Machine Learning (ML) based Human Activity Recognition Model using Smart Sensors in IoT Environment

Navita,; Mittal, Pooja

doi:10.1109/confluence52989.2022.9734152

Cited by 11 publications

(14 citation statements)

References 12 publications

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“…The data were acquired from BioBeats (http://biobeats.com) that generates IoT wearable devices for heart rate signal monitoring and offering feedback accordingly. In this section we mainly focus on the state of methods and results of the proposed SPR-SVIAL method with the existing HAR_WCNN [1] and IoT-based human activity monitoring model [2] for Smart Healthcare Monitoring. Assessment metrics employed for analyzing the performance of both proposed and existing methods are listed as follows:…”

Section: Experimental Evaluationsmentioning

confidence: 99%

“…Fig. 4 demonstrates the smart healthcare monitoring time by means of the three different methods, SPR-SVIAL, HAR_WCNN [1] and IoT-based human activity monitoring model [2]. As shown in the above figure, 5000 instances were acquired from 22 different participants contains both male and female.…”

Section: Performance Analysis Of Smart Health Monitoring Timementioning

confidence: 99%

“…However, with '500' instances acquired from unprocessed acceleration data by means of Axis 1, Axis 2 and Axis 3, the time used during analyzing single patients healthcare with respect to Heart Rate signals was found to be '0.99 ms' using SPR-SVIAL, '1.1 ms' using [1] and '0.83 ms' using [2], the overall analysis time were observed to be '495 ms', '550 ms', '600 ms', respectively. Based on this numerical assessment the time analysis for heart rate signals using SPR-SVIAL was comparatively lesser than [1] and [2]. Because Statistical Partial Regression Feature Extraction model application is applied in the analysis.…”

Section: Performance Analysis Of Smart Health Monitoring Timementioning

confidence: 99%

“…To address this problem, in this work, Partial Regression Feature Extraction model is introduced that with the assist of partial regression function extracts the dimensionality reduced features involved inside smart healthcare monitoring and thus minimizing the smart healthcare monitoring time in a significant approach. An IoT-based human activity monitoring model was proposed in [2] to continuously verify the elder people behavior by means of smart sensor-based technologies. In this model, different machine learning algorithms were employed to detect any risk in human activity behavior.…”

Section: Introductionmentioning

confidence: 99%

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Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Akilandeswari,

Sivakumar,

Alkahtani

et al. 2024

CMC

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In this present time, Human Activity Recognition (HAR) has been of considerable aid in the case of health monitoring and recovery. The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance. Although many research works conducted on Smart Healthcare Monitoring, there remain a certain number of pitfalls such as time, overhead, and falsification involved during analysis. Therefore, this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning (SPR-SVIAL) for Smart Healthcare Monitoring. At first, the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process. Here, the input dataset the continuous beat-to-beat heart data, triaxial accelerometer data, and psychological characteristics were acquired from IoT wearable devices. To attain highly accurate Smart Healthcare Monitoring with less time, Partial Least Square helps extract the dimensionality-reduced features. After that, with these resulting features, SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead. Experimental evaluation is carried out for factors such as time, overhead, and false positive rate accuracy concerning several instances. The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

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Section: Experimental Evaluationsmentioning

confidence: 99%

Section: Performance Analysis Of Smart Health Monitoring Timementioning

confidence: 99%

Section: Performance Analysis Of Smart Health Monitoring Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Akilandeswari,

Sivakumar,

Alkahtani

et al. 2024

CMC

View full text Add to dashboard Cite

show abstract

“…We also showed the method of classification adopted in the treatment. [25] SVM-Gaussian kerne 96.50% De Leonardis et al [26] K-nearest neighbor -feedforward neural network -SVM -decision tree -Naïve Bayes 97.00% Nurhanim et al [27] SVM polynomial kernel -one versus all 98.57% Agarwal and Alam [28] SVMs-k-nearest neighbor-linear discriminant analysis 98.00% Minarno et al [29] SVM+LR 98.00% Jindal et al [30] SVM, KNN, and LR 92.78% Patel and Shah [31] Long short-term, LR 92.00% Navita and Mittal [32] SVM 98.03% Figure 6. Model accuracy scores…”

Section: Machine Learning Model Evaluationmentioning

confidence: 99%

Real-time human activity recognition from smart phone using linear support vector machines

Maaloul¹,

Lejdel²,

Abdelhamid³

2023

TELKOMNIKA

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The recognition of human activity (HAR) the use of cell devices embedded in its exten sively disbursed sensors affords guidance, instructions, and take care of citizens of smart cities. Consequently, it became essential to analyze human every day sports. To examine statistical models of human conduct, synthetic intelligence strategies such as machine studying can be used. Many studies have not studied type overall performance in real-time due to statistics series. To remedy this trouble, this paper proposes a structure primarily based on open supply technology and platforms consisting of Apache Kafka, for messages to flow over the internet, method them and provide shape for existing facts in real-time and formulates the trouble of identifying human pastime by using a smartphone tool as a type hassle using statistics collection by telephone sensors. The proposed version is skilled by some machine learning algorithms. The algorithm that has proven superior and quality results helps a linear vector machines.

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Human Action Recognition for Sanitation Data Using Voting Classifier

Pareek,

Nigam,

Singh

2024

Lecture Notes in Networks and Systems

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Machine Learning (ML) based Human Activity Recognition Model using Smart Sensors in IoT Environment

Cited by 11 publications

References 12 publications

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

Real-time human activity recognition from smart phone using linear support vector machines

Human Action Recognition for Sanitation Data Using Voting Classifier

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