A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition

Lv, Tianqi; Wang, Xiaojuan; Jin, Lei; Xiao, Yabo; Song, Mei

doi:10.1109/access.2020.2986246

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…At last, a fully-connected layer and a softmax function were utilized for computing the probability of every class. Lv et al [13] introduce a technique to recognize human actions utilizing skeleton data by RGB-D camera, called Kinect device. The HAR was learning in the CV field.…”

Section: Related Workmentioning

confidence: 99%

Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

Alonazi¹,

Alnfiai²

2023

Computer Systems Science and Engineering

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Human-Computer Interaction (HCI) is a sub-area within computer science focused on the study of the communication between people (users) and computers and the evaluation, implementation, and design of user interfaces for computer systems. HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science. Usability is an aspect of HCI dedicated to guaranteeing that human-computer communication is, amongst other things, efficient, effective, and sustaining for the user. Simultaneously, Human activity recognition (HAR) aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions. The vision-based HAR study is the basis of several applications involving health care, HCI, and video surveillance. This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activity Recognition (FHODL-AR) on HCI driven usability. In the presented FHODL-AR technique, the input images are investigated for the identification of different human activities. For feature extraction, a modified SqueezeNet model is introduced by the inclusion of few bypass connections to the SqueezeNet among Fire modules. Besides, the FHO algorithm is utilized as a hyperparameter optimization algorithm, which in turn boosts the classification performance. To detect and categorize different kinds of activities, probabilistic neural network (PNN) classifier is applied. The experimental validation of the FHODL-AR technique is tested using benchmark datasets, and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.

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Section: Related Workmentioning

confidence: 99%

Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

Alonazi¹,

Alnfiai²

2023

Computer Systems Science and Engineering

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“…Our benchmark regroups head-to-head 17 articles (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) by sharing their approach concerning the human activity recognition task evaluated on the UniMiB-SHAR dataset. Deep learning was the method of choice in almost every case (21-26, 28-32, 34, 35) to try to achieve state-of-the-art results.…”

Section: Related Work From Benchmarkmentioning

confidence: 99%

The use of deep learning for smartphone-based human activity recognition

Stampfler

Elgendi²,

Fletcher³

et al. 2023

Front. Public Health

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The emerging field of digital phenotyping leverages the numerous sensors embedded in a smartphone to better understand its user's current psychological state and behavior, enabling improved health support systems for patients. As part of this work, a common task is to use the smartphone accelerometer to automatically recognize or classify the behavior of the user, known as human activity recognition (HAR). In this article, we present a deep learning method using the Resnet architecture to implement HAR using the popular UniMiB-SHAR public dataset, containing 11,771 measurement segments from 30 users ranging in age between 18 and 60 years. We present a unified deep learning approach based on a Resnet architecture that consistently exceeds the state-of-the-art accuracy and F1-score across all classification tasks and evaluation methods mentioned in the literature. The most notable increase we disclose regards the leave-one-subject-out evaluation, known as the most rigorous evaluation method, where we push the state-of-the-art accuracy from 78.24 to 80.09% and the F1-score from 78.40 to 79.36%. For such results, we resorted to deep learning techniques, such as hyper-parameter tuning, label smoothing, and dropout, which helped regularize the Resnet training and reduced overfitting. We discuss how our approach could easily be adapted to perform HAR in real-time and discuss future research directions.

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“…In Table A.5 ("Appendix 1"), the description of sensor-datasets is illustrated with attributes such as data source, #factors, sensor location, and activity type. It includes wearable sensor-based datasets (Alsheikh et al 2016;Asteriadis and Daras 2017;Zhang et al 2012;Chavarriaga et al 2013;Munoz-Organero 2019;Roggen et al 2010;Qin et al 2019), as well as smart-device sensor-based datasets (Ravi et al 2016;Cui and Xu 2013;Weiss et al 2019;Miu et al 2015;Reiss and Stricker 2012a, b;Lv et al 2020;Gani et al 2019;Stisen et al 2015;Röcker et al 2017;Micucci et al 2017) Apart from datasets mentioned in Table A.5, there are few more datasets worth mentioning such as Kasteren dataset (Kasteren et al 2011;, which is also very popular. (2) Vision-based HAR: Devices for collecting 3D data are CCTV cameras (Koppula and Saxena 2016;Devanne et al 2015;Zhang and Parker 2016;Li et al 2010;Duan et al 2020;Kalfaoglu et al 2020;Gorelick et al 2007;Mahadevan et al 2010), depth cameras (Cippitelli et al 2016;Gaglio et al 2015;Neili Boualia and Essoukri Ben Amara 2021;Ding et al 2016; Cornell Activity Datasets: CAD-60 & CAD-120 2021), and videos from public domains like YouTube and Hollywood movie scenes (Gu et al 2018;Soomro et al 2012;Kuehne et al 2011;Sigurdsson et...…”

Section: A Short Note On Har Datasetsmentioning

confidence: 99%

Human activity recognition in artificial intelligence framework: a narrative review

et al. 2022

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Human activity recognition (HAR) has multifaceted applications due to its worldly usage of acquisition devices such as smartphones, video cameras, and its ability to capture human activity data. While electronic devices and their applications are steadily growing, the advances in Artificial intelligence (AI) have revolutionized the ability to extract deep hidden information for accurate detection and its interpretation. This yields a better understanding of rapidly growing acquisition devices, AI, and applications, the three pillars of HAR under one roof. There are many review articles published on the general characteristics of HAR, a few have compared all the HAR devices at the same time, and few have explored the impact of evolving AI architecture. In our proposed review, a detailed narration on the three pillars of HAR is presented covering the period from 2011 to 2021. Further, the review presents the recommendations for an improved HAR design, its reliability, and stability. Five major findings were: (1) HAR constitutes three major pillars such as devices, AI and applications; (2) HAR has dominated the healthcare industry; (3) Hybrid AI models are in their infancy stage and needs considerable work for providing the stable and reliable design. Further, these trained models need solid prediction, high accuracy, generalization, and finally, meeting the objectives of the applications without bias; (4) little work was observed in abnormality detection during actions; and (5) almost no work has been done in forecasting actions. We conclude that: (a) HAR industry will evolve in terms of the three pillars of electronic devices, applications and the type of AI. (b) AI will provide a powerful impetus to the HAR industry in future. Supplementary Information The online version contains supplementary material available at 10.1007/s10462-021-10116-x.

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A Hybrid Network Based on Dense Connection and Weighted Feature Aggregation for Human Activity Recognition

Cited by 21 publications

References 32 publications

Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

Fire Hawk Optimizer with Deep Learning Enabled Human Activity Recognition

The use of deep learning for smartphone-based human activity recognition

Human activity recognition in artificial intelligence framework: a narrative review

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