HActivityNet: A Deep Convolutional Neural Network for Human Activity Recognition

Human Activity Recognition (HAR), a vast area of a computer vision research, has gained standings in recent years due to its applications in various fields. As human activity has diversification in action, interaction, and it embraces a large amount of data and powerful computational resources, it is very difficult to recognize human activities from an image. In order to solve the computational cost and vanishing gradient problem, in this work, we have proposed a revised simple convolutional neural network (CNN) model named Human Activity Recognition Network (HActivityNet) that is automatically extract and learn features and recognize activities in a rapid, precise and consistent manner. To solve the problem of imbalanced positive and negative data, we have created two datasets, one is HARDataset1 dataset which is created by extracted image frames from KTH dataset, and another one is HARDataset2 dataset prepared from activity video frames performed by us. The comprehensive experiment shows that our model performs better with respect to the present state of the art models. The proposed model attains an accuracy of 99.5% on HARDatase1 and almost 100% on HARDataset2 dataset. The proposed model also performed well on real data.

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RF-Based Human Activity Recognition Using Signal Adapted Convolutional Neural Network

IEEE Transactions on Mobile Computing ◽

10.1109/tmc.2021.3073969 ◽

2021 ◽

pp. 1-1

Author(s):

Zhe Chen ◽

Chao Cai ◽

Tianyue Zheng ◽

Jun Luo ◽

Jie Xiong ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Human Activity Recognition

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Wi-Sense: a passive human activity recognition system using Wi-Fi and convolutional neural network and its integration in health information systems

annals of telecommunications - annales des télécommunications ◽

10.1007/s12243-021-00865-9 ◽

2021 ◽

Author(s):

Muhammad Muaaz ◽

Ali Chelli ◽

Martin Wulf Gerdes ◽

Matthias Pätzold

Keyword(s):

Neural Network ◽

Information Systems ◽

Convolutional Neural Network ◽

Health Information ◽

Activity Recognition ◽

Human Activity ◽

Health Information Systems ◽

Recognition System ◽

Human Activity Recognition ◽

Ratio Method

AbstractA human activity recognition (HAR) system acts as the backbone of many human-centric applications, such as active assisted living and in-home monitoring for elderly and physically impaired people. Although existing Wi-Fi-based human activity recognition methods report good results, their performance is affected by the changes in the ambient environment. In this work, we present Wi-Sense—a human activity recognition system that uses a convolutional neural network (CNN) to recognize human activities based on the environment-independent fingerprints extracted from the Wi-Fi channel state information (CSI). First, Wi-Sense captures the CSI by using a standard Wi-Fi network interface card. Wi-Sense applies the CSI ratio method to reduce the noise and the impact of the phase offset. In addition, it applies the principal component analysis to remove redundant information. This step not only reduces the data dimension but also removes the environmental impact. Thereafter, we compute the processed data spectrogram which reveals environment-independent time-variant micro-Doppler fingerprints of the performed activity. We use these spectrogram images to train a CNN. We evaluate our approach by using a human activity data set collected from nine volunteers in an indoor environment. Our results show that Wi-Sense can recognize these activities with an overall accuracy of 97.78%. To stress on the applicability of the proposed Wi-Sense system, we provide an overview of the standards involved in the health information systems and systematically describe how Wi-Sense HAR system can be integrated into the eHealth infrastructure.

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A time-efficient convolutional neural network model in human activity recognition

Multimedia Tools and Applications ◽

10.1007/s11042-020-10435-1 ◽

2021 ◽

Author(s):

Marjan Gholamrezaii ◽

SMT AlModarresi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Network Model ◽

Neural Network Model ◽

Human Activity Recognition

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A Hybrid Approach for Human Activity Recognition with Support Vector Machine and 1D Convolutional Neural Network

2020 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) ◽

10.1109/aipr50011.2020.9425332 ◽

2020 ◽

Author(s):

Md Maruf Hossain Shuvo ◽

Nafis Ahmed ◽

Koundinya Nouduri ◽

Kannappan Palaniappan

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Hybrid Approach ◽

Human Activity Recognition ◽

Support Vector

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1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition

10.1109/iicaiet51634.2021.9573979 ◽

2021 ◽

Author(s):

Jia Xin Goh ◽

Kian Ming Lim ◽

Chin Poo Lee

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Short Term Memory ◽

Human Activity Recognition ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Human activity recognition using temporal convolutional neural network architecture

Expert Systems with Applications ◽

10.1016/j.eswa.2021.116287 ◽

2021 ◽

pp. 116287

Author(s):

Yair A. Andrade-Ambriz ◽

Sergio Ledesma ◽

Mario-Alberto Ibarra-Manzano ◽

Marvella I. Oros-Flores ◽

Dora-Luz Almanza-Ojeda

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Network Architecture ◽

Human Activity Recognition ◽

Neural Network Architecture

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HARTH: A Human Activity Recognition Dataset for Machine Learning

Sensors ◽

10.3390/s21237853 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7853

Author(s):

Aleksej Logacjov ◽

Kerstin Bach ◽

Atle Kongsvold ◽

Hilde Bremseth Bårdstu ◽

Paul Jarle Mork

Keyword(s):

Neural Network ◽

Machine Learning ◽

Support Vector Machine ◽

Convolutional Neural Network ◽

Activity Recognition ◽

Human Activity ◽

Short Term Memory ◽

Human Activity Recognition ◽

Support Vector ◽

Free Living

Existing accelerometer-based human activity recognition (HAR) benchmark datasets that were recorded during free living suffer from non-fixed sensor placement, the usage of only one sensor, and unreliable annotations. We make two contributions in this work. First, we present the publicly available Human Activity Recognition Trondheim dataset (HARTH). Twenty-two participants were recorded for 90 to 120 min during their regular working hours using two three-axial accelerometers, attached to the thigh and lower back, and a chest-mounted camera. Experts annotated the data independently using the camera’s video signal and achieved high inter-rater agreement (Fleiss’ Kappa =0.96). They labeled twelve activities. The second contribution of this paper is the training of seven different baseline machine learning models for HAR on our dataset. We used a support vector machine, k-nearest neighbor, random forest, extreme gradient boost, convolutional neural network, bidirectional long short-term memory, and convolutional neural network with multi-resolution blocks. The support vector machine achieved the best results with an F1-score of 0.81 (standard deviation: ±0.18), recall of 0.85±0.13, and precision of 0.79±0.22 in a leave-one-subject-out cross-validation. Our highly professional recordings and annotations provide a promising benchmark dataset for researchers to develop innovative machine learning approaches for precise HAR in free living.

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