Hybrid YOLO-Inception-ResNetV2 Pipeline for Automated Human Activity Recognition in Controlled Environments. [PDF]
Mandadapu P.
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InclusiveHAR: A smartphone-based dataset for human activity recognition across diverse physical abilities. [PDF]
Kamel Tabbakh SR +8 more
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DeM-FCN: an ultra-lightweight and purely convolutional framework for edge-native human activity recognition in wearable fitness tracking. [PDF]
Xu Y +8 more
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RadarSSM: A Lightweight Spatiotemporal State Space Network for Efficient Radar-Based Human Activity Recognition. [PDF]
Zhao R, Miao F, Liu Y.
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An IoT-enabled CRNN framework for secure wearable sensor-based activity recognition in physical education. [PDF]
Yuan J, Zhang Y, Chen B.
europepmc +1 more source
Dynamic-Attentive Selective Mamba with Group-Aware Convolution for Wearable Sensor-Based Sports and Daily Activity Recognition. [PDF]
Li Z, Kang W.
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Activity recognition is an emerging field that demands active research in ubiquitous computing for analyzing complex scenarios such as concurrent situation assessment and domination of major over the minor activities. In this paper, an evolutionary ensembles approach using Genetic Algorithm (GA) as a homogeneous learner has been proposed. This approach
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This paper outlines a proposal regarding the use of machine learning, specifically a long-short term model, to increase the military’s effectiveness and safety protocols. The approach is to collect data from weapons training and apply it to a model that can distinguish between weapon activities.
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Egocentric Activity Recognition on a Budget
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018Recent advances in embedded technology have enabled more pervasive machine learning. One of the common applications in this field is Egocentric Activity Recognition (EAR), where users wearing a device such as a smartphone or smartglasses are able to receive feedback from the embedded device. Recent research on activity recognition has mainly focused on
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