Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

Thapa, Keshav; Seo, Yousung; Yang, Shunkun; Kim, Kyong

doi:10.3390/s23020683

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…• Model 1: Li et al (2023) proposed a sensor data contribution significance analysis method based on the sensor status frequency-inverse type frequency for HAR. • Model 2: Thapa et al (2023) proposed an approach that decouples the VAE using adversarial learning to ensure robust classifier operation without newly labeled training data under changes to the individual activity and the on-body sensor position. • Model 3: Wu et al (2023) proposed a new HAR system based on a pedal-wearable device and a novel differential spatiotemporal LSTM (DST-LSTM) method.…”

Section: Dataset and Baseline Modelmentioning

confidence: 99%

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Zhang,

Tong,

Lin

2024

Journal of Organizational and End User Computing

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With the exacerbation of the rural aging population trend, home-based health monitoring for the rural elderly has become a societal focal point, demanding an effective technological means to elevate the level of rural elderly health management. However, traditional algorithms for monitoring rural elderly behavior face myriad challenges, such as effectively capturing temporal and spatial features. Consequently, addressing the need to enhance the accuracy and robustness of rural elderly behavior recognition has become an urgent problem to solve. This study responds to this challenge by comprehensively employing deep learning and temporal modeling techniques, designing, and validating a short-term and long-term dual-layer home-based health monitoring system for the rural elderly.In the short-term layer, the model utilizes smartphones to collect health information from the rural elderly in various ways and performs real-time anomaly behavior detection.

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Section: Dataset and Baseline Modelmentioning

confidence: 99%

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Zhang,

Tong,

Lin

2024

Journal of Organizational and End User Computing

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“…Efficient action recognition plays a vital role in enhancing efficiency, security, and decision-making across multiple domains, including image processing [7], sign language recognition [8], artificial intelligence [9], and human-computer interfaces [10], enabling intelligent systems to make informed decisions and respond appropriately. Yet, in areas such as inhome nursing, elder care, and anomaly detection [11], [12], the intricate dynamics of human movements present significant challenges.…”

Section: Introductionmentioning

confidence: 99%

HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods

Karim,

Khalid,

Aleryani

et al. 2024

IEEE Access

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Human Action Recognition (HAR) is a vital area of computer vision with diverse applications in security, healthcare, and human-computer interaction. Addressing the challenges of HAR, particularly in dynamic and complex environments, is essential to advancing this field. The Human Actions in Diverse Environments (HADE) framework introduced in this paper represents a significant advancement in improving the capabilities of Convolutional Neural Networks (CNNs) for effective HAR. The strength of the HADE framework is its carefully curated dataset, primarily derived from smartphone camera footage. This dataset encompasses a wide range of human actions captured in various settings, providing a robust foundation for training our novel HAR models HADE I and HADE II. These models have been specifically designed and optimized for parallel processing on GPUs, showing significant improvements in the efficiency of both training and inference processes. Through a comprehensive evaluation, the HADE framework demonstrated a remarkable improvement in HAR accuracy, achieving 83.57% on our custom dataset. This marks a considerable enhancement over the existing methodologies and underscores the efficacy of the HADE approach in accurately interpreting complex human actions. Particularly noteworthy is the framework's potential applicability in healthcare, in the domain of neurological patient care, where it can aid in early detection and facilitate personalized treatment plans. Future research will focus on expanding the range of actions covered by HAR and exploring avenues for real-time processing. The introduction of the HADE framework not only makes a substantial contribution to the field of computer vision but also paves the way for its practical application across various sectors.

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“…HAR has become a crucial tool for monitoring a person's dynamism, and it is accomplished by utilizing ML techniques (Brishtel et al, 2023). HAR is a technique of automatically analyzing and detecting human activities related to data needed from different wearable devices and smartphone sensors like location, accelerometer sensors, time, various other environmental sensors, and gyroscope sensors (Thapa et al, 2023). Combined with other technologies like IoT, it is utilized in diverse application areas like industry, healthcare, and sports (Park et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Internet of Things-driven Human Activity Recognition of Elderly and Disabled People Using Arithmetic Optimization Algorithm with LSTM Autoencoder

Alotaibi,

Alnfiai,

Al-Wesabi

et al. 2023

Journal of Disability Research

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In recent times, mobile communications and Internet of Things (IoT) techniques have been technologically advanced to gather environmental and human data for many applications and intelligent services. Remote monitoring of disabled and older people living in smart homes is very difficult. Human activity recognition (HAR) is an active research area for classifying human movement and application in many regions like rehabilitation, healthcare systems, medical diagnosis, surveillance from smart homes, and elderly care. HAR data are gathered in wearable devices that contain many kinds of sensors or with the mobile sensor aid. Lately, deep learning (DL) algorithm has shown remarkable performance in classifying human activity on HAR information. This paper presents a new Arithmetic Optimization Algorithm with LSTM Autoencoder (AOA-LSTMAE) for HAR technique in the IoT environment. In the presented AOA-LSTMAE technique, the major intention is to recognize several types of human activities in the IoT environment. To accomplish this, the AOA-LSTMAE technique mainly derives the P-ResNet model for feature extraction. In addition, the AOA-LSTMAE technique utilizes the LSTMAE classification model for the recognition of different activities. For improving the recognition efficacy of the LSTMAE model, AOA is used as a hyperparameter optimization system. The simulation validation of the AOA-LSTMAE technique is tested on benchmark activity recognition data. The simulation results of the AOA-LSTMAE technique and compared methods stated the improvement of the proposed model with an accuracy of 99.12% over other recent algorithms.

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Semi-Supervised Adversarial Auto-Encoder to Expedite Human Activity Recognition

Cited by 5 publications

References 48 publications

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

Home Activity Recognition for Rural Elderly Based on Deep Learning and Smartphone Sensors

HADE: Exploiting Human Action Recognition Through Fine-Tuned Deep Learning Methods

Internet of Things-driven Human Activity Recognition of Elderly and Disabled People Using Arithmetic Optimization Algorithm with LSTM Autoencoder

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