Evaluating state-of-the-art classifiers for human activity recognition using smartphones

2019 ◽  
Author(s):  
A. Lentzas ◽  
A. Agapitos ◽  
D. Vrakas
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition

scholarly journals Rate-Invariant Modeling in Lie Algebra for Activity Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1888
Author(s):  
Malek Boujebli ◽  
Hassen Drira ◽  
Makram Mestiri ◽  
Imed Riadh Farah
Keyword(s):  
Computer Vision ◽  
Lie Algebra ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
High Complexity ◽  
Human Motions ◽  
Inter Frame ◽  
Automatic Systems

Human activity recognition is one of the most challenging and active areas of research in the computer vision domain. However, designing automatic systems that are robust to significant variability due to object combinations and the high complexity of human motions are more challenging. In this paper, we propose to model the inter-frame rigid evolution of skeleton parts as the trajectory in the Lie group SE(3)×…×SE(3). The motion of the object is similarly modeled as an additional trajectory in the same manifold. The classification is performed based on a rate-invariant comparison of the resulting trajectories mapped to a vector space, the Lie algebra. Experimental results on three action and activity datasets show that the proposed method outperforms various state-of-the-art human activity recognition approaches.

scholarly journals Complex Deep Neural Networks from Large Scale Virtual IMU Data for Effective Human Activity Recognition Using Wearables

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8337
Author(s):  
Hyeokhyen Kwon ◽  
Gregory D. Abowd ◽  
Thomas Plötz
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
The State ◽  
Model Parameters ◽  
Data Systems ◽  
Performance Improvements ◽  
Measurement Units

Supervised training of human activity recognition (HAR) systems based on body-worn inertial measurement units (IMUs) is often constrained by the typically rather small amounts of labeled sample data. Systems like IMUTube have been introduced that employ cross-modality transfer approaches to convert videos of activities of interest into virtual IMU data. We demonstrate for the first time how such large-scale virtual IMU datasets can be used to train HAR systems that are substantially more complex than the state-of-the-art. Complexity is thereby represented by the number of model parameters that can be trained robustly. Our models contain components that are dedicated to capture the essentials of IMU data as they are of relevance for activity recognition, which increased the number of trainable parameters by a factor of 1100 compared to state-of-the-art model architectures. We evaluate the new model architecture on the challenging task of analyzing free-weight gym exercises, specifically on classifying 13 dumbbell execises. We have collected around 41 h of virtual IMU data using IMUTube from exercise videos available from YouTube. The proposed model is trained with the large amount of virtual IMU data and calibrated with a mere 36 min of real IMU data. The trained model was evaluated on a real IMU dataset and we demonstrate the substantial performance improvements of 20% absolute F1 score compared to the state-of-the-art convolutional models in HAR.

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.

Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow

2021 ◽  
pp. 2250009
Author(s):  
Arati Kushwaha ◽  
Ashish Khare ◽  
Manish Khare
Keyword(s):  
Optical Flow ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Video Sequences ◽  
Histogram Of Oriented Gradients ◽  
Art Methods ◽  
Orientation Information

Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, [Formula: see text]-measure, and Matthew’s correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods.

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 HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

2021 ◽  
Author(s):  
Hamza Ali Imran ◽  
Usama Latif
Keyword(s):  
Computer Vision ◽  
Activity Recognition ◽  
Human Activity ◽  
Health Monitoring ◽  
Inertial Sensors ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Computer Interface ◽  
Human Computer Interface ◽  
Dense Networks

Human Activity Recognition (HAR) an important area of research in the light of enormous applications that it provides, such as health monitoring, sports, entertainment, efficient human computer interface, child care, education and many more. Use of Computer Vision for Human Activity Recognition has many limitations. The use of inertial sensors which include accelerometer and gyroscopic sensors for HAR is becoming the norm these days considering their benefits over traditional Computer Vision techniques. In this paper we have proposed a 1-dimensional Convolutions Neural Network which is inspired by two state-of-the art architectures proposed for image classifications; namely Inception Net and Dense Net. We have evaluated its performance on two different publicly available datasets for HAR. Precision, Recall, F1-measure and accuracies are reported.<br>

scholarly journals A Survey of Vision-Based Transfer Learning in Human Activity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2412
Author(s):  
David Ada Adama ◽  
Ahmad Lotfi ◽  
Robert Ranson
Keyword(s):  
Transfer Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Computational Intelligence ◽  
State Of The Art ◽  
Sensory Information ◽  
Human Activity Recognition ◽  
Future Directions ◽  
Current State ◽  
Comprehensive Survey

Human activity recognition (HAR) and transfer learning (TL) are two broad areas widely studied in computational intelligence (CI) and artificial intelligence (AI) applications. Much effort has been put into developing suitable solutions to advance the current performance of existing systems. However, challenges are facing the existing methods of HAR. In HAR, the variations in data required in HAR systems pose challenges to many existing solutions. The type of sensory information used could play an important role in overcoming some of these challenges. Vision-based information in 3D acquired using RGB-D cameras is one type. Furthermore, with the successes encountered in TL, HAR stands to benefit from TL to address challenges to existing methods. Therefore, it is important to review the current state-of-the-art related to both areas. This paper presents a comprehensive survey of vision-based HAR using different methods with a focus on the incorporation of TL in HAR methods. It also discusses the limitations, challenges and possible future directions for more research.

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