5. Bhatia M, Sood SK, Kaur S (2020) Quantumized approach of load scheduling in fog computing environment for IoT applications. Computing, pp 1–19

  6. Jara AJ, Zamora-Izquierdo MA, Skarmeta AF (2013) Interconnection framework for mhealth and remote monitoring based on the internet of things. IEEE J Sel Areas Commun 31(9):47–65

    Article  Google Scholar 

  7. Bhatia M, Sood SK (2018) An intelligent framework for workouts in gymnasium: m-health perspective. Comput Electr Eng 65:292–309

    Article  Google Scholar 

  8. Bhatia M, Ahanger TA, Tariq U, Ibrahim A (2021) Cognitive intelligence in fog computing-inspired veterinary healthcare. Comput Electr Eng 91:107061

    Article  Google Scholar 

  9. Bhatia M, Sood SK (2018) Internet of things based activity surveillance of defence personnel. J Ambient Intell Humaniz Comput 9(6):2061–2076

    Article  Google Scholar 

  10. Bhatia M, Sood SK, Kaur S (2019) Quantum-based predictive fog scheduler for IoT applications. Comput Ind 111:51–67

    Article  Google Scholar 

  11. Bhatia M, Manocha A (2020) Cognitive framework of food quality assessment in iot-inspired smart restaurants. IEEE Internet of Things J

  12. Bhatia M (2020) Fog computing-inspired smart home framework for predictive veterinary healthcare. Microprocess Microsyst 78:103227

    Article  Google Scholar 

  13. Pate RR, Davis MG, Robinson TN, Stone EJ, McKenzie TL (2006) A leadership role for schools: a scientific statement from the American heart association council on nutrition, physical activity, and metabolism (physical activity committee) in collaboration with the councils on cardiovascular disease in the young and cardiovascular nursing. Circulation 114(11):1214–24

    Article  Google Scholar 

  14. Bhatia M (2020) Game theory based framework of smart food quality assessment. Trans Emerg Telecommun Technol 31(12):e3296

    Google Scholar 

  15. Bhatia M, Kaur S, Sood SK, Behal V (2020) Internet of things-inspired healthcare system for urine-based diabetes prediction. Artif Intell Med 107:101913

    Article  Google Scholar 

  16. Bhatia M, Kaur S, Sood SK (2020) IoT-inspired smart toilet system for home-based urine infection prediction. ACM Trans Comput Healthc 1(3):1–25

    Article  Google Scholar 

  17. Bhatia M, Sood S, Sood V (2020) A novel quantum-inspired solution for high-performance energy-efficient data acquisition from IoT networks. J Ambient Intell Humaniz Comput pp. 1–20

  18. Kemmler W, von Stengel S (2013) Exercise frequency, health risk factors, and diseases of the elderly. Arch Phys Med Rehabilit 94(11):2046–2053

    Article  Google Scholar 

  19. Yang G, Xie L, Mäntysalo M, Zhou X, Pang Z, Da Xu L, Kao-Walter S, Chen Q, Zheng LR (2014) A health-iot platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans Ind Inform 10(4):2180–2191

    Article  Google Scholar 

  20. Fanucci L, Saponara S, Bacchillone T, Donati M, Barba P, Sánchez-Tato I, Carmona C (2012) Sensing devices and sensor signal processing for remote monitoring of vital signs in chf patients. IEEE Trans Instrum Meas 62(3):553–569

    Article  Google Scholar 

  21. Kim SY, Jeong W, Park EC, Park S, Jang SI (2021) Association between the severity of new-onset depression and unmet healthcare needs of south korean adults. Plos one 16(8):e0256222

    Article  Google Scholar 

  22. Hossain MS, Muhammad G (2016) Cloud-assisted industrial internet of things (iot)-enabled framework for health monitoring. Comput Netw 101:192–202

    Article  Google Scholar 

  23. Salem O, Liu Y, Mehaoua A, Boutaba R (2014) Online anomaly detection in wireless body area networks for reliable healthcare monitoring. IEEE J Biomed Health Inform 18(5):1541–1551

    Article  Google Scholar 

  24. Brugarolas R, Latif T, Dieffenderfer J, Walker K, Yuschak S, Sherman BL, Roberts DL, Bozkurt A (2015) Wearable heart rate sensor systems for wireless canine health monitoring. IEEE Sens J 16(10):3454–3464

    Article  Google Scholar 

  25. Chen J, Chen H, Wu Z, Hu D, Pan JZ (2017) Forecasting smog-related health hazard based on social media and physical sensor. Inform Syst 64:281–291

    Article  Google Scholar 

  26. Luo RC, Chang CC, Lai CC (2011) Multisensor fusion and integration: theories, applications, and its perspectives. IEEE Sens J 11(12):3122–3138

    Article  Google Scholar 

  27. Gjoreski H, Kaluža B, Gams M, Milić R, Luštrek M (2015) Context-based ensemble method for human energy expenditure estimation. Appl Soft Comput 37:960–970

    Article  Google Scholar 

  28. Garcia-Valverde T, Muñoz A, Arcas F, Bueno-Crespo A, Caballero A (2014) Heart health risk assessment system: a nonintrusive proposal using ontologies and expert rules. BioMed Res Int 2014

  29. Kupusinac A, Stokic E, Kovacevic I (2016) Hybrid EANN-EA system for the primary estimation of cardiometabolic risk. J Med Syst 40(6):138

    Article  Google Scholar 

  30. Vickers NJ (2017) Animal communication: when i’m calling you, will you answer too? Curr Biol 27(14):R713–R715

    Article  Google Scholar 

  31. Ellison NB, Hancock JT (2013) Profile as promise: honest and deceptive signals in online dating. IEEE Secur Priv 11(5):84–88

    Article  Google Scholar 

  32. Atallah L, Lo B, King R, Yang Guang-zhong (2011) Sensor positioning for activity recognition using wearable accelerometers. IEEE Trans Biomed Circuits Syst 5:320–329

    Article  Google Scholar 

  33. Patel S, Mancinelli C, Healey J, Moy M, Bonato P, Using wearable sensors to monitor physical activities of patients with copd: A comparison of classifier performance. In: 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, vol 2009, pp 234–239. (IEEE)

  34. Yang MS, Tsai HS (2008) A gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognit Lett 29(12):1713–1725

    Article  Google Scholar 

  35. Allen FR, Ambikairajah E, Lovell NH, Celler BG (2006) Classification of a known sequence of motions and postures from accelerometry data using adapted gaussian mixture models. Physiol Meas 27(10):935

    Article  Google Scholar 

  36. Ultsch A (2003) U*-matrix: a tool to visualize clusters in high dimensional data

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  1. College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia

    Tariq Ahamed Ahanger

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  1. Tariq Ahamed Ahanger
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Correspondence to Tariq Ahamed Ahanger.

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Ahanger, T.A. IoT inspired smart environment for personal healthcare in gym. Neural Comput & Applic 35, 23007–23023 (2023). https://doi.org/10.1007/s00521-022-07488-8

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  • DOI: https://doi.org/10.1007/s00521-022-07488-8

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