CAR - a deep learning structure for concurrent activity recognition

Zhang, Yanyi; Li, Xinyu; Zhang, Jianyu; Chen, Shuhong; Zhou, Moliang; Farneth, Richard A.; Marsic, Ivan; Burd, Randall S.

doi:10.1145/3055031.3055058

Cited by 12 publications

(8 citation statements)

References 2 publications

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“…Notice that a single person executes the concurrent operation. In addition to mutual multimodal fusion, Zhang et al [130] have established a single fully linked bridge network for each candidate operation. That operation was separately identified by separate softmax layers on the final judgment sheet.…”

Section: G Parallel Activitiesmentioning

confidence: 99%

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Kumar

Chauhan

2021

CCF Trans. Pervasive Comp. Interact.

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The growing use of sensor tools and the Internet of Things requires sensors to understand the applications. There are major difficulties in realistic situations, though, that can impact the efficiency of the recognition system. Recently, as the utility of deep learning in many fields has been shown, various deep approaches were researched to tackle the challenges of detection and recognition. We present in this review a sample of specialized deep learning approaches for the identification of sensor based human behavior. Next, we present the multi-modal sensory data and include information for the public databases which can be used in different challenge tasks for study. A new taxonomy is then suggested, to organize deep approaches according to challenges. Deep problems and approaches connected to problems are summarized and evaluated to provide an analysis of the ongoing advancement in science. By the conclusion of this research, we are answering unanswered issues and providing perspectives into the future.

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Section: G Parallel Activitiesmentioning

confidence: 99%

RETRACTED ARTICLE: Human activity recognition with deep learning: overview, challenges and possibilities

Kumar

Chauhan

2021

CCF Trans. Pervasive Comp. Interact.

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“…These systems work on general activity recognition and cannot be directly used for medical activity recognition because of the challenges of real-time prediction, privacy concerns, concurrent activities, and noisy data. Depth videos have been used instead of RGB videos to address privacy concerns but achieved only moderate performance because the depth videos are gray-scale videos and lack sufficient features for recognizing complex activities [27].…”

Section: General Activity Recognitionmentioning

confidence: 99%

Video-based Concurrent Activity Recognition for Trauma Resuscitation

Zhang

Marsic

et al. 2020

2020 IEEE International Conference on Healthcare Informatics (ICHI)

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We introduce a video-based system for concurrent activity recognition during teamwork in a clinical setting. During system development, we preserved patient and provider privacy by precomputing spatio-temporal features. We extended the inflated 3D ConvNet (i3D) model for concurrent activity recognition. For the model training, we tuned the weights of the final stages of i3D using back-propagated loss from the fully-connected layer. We applied filtering on the model predictions to remove noisy predictions. We evaluated the system on five activities performed during trauma resuscitation, the initial management of injured patients in the emergency department. Our system achieved an average value of 74% average precision (AP) for these five activities and outperformed previous systems designed for the same domain. We visualized feature maps from the model, showing that the system learned to focus on regions relevant to performance of each activity.

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“…A general belief is that deep learning requires "Big Data" to be effective; but small datasets also produce good results [41][42] via data augmentation and transfer learning. Figure 1 summarizes the research on activity classification using deep learning for enhanced accuracy [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57] yielding precisions from 80 to almost 100%. It is hard to assess the different performances since all the deep learning algorithms are ad hoc and the size and the nature of datasets vary.…”

Section: Machine Learning Perspectivementioning

confidence: 99%

“…The problematic of multiple people in the field of view [47,54,56,59] is rarely studied as they mostly consider only one person. In [16,19,24,25,[60][61][62][63], the multi-static radar approach is utilized for classification from spectrograms using feature extraction.…”

Section: Machine Learning Perspectivementioning

confidence: 99%

Radar for assisted living in the context of Internet of Things for Health and beyond

Kernec¹,

Fioranelli²,

Yang³

et al. 2018

EasyChair Preprints

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This paper discusses the place of radar for assisted living in the context of IoT for Health and beyond. First, the context of assisted living and the urgency to address the problem is described. The second part gives a literature review of existing sensing modalities for assisted living and explains why radar is an upcoming preferred modality to address this issue. The third section presents developments in machine learning that helps improve performances in classification especially with deep learning with a reflection on lessons learned from it. The fourth section introduces recent published work from our research group in the area that shows promise with multimodal sensor fusion for classification and long short-term memory applied to early stages in the radar signal processing chain. Finally, we conclude with open challenges still to be addressed in the area and open to future research directions in animal welfare.

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CAR - a deep learning structure for concurrent activity recognition

Cited by 12 publications

References 2 publications

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

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

Video-based Concurrent Activity Recognition for Trauma Resuscitation

Radar for assisted living in the context of Internet of Things for Health and beyond

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