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IoT-inspired machine learning-assisted sedentary behavior analysis in smart healthcare industry

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Abstract

Sedentary behavior is very common in today’s lifestyle that causes several metabolic and cardiovascular diseases. The high scale of physical inactiveness with inadequate activities such as drinking alcohol, smoking and negligence of healthy diet causes extreme health issues. Therefore, it becomes essential to recognize health, behavioral, and environmental irregularities to determine the scale of health severity for the individuals having sedentary behavior. Conspicuously, in the proposed study, an e-healthcare framework has been proposed by utilizing the advantages of Internet of Things (IoT) and fog technology to determine the irregularities related to health, behavioral, physical posture, and environmental conditions of the individual. In the initial stage of data processing, the weighted K-mean clustering technique is used to identify the irregularities at the fog layer that helps to maintain the sensitivity of the healthcare domain. Furthermore, Weighted K-mean Clustering technique-Artificial Neural Network assisted (WKMC-ANN) hybrid methodology is proposed on the cloud layer for the early prediction of health severity. Adding a multi-stage decision-making mechanism also helps to optimize the assistive resource distribution. To determine the performance of irregularity determination, a total of 15 individuals by the age of 32–45 years are continuously monitored by deploying the framework for 30 days in an indoor environment. The experimental results of the framework demonstrate the efficiency of distinguishing the health inconsistencies by achieving 94.51% of accuracy, 93.75% of sensitivity, 91.32% of specificity, 91.58% of precision, and 92.78% of f-measure.

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Abbreviations

AC:

Abnormal cluster

ADM:

Alert-based decision making

CC:

Cloud computing

CDC:

Center for Disease Control

DBSCAN:

Density-based spatial clustering of applications with noise

DLS:

Damped least-squares

ESP:

Early severity prediction

FC:

Fog computing

FCM:

Fuzzy C-means

GNM:

Gauss–Newton method

ICT:

Information and communication technology

IoT:

Internet of Things

logsig:

Logarithmic sigmoid

MAE:

Mean absolute error

MSE:

Mean square error

NC:

Normal cluster

RTA:

Real-time analysis

SD:

Standard deviation

SGD:

Steepest gradient descent

SoS:

Scale of severity

SSL:

Secure socket layer

SSR:

Severity scale recognition

TCP:

Transmission control protocol

WKMC-ANN:

Weighted K-mean clustering technique-artificial neural network

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Authors and Affiliations

  1. Lovely Professional University, Jalandhar, Punjab, 144411, India

    Ankush Manocha, Gulshan Kumar, Munish Bhatia & Amit Sharma

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  1. Ankush Manocha
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  2. Gulshan Kumar
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  3. Munish Bhatia
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  4. Amit Sharma
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Correspondence to Munish Bhatia.

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Manocha, A., Kumar, G., Bhatia, M. et al. IoT-inspired machine learning-assisted sedentary behavior analysis in smart healthcare industry. J Ambient Intell Human Comput 14, 5179–5192 (2023). https://doi.org/10.1007/s12652-021-03371-x

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