Human physical activity recognition based on computer vision with deep learning model

2016 ◽  
Author(s):  
Lingfei Mo ◽  
Fan Li ◽  
Yanjia Zhu ◽  
Anjie Huang
Keyword(s):  
Physical Activity ◽  
Computer Vision ◽  
Deep Learning ◽  
Activity Recognition ◽  
Learning Model ◽  
Physical Activity Recognition ◽  
Deep Learning Model

scholarly journals Masked Face Analysis via Multi-Task Deep Learning

2021 ◽  
Vol 7 (10) ◽  
pp. 204
Author(s):  
Vatsa S. Patel ◽  
Zhongliang Nie ◽  
Trung-Nghia Le ◽  
Tam V. Nguyen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Unified Framework ◽  
Improve Performance ◽  
Face Analysis ◽  
And Gender ◽  
Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

scholarly journals A Hybrid Deep Learning Model for Human Activity Recognition Using Multimodal Body Sensing Data

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 99152-99160 ◽  
Author(s):  
Abdu Gumaei ◽  
Mohammad Mehedi Hassan ◽  
Abdulhameed Alelaiwi ◽  
Hussain Alsalman
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Learning Model ◽  
Human Activity Recognition ◽  
Sensing Data ◽  
Deep Learning Model

scholarly journals Hockey activity recognition using pre-trained deep learning model

ICT Express ◽  
2020 ◽  
Vol 6 (3) ◽  
pp. 170-174
Author(s):  
Keerthana Rangasamy ◽  
Muhammad Amir As’ari ◽  
Nur Azmina Rahmad ◽  
Nurul Fathiah Ghazali
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Learning Model ◽  
Deep Learning Model

ST-DeepHAR: Deep Learning Model for Human Activity Recognition in IoHT Applications

2020 ◽  
pp. 1-1
Author(s):  
Mohamed Abdel-Basset ◽  
Hossam Hawash ◽  
Ripon K. Chakrabortty ◽  
Michael Ryan ◽  
Mohamed Elhoseny ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Learning Model ◽  
Human Activity Recognition ◽  
Deep Learning Model

scholarly journals Enhanced Hand-Oriented Activity Recognition Based on Smartwatch Sensor Data Using LSTMs

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1570
Author(s):  
Sakorn Mekruksavanich ◽  
Anuchit Jitpattanakul ◽  
Phichai Youplao ◽  
Preecha Yupapin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Activity Recognition ◽  
Learning Model ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Deep Learning Model ◽  
F Measure

The creation of the Internet of Things (IoT), along with the latest developments in wearable technology, has provided new opportunities in human activity recognition (HAR). The modern smartwatch offers the potential for data from sensors to be relayed to novel IoT platforms, which allow the constant tracking and monitoring of human movement and behavior. Recently, traditional activity recognition techniques have done research in advance by choosing machine learning methods such as artificial neural network, decision tree, support vector machine, and naive Bayes. Nonetheless, these conventional machine learning techniques depend inevitably on heuristically handcrafted feature extraction, in which human domain knowledge is normally limited. This work proposes a hybrid deep learning model called CNN-LSTM that employed Long Short-Term Memory (LSTM) networks for activity recognition with the Convolution Neural Network (CNN). The study makes use of HAR involving smartwatches to categorize hand movements. Using the study based on the Wireless Sensor Data Mining (WISDM) public benchmark dataset, the recognition abilities of the deep learning model can be accessed. The accuracy, precision, recall, and F-measure statistics are employed using the evaluation metrics to assess the recognition abilities of LSTM models proposed. The findings indicate that this hybrid deep learning model offers better performance than its rivals, where the achievement of 96.2% accuracy, while the f-measure is 96.3%, is obtained. The results show that the proposed CNN-LSTM can support an improvement of the performance of activity recognition.

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