Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks

Zhan, Yi; Nishimura, Takahiro; Kuroda, Tadahiro

doi:10.1541/ieejeiss.130.565

Cited by 8 publications

(2 citation statements)

References 16 publications

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“…It assumes that letters form words, which then form sentences. The model takes syntactic constraints into account when describing the relationships (rules) between subpatterns and primitives that describe patterns [ 9 ]. This study examines things using a variety of well-known methods.…”

Section: Introductionmentioning

confidence: 99%

Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities

Kaleeswaran¹,

Kavitha

2022

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Recognizing patterns associated with particular events enables the detection of specific critical changes in the events. Due to the resource constraints inherent in WSNs, pattern recognition is highly dependent on the complexity of the computation, the number of iterations, and the requirements for node training. Iterative learning is frequently used in computer-based computer vision. As a result, these methods are in conflict with the perfectly alright architecture of the WSN. The proposed technique, Optimal Cellular Microscopic Pattern Recognizer (OCMPR), enables the detection of macroscale events in WSN. Using the distributed system computational resources of WSNs, the approach reduces calculations for conserving energy and improves recognition. The method generates promising results by combining a well-known optimization technique (the genetic algorithm) with CMPR. This approach addresses the resource-constrained WSN’s real-time mission-critical application needs. Global and quick recognition is achieved by dispersing processing over a network’s nodes, allowing for loosely connected communication. The results demonstrate the suggested scheme’s versatility.

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Section: Introductionmentioning

confidence: 99%

Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities

Kaleeswaran¹,

Kavitha

2022

Scanning

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“…MFCCs and its variants are widely utilized in the area of signal processing and HAR [21], [22]. To capture MFCCs feature, the signal is split into frames of fixed length.…”

Section: Feature Extractionmentioning

confidence: 99%

An Efficient Human Activity Recognition Using Hybrid Features and Transformer Model

Saidani,

Alsafyani,

Alroobaea

et al. 2023

IEEE Access

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Human activity recognition is a challenging and active research topic in computer science due to its applications in video surveillance, health monitoring, rehabilitation, human-robot interaction, robotics, gesture and posture analysis, and sports. In the past, various studies have utilized manual features to identify human activities and obtained good accuracy. Nonetheless, the performance of such features degraded in complex situations. Therefore, recent research used deep learning (DL) techniques to capture the local features automatically from given activity instances. Though automatic feature extraction overcomes the problems of manual features, there is still a need to enhance the efficiency and accuracy of existing techniques. The motivation behind this research is to improve the efficiency and accuracy of HAR systems. This research proposed a HAR system, which applies data enhancement techniques before capturing robust and discriminative features set from each activity instance. The captured feature set is given to the transformer model for activities recognition using the PAMAP2, UCI HAR, and WISDM datasets. The achieved results revealed that the proposed HAR model outperformed the baseline methods. Specifically, the proposed HAR achieved 98.2% accuracy for PAMAP2 with all instances in 12 activities, 98.6% accuracy for UCI HAR with all instances in 6 activities, 97.3% for WISDM with all instances in 6 activities. The advantage of the proposed hybrid features is the capability to capture both low-level and high-level information from the sensor data, potentially enhancing the discriminative power of the system. In addition, this study employed a transformer a model due to its ability to capture long-range dependencies, which are beneficial in recognizing complex human activities patterns.

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Enabled IoT Applications for Covid-19

Gasmi

2021

EAI/Springer Innovations in Communication and Computing

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Human Activity Recognition from Environmental Background Sounds for Wireless Sensor Networks

Cited by 8 publications

References 16 publications

Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities

Optimal Cellular Microscopic Pattern Recognizer- (OCMPR-) Based Wireless Detection Network for Efficiently Leveraging the Parallel Distributed Processing Capabilities

An Efficient Human Activity Recognition Using Hybrid Features and Transformer Model

Enabled IoT Applications for Covid-19

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