RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Kumar, Pranjal; Chauhan, Siddhartha

doi:10.1007/s42486-021-00063-5

Cited by 10 publications

(4 citation statements)

References 195 publications

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“…Machine learning is one of the key emerging technology for processing health data for diagnosis purposes and is playing an important role in enhancing the performance of WBANs [ 239 , 240 ]. Communication networks are also benefiting from technology [ 241 ].…”

Section: Open Issuesmentioning

confidence: 99%

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

Bhatti

Saleem

Imran

et al. 2022

Sensors

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The deployment of wearable or body-worn devices is increasing rapidly, and thus researchers’ interests mainly include technical and economical issues, such as networking, interoperability, security, power optimization, business growth and regulation. To address these issues properly, previous survey papers usually focused on describing the wireless body area network architecture and network protocols. This implies that deployment issues and awareness issues of wearable and BAN devices are not emphasized in previous work. To defeat this problem, in this study, we have focused on feasibility, limitations, and security concerns in wireless body area networks. In the aspect of the economy, we have focused on the compound annual growth rate of these devices in the global market, different regulations of wearable/wireless body area network devices in different regions and countries of the world and feasible research projects for wireless body area networks. In addition, this study focuses on the domain of devices that are equally important to physicians, sportsmen, trainers and coaches, computer scientists, engineers, and investors. The outcomes of this study relating to physicians, fitness trainers and coaches indicate that the use of these devices means they would be able to treat their clients in a more effective way. The study also converges the focus of businessmen on the Annual Growth Rate (CAGR) and provides manufacturers and vendors with information about different regulatory bodies that are monitoring and regulating WBAN devices. Therefore, by providing deployment issues in the aspects of technology and economy at the same time, we believe that this survey can serve as a preliminary material that will lead to more advancements and improvements in deployment in the area of wearable wireless body area networks. Finally, we present open issues and further research direction in the area of wireless body area networks.

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Section: Open Issuesmentioning

confidence: 99%

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

Bhatti

Saleem

Imran

et al. 2022

Sensors

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“…The authors found that the decision tree algorithm outperformed other algorithms in terms of accuracy. This study [22] provides an overview of the current state of the art in deep learning for human activity recognition. The authors present a classification of deep learning models used for this task, including CNNs, RNNs, and Autoencoders, and discuss the challenges and opportunities in this field.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition in Smart Home using Deep Learning Techniques

Kolkar¹,

Geetha

2021

2021 13th International Conference on Information &Amp; Communication Technology and System (ICTS)

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The research on human activity recognition has provided novel solutions to many applications like healthcare, sports, and user profiling. Considering the complex nature of human activities, it is still challenging even after effective and efficient sensors are available. The existing works on human activity recognition using smartphone sensors focus on recognizing basic human activities like sitting, sleeping, standing, stair up and down and running. However, more than these basic activities is needed to analyze human behavioural pattern. The proposed framework recognizes basic human activities using deep learning models. Also, ambient sensors like PIR, pressure sensors, and smartphone-based sensors like accelerometers and gyroscopes are combined to make it hybrid-sensorbased human activity recognition. The hybrid approach helped derive more activities than the basic ones, which also helped derive human activity patterns or user profiling. User profiling provides sufficient information to identify daily living activity patterns and predict whether any anomaly exists. The framework provides the base for applications such as elderly monitoring when they are alone at home. The GRU model's accuracy of 95% is observed to recognize the basic activities. Finally, Human activity patterns over time are recognized based on the duration and frequency of the activities. It is observed that human activity pattern, like, morning walking duration, varies depending on the day of the week.

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“…Besides smart surveillance, HAR has numerous applications such as video retrieval [ 4 ] and video summarization [ 5 ]. However, accurate HAR using computer vision techniques is challenging due to instantaneous transition of events in successive frames, illumination variations, different viewpoints, cluttered background, and different scaling [ 6 ]. In videos context, activity recognition relies on the collection of multiple consecutive video frames, where both spatial and temporal information need to be analysed for an individual's body movements.…”

Section: Introductionmentioning

confidence: 99%

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos

Hussain

Ullah

et al. 2022

Computational Intelligence and Neuroscience

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Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from a sequence of frames is a challenging task due to cluttered background, different viewpoints, low resolution, and partial occlusion. Current CNN-based techniques use large-scale computational classifiers along with convolutional operators having local receptive fields, limiting their performance to capture long-range temporal information. Therefore, in this work, we introduce a convolution-free approach for accurate HAR, which overcomes the above-mentioned problems and accurately encodes relative spatial information. In the proposed framework, the frame-level features are extracted via pretrained Vision Transformer; next, these features are passed to multilayer long short-term memory to capture the long-range dependencies of the actions in the surveillance videos. To validate the performance of the proposed framework, we carried out extensive experiments on UCF50 and HMDB51 benchmark HAR datasets and improved accuracy by 0.944% and 1.414%, respectively, when compared to state-of-the-art deep models.

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RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Cited by 10 publications

References 195 publications

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

A Survey on Wireless Wearable Body Area Networks: A Perspective of Technology and Economy

Human Activity Recognition in Smart Home using Deep Learning Techniques

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos

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