Privacy preserving human activity recognition framework using an optimized prediction algorithm

Kumar, Kambala Vijaya; Harikiran, J.

doi:10.11591/ijai.v11.i1.pp254-264

Cited by 9 publications

(4 citation statements)

References 52 publications

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“…The developed provable data possession (PDP) scheme failed to support dynamic auditing for providing data privacy protection [9]- [11]. The another developed public auditing protocol, which utilizes a binary binomial tree (BBT)-like data structure along with the boneh-lynn-shacham signature-based homomorphic verifiable authenticator (BLS-HVA) modelfaced an unexpected problem after introducing a third-party auditor [12].…”

Section: Introductionmentioning

confidence: 99%

Effective privacy preserving in cloud computing using position aware Merkle tree model

Gangadharaiah,

Shrinivasacharya

2024

Bulletin EEI

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In this research manuscript, a new protocol is proposed for predicting the available space in the cloud and verifying the security of stored data. The protocol is utilized for learning the available data, and based on this learning, the available storage space is identified, after which the cloud service providers allow for data storage. The Integrity verification separates the private and the public data, which avoids privacy issues. The integration of the private data is done with the help of cloud service providers with respect to the third-party auditing (TPA). Earlier, public key cryptography and bilinear map technologies have been combined by the researchers, but the computation time and costs were high. To secure the integrity of the data storage, the client should execute several computations. Therefore, this research suggests a reliable and effective method called position-aware Merkle tree (PMT), which is implemented for ensuring data integrity. The proposed system uses a PMT that enables the TPA to perform multiple auditing tasks with high efficiency, less computational cost and computation time. Simulation results clearly shows that the developed PMT method consumed 0.00459 milliseconds of computation time, which is limited when compared to the existing models.

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Section: Introductionmentioning

confidence: 99%

Effective privacy preserving in cloud computing using position aware Merkle tree model

Gangadharaiah,

Shrinivasacharya

2024

Bulletin EEI

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“…We proposed using AlexNet [3], ResNet18 [4] and SqueezeNet1_0 [5] deep learning models to classify and evaluate the accuracy of dances to its class. Several works have been done to classify human movements such as proposed by Yildirim and Çinar [6], Kumar and Harikiran [7] and Zamri et al [8] that uses deep learning models. However, those works are similar in method whereby the authors utilized singular images of ISSN: 2252-8938  a human performing an action to train their deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

Zakry

Hipiny

Ujir

2023

IJ-AI

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Dancing is an art form of creative expression that is based on movement. Dancing comprises varying styles, pacing and composition to convey an artist’s expression. Thus, the classification of any dance to a certain genre or type depends on how accurate or similar it is to what is generally understood to be the specific movements of that dance type. This presents a problem for new dancers to assess if the dance movements that they have just learned is accurate or not to what the original dance type is. This paper proposed that deep learning models can classify dance videos of amateur dancers according to the similar movements of actions of several dance classes. For this study, AlexNet, ResNet and SqueezeNet models was used to perform training on multiple frames of actions of several dance videos for label prediction and the classification accuracy of the models during each training epoch is compared. This study observed that the average classification accuracy of the deep learning models is 94.9669% and is comparable to other approaches used for dance classifications.

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“…The pioneering activity recognition approach has been based on the analysis of visual data, including both images and videos [7]. Considering the dynamism with which a person performs an activity during his/her daily living, multiple challenges can be found in the use of vision-based solutions, including viewpoint variations, occlusions, cluttered backgrounds, different illumination conditions, and privacy concerns [8,9]. As a result, alternative solutions have been studied in recent years, such as those based on the use of sensors, which can be positioned in the environment surrounding the user or directly worn by the same [10].…”

Section: Introductionmentioning

confidence: 99%

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

D'Arco

Wang

Zheng

2023

Neural Comput & Applic

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Health monitoring, rehabilitation, and fitness are just a few domains where human activity recognition can be applied. In this study, a deep learning approach has been proposed to recognise ambulation and fitness activities from data collected by five participants using smart insoles. Smart insoles, consisting of pressure and inertial sensors, allowed for seamless data collection while minimising user discomfort, laying the baseline for the development of a monitoring and/or rehabilitation system for everyday life. The key objective has been to enhance the deep learning model performance through several techniques, including data segmentation with overlapping technique (2 s with 50% overlap), signal down-sampling by averaging contiguous samples, and a cost-sensitive re-weighting strategy for the loss function for handling the imbalanced dataset. The proposed solution achieved an Accuracy and F1-Score of 98.56% and 98.57%, respectively. The Sitting activities obtained the highest degree of recognition, closely followed by the Spinning Bike class, but fitness activities were recognised at a higher rate than ambulation activities. A comparative analysis was carried out both to determine the impact that pre-processing had on the proposed core architecture and to compare the proposed solution with existing state-of-the-art solutions. The results, in addition to demonstrating how deep learning solutions outperformed those of shallow machine learning, showed that in our solution the use of data pre-processing increased performance by about 2%, optimising the handling of the imbalanced dataset and allowing a relatively simple network to outperform more complex networks, reducing the computational impact required for such applications.

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Privacy preserving human activity recognition framework using an optimized prediction algorithm

Cited by 9 publications

References 52 publications

Effective privacy preserving in cloud computing using position aware Merkle tree model

Effective privacy preserving in cloud computing using position aware Merkle tree model

Classification of dances using AlexNet, ResNet18 and SqueezeNet1_0

DeepHAR: a deep feed-forward neural network algorithm for smart insole-based human activity recognition

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