MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications

Park, Hyunseo; Kim, Nakyoung; Lee, Gyeong Ho; Choi, Jun Kyun

doi:10.1016/j.future.2022.09.024

Cited by 39 publications

(11 citation statements)

References 62 publications

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“…HAR is a field in computer vision and machine learning that focuses on recognizing and classifying different human activities [18]. The recent advancements in this research domain demonstrate the outstanding performances of convolutional neural network (CNN)-based approaches [19]. The commercial application of HAR technology is visible in different sectors, including the healthcare sector, fitness tracking, smart homes, smart surveillance and security, and sports analysis [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

et al. 2023

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Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.

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Section: Literature Reviewmentioning

confidence: 99%

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

et al. 2023

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“…In Park et al (2023), the authors devised a DL-related HAR approach named MultiCNN-FilterLSTM that combined a multihead CNN with an LSTM using a residual connection where feature vectors are productively dealt with hierarchically. Consequently, a new method, filterwise LSTM (FilterLSTM), was presented that uses LSTM cells.…”

Section: Related Studiesmentioning

confidence: 99%

Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Alotaibi,

Alnfiai,

Al-Wesabi

et al. 2023

Journal of Disability Research

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Aging is related to a decrease in the ability to execute activities of day-to-day routine and decay in physical exercise, which affect mental and physical health. Elderly patients or people can depend on a human activity recognition (HAR) system, which monitors the activity interventions and patterns if any critical event or behavioral changes occur. A HAR system incorporated with the Internet of Things (IoT) environment might allow these people to live independently. While the number of groups of activities and sensor measurements is enormous, the HAR problem could not be resolved deterministically. Hence, machine learning (ML) algorithm was broadly applied for the advancement of the HAR system to find the patterns of human activity from the sensor data. Therefore, this study presents an Optimal Deep Recurrent Neural Networks for Human Activity Recognition (ODRNN-HAR) on Elderly and Disabled Persons technique in the IoT platform. The intension of the ODRNN-HAR approach lies in the recognition and classification of various kinds of human activities in the IoT environment. Primarily, the ODRNN-HAR technique enables IoT devices to collect human activity data and employs Z-score normalization as a preprocessing step. For effectual recognition of human activities, the ODRNN-HAR technique uses the DRNN model. At the final stage, the optimal hyperparameter adjustment of the DRNN model takes place using the mayfly optimization (MFO) algorithm. The result analysis of the ODRNN-HAR algorithm takes place on benchmark HAR dataset, and the outcomes are examined. The comprehensive simulation outcomes highlighted the improved recognition results of the ODRNN-HAR approach in terms of different measures.

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“…Various multi-head frameworks for HAR have been designed in recent research. A combination of multihead CNN and a novel filter-wise approach of LSTM in a residual way was proposed where the hierarchical order of feature vectors was processed efficiently for the application of action recognition in an IOT field [42]. A double-head architecture was arranged in [43] where a fully convolutional branch was used in alignment along the LSTM branch.…”

Section: Introductionmentioning

confidence: 99%

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

Verma,

Tyagi,

Aneja

2024

Eng. Res. Express

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In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual's body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data. 

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MultiCNN-FilterLSTM: Resource-efficient sensor-based human activity recognition in IoT applications

Cited by 39 publications

References 62 publications

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Optimal Deep Recurrent Neural Networks for IoT-enabled Human Activity Recognition in Elderly and Disabled Persons

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

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