Abstract
To study the application of wearable devices in the sixth generation (6G) Internet of things (IoT) communication environment, a human behaviour recognition method of wearable devices is designed by using artificial intelligence technology. First, the information in IoT is analyzed and processed by using data mining technology, and the classifier model is determined though experiments. Second, a human behaviour recognition model is designed by using artificial neural network (ANN), and the learning of neural network parameters is conducted by back propagation algorithm to improve the ability of behaviour recognition. Finally, the classifier and the human behaviour recognition model based on ANN are tested. The experimental results indicate that the classification accuracy of compound Bayes classification is the highest, so the compound Bayes classifier can be used as the data mining technology of wearable devices in IoT. When the number of neuron nodes in the hidden layer is 11, the recognition accuracy of human behaviour is more than 75%. In addition, compared with other algorithms, the overall recognition effect is better. Thus, the designed recognition model can be used for the recognition of human behaviour and it provides a reference for the study of the application of wearable devices in the environment of IoT.
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Bargh M (2019) Digital health software and sensors: internet of things-based healthcare services, wearable medical devices, and real-time data analytics. Am J Med Res 6(2):61–66
Brown J, Cug J, Kolencik J (2020) Internet of things-based smart healthcare systems: real-time patient-generated medical data from networked wearable devices. Am J Med Res 7(1):21–26
Byrne S (2019) Remote medical monitoring and cloud-based internet of things healthcare systems. Am J Med Res 6(2):19–24
Durkin K (2019) Artificial intelligence-driven smart healthcare services, wearable medical devices, and body sensor networks. Am J Med Res 6(2):37–42
Fincham S (2019) Internet of things-based medical applications, wearable sensor systems, and real-time health monitoring. Am J Med Res 6(2):49–54
Greenberg A (2020) Protecting virtual things: patentability of artificial intelligence technology for the internet of things. IDEA 60:328
Gu Z, Wei J (2020) Empirical study on initial trust of wearable devices based on product characteristics. J Comput Inf Syst 9:1–9
Guo K, Lu Y, Gao H et al (2018) Artificial intelligence-based semantic internet of things in a user-centric smart city. Sensors 18(5):1341
Hao M, Li H, Luo X et al (2019) Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans Industr Inf 16(10):6532–6542
Harrer S, Shah P, Antony B et al (2019) Artificial intelligence for clinical trial design. Trends Pharmacol Sci 40(8):577–591
Kang M, Park E, Cho BH et al (2018) Recent patient health monitoring platforms incorporating internet of things-enabled smart devices. Int Neurourol J 22(2):76
Karthick GS, Sridhar M, Pankajavalli PB (2020) Internet of things in animal healthcare (IoTAH): review of recent advancements in architecture, sensing technologies and real-time monitoring. SN Comput Sci 1(5):1–16
Kenrick N, Svabova L, Nica E (2019) Real-time health-related data, wearable medical sensor devices, and smart cyber-physical systems. Am J Med Res 6(2):25–30
Krittanawong C, Johnson KW, Hershman SG et al (2018) Big data, artificial intelligence, and cardiovascular precision medicine. Exp Rev Precis Med Drug Dev 3(5):305–317
Lee CY (2018) Smart wearable apparatus for elderly care. Impact 2018(2):32–34
Lewis S (2020) Wearable internet of things healthcare systems: smart biomedical sensors, wireless connected devices, and real-time patient monitoring. Am J Med Res 7(1):55–60
Metcalf D, Milliard STJ, Gomez M et al (2016) Wearables and the internet of things for health: Wearable, interconnected devices promise more efficient and comprehensive health care. IEEE Pulse 7(5):35–39
Nam KH, Kim DH, Choi BK et al (2019) Internet of things, digital biomarker, and artificial intelligence in spine: current and future perspectives. Neurospine 16(4):705
Qi J, Yang P, Min G et al (2017) Advanced internet of things for personalised healthcare systems: a survey. Pervas Mob Comput 41:132–149
Qi J, Yang P, Newcombe L et al (2020) An overview of data fusion techniques for Internet of Things enabled physical activity recognition and measure. Inf Fusion 55:269–280
Ravi D, Wong C, Lo B et al (2016) A deep learning approach to on-node sensor data analytics for mobile or wearable devices. IEEE J Biomed Health Inform 21(1):56–64
Shahzad M, Singh MP (2017) Continuous authentication and authorization for the internet of things. IEEE Int Comput 21(2):86–90
Sheth A, Jaimini U, Yip HY (2018) How will the internet of things enable augmented personalized health. IEEE Intell Syst 33(1):89–97
Sheth A, Yip HY, Shekarpour S (2019) Extending patient-chatbot experience with internet-of-things and background knowledge: case studies with healthcare applications. IEEE Intell Syst 34(4):24–30
Sodhro AH, Pirbhulal S, de Albuquerque VHC (2019) Artificial intelligence-driven mechanism for edge computing-based industrial applications. IEEE Trans Ind Inf 15(7):4235–4243
Sztyler T, Stuckenschmidt H, Petrich W (2017) Position-aware activity recognition with wearable devices. Pervas Mobile Comput 38:281–295
Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178
Tuan MND, Thanh NN, Le Tuan L (2019) Applying a mindfulness-based reliability strategy to the Internet of Things in healthcare–a business model in the Vietnamese market. Technol Forecast Soc Chang 140:54–68
Vashistha R, Dangi AK, Kumar A et al (2018) Futuristic biosensors for cardiac health care: an artificial intelligence approach. 3 Biotech 8(8):358
Zhang K, Lan S, Zhang G (2021) On the effect of training convolution neural network for millimeter-wave radar-based hand gesture recognition. Sensors 21(1):259
Zhang Y, Ma X, Zhang J et al (2019) Edge intelligence in the cognitive internet of things: improving sensitivity and interactivity. IEEE Netw 33(3):58–64
Zheng X, Sun S, Mukkamala RR et al (2019) Accelerating health data sharing: a solution based on the internet of things and distributed ledger technologies. J Med Internet Res 21(6):13583
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Xu, L. Application of wearable devices in 6G internet of things communication environment using artificial intelligence. Int J Syst Assur Eng Manag 12, 741–747 (2021). https://doi.org/10.1007/s13198-021-01070-6
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DOI: https://doi.org/10.1007/s13198-021-01070-6