scholarly journals A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8227
Author(s):  
Saad Irfan ◽  
Nadeem Anjum ◽  
Nayyer Masood ◽  
Ahmad S. Khattak ◽  
Naeem Ramzan
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Classification Accuracy ◽  
State Of The Art ◽  
Decision Fusion ◽  
Human Activity Recognition ◽  
Transition Activities ◽  
Accuracy And Precision ◽  
Deep Learning Model

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.

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

A Survey on Deep Learning for Human Activity Recognition

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Fuqiang Gu ◽  
Mu-Huan Chung ◽  
Mark Chignell ◽  
Shahrokh Valaee ◽  
Baoding Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Smart Home ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Comprehensive Survey ◽  
Conventional Machine

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

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 Empirical Study and Improvement on Deep Transfer Learning for Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 57 ◽  
Author(s):  
Renjie Ding ◽  
Xue Li ◽  
Lanshun Nie ◽  
Jiazhen Li ◽  
Xiandong Si ◽  
...  
Keyword(s):  
Deep Learning ◽  
Empirical Study ◽  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Sensor Data ◽  
Accurate Identification ◽  
First Time ◽  
Deep Learning Model

Human activity recognition (HAR) based on sensor data is a significant problem in pervasive computing. In recent years, deep learning has become the dominating approach in this field, due to its high accuracy. However, it is difficult to make accurate identification for the activities of one individual using a model trained on data from other users. The decline on the accuracy of recognition restricts activity recognition in practice. At present, there is little research on the transferring of deep learning model in this field. This is the first time as we known, an empirical study was carried out on deep transfer learning between users with unlabeled data of target. We compared several widely-used algorithms and found that Maximum Mean Discrepancy (MMD) method is most suitable for HAR. We studied the distribution of features generated from sensor data. We improved the existing method from the aspect of features distribution with center loss and get better results. The observations and insights in this study have deepened the understanding of transfer learning in the activity recognition field and provided guidance for further research.

scholarly journals Moving the Lab into the Mountains: A Pilot Study of Human Activity Recognition in Unstructured Environments

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 654
Author(s):  
Brian Russell ◽  
Andrew McDaid ◽  
William Toscano ◽  
Patria Hume
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
A Priori ◽  
Parameter Tuning ◽  
Learning Model ◽  
Assessment Method ◽  
Human Activity Recognition ◽  
The One ◽  
Deep Learning Model

Goal: To develop and validate a field-based data collection and assessment method for human activity recognition in the mountains with variations in terrain and fatigue using a single accelerometer and a deep learning model. Methods: The protocol generated an unsupervised labelled dataset of various long-term field-based activities including run, walk, stand, lay and obstacle climb. Activity was voluntary so transitions could not be determined a priori. Terrain variations included slope, crossing rivers, obstacles and surfaces including road, gravel, clay, mud, long grass and rough track. Fatigue levels were modulated between rested to physical exhaustion. The dataset was used to train a deep learning convolutional neural network (CNN) capable of being deployed on battery powered devices. The human activity recognition results were compared to a lab-based dataset with 1,098,204 samples and six features, uniform smooth surfaces, non-fatigued supervised participants and activity labelling defined by the protocol. Results: The trail run dataset had 3,829,759 samples with five features. The repetitive activities and single instance activities required hyper parameter tuning to reach an overall accuracy 0.978 with a minimum class precision for the one-off activity (climbing gate) of 0.802. Conclusion: The experimental results showed that the CNN deep learning model performed well with terrain and fatigue variations compared to the lab equivalents (accuracy 97.8% vs. 97.7% for trail vs. lab). Significance: To the authors knowledge this study demonstrated the first successful human activity recognition (HAR) in a mountain environment. A robust and repeatable protocol was developed to generate a validated trail running dataset when there were no observers present and activity types changed on a voluntary basis across variations in terrain surface and both cognitive and physical fatigue levels.

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