Approach For Human Action Recognition Research Articles

Segment-Based Unsupervised Deep Learning for Human Activity Recognition using Accelerometer Data and SBOA based Channel Attention Networks

The deep learning community has increasingly focused on the critical challenges of human activity segmentation and detection based on sensors, which have numerous real-world applications. In most prior efforts, activity segmentation and recognition have been treated as separate processes, relying on pre-segmented sensor streams. This research proposes an unsupervised deep learning approach for Human Activity Recognition (HAR) that is segment-based, with an emphasis on activity continuity. The approach integrates segment-based SimCLR with Segment Feature Decorrelation (SDFD) and a new framework that leverages pairs of segment data for contrastive learning of visual representations. Furthermore, the Secretary Bird Optimization Algorithm (SBOA) and Channel Attention with Spatial Attention Network (CASANet) are utilized to enhance the performance of sensor-based human activity detection. CASANet effectively extracts key features and spatial dependencies in sensor data, while SBOA optimizes the model for greater accuracy and generalization. Evaluations on two publicly available datasets—Mhealth and PAMAP2—demonstrated an average F1 score of 98%, highlighting the approach’s efficacy in improving activity recognition performance.

Hybrid Deep Learning Approaches for Human Activity Recognition and Postural Transitions Using Mobile Device Sensors

Hybrid Deep Learning Approaches for Human Activity Recognition and Postural Transitions Using Mobile Device Sensors

A Dual Pipeline With Spatio-Temporal Attention Fusion Approach for Human Activity Recognition

A Dual Pipeline With Spatio-Temporal Attention Fusion Approach for Human Activity Recognition

A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation

A Comparative Analysis of XGBoost and LightGBM Approaches for Human Activity Recognition: Speed and Accuracy Evaluation

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works.

Improved skeleton-based activity recognition using convolutional block attention module

Inferring human activities from the skeletons extracted from activity photos or videos is a fundamental yet important issue in the research community of computer vision. Current skeleton-based activity recognition methods have accomplished this task by extracting features in either spatial or temporal perspective, or combining both views; however, the extracted features lack further after-processing like feature augmentation. Aiming at the above shortcomings, this article presents a new human activity recognition approach based on human skeleton data, named Convolutional Block Attention Module-based Spatial Temporal Convolutional Network (CBAM-STGCN). To enhance the discriminative validity of the generated features, we leverage the attention mechanism to weight feature units within each dimension by introducing two attention modules designed in terms of different aspects into the model architecture. Specifically, the original feature map of an activity sample is adaptively refined by the convolutional block attention module, where two separate attention maps obtained with regard to spatial and channel dimensions are adopted to generate the corresponding attended feature maps. Evaluation results on the Kinetics benchmark dataset showed that the proposed model has improved 1.76 % and 2.43 % according to top-1 and top-5 metrics, respectively, compared to the conventional baseline model. Further ablation experiments proved that both channel and spatial attention modules contribute to the model performance.

STAR-3D: A Holistic Approach for Human Activity Recognition in the Classroom Environment

The video camera is essential for reliable activity monitoring, and a robust analysis helps in efficient interpretation. The systematic assessment of classroom activity through videos can help understand engagement levels from the perspective of both students and teachers. This practice can also help in robot-assistive classroom monitoring in the context of human–robot interaction. Therefore, we propose a novel algorithm for student–teacher activity recognition using 3D CNN (STAR-3D). The experiment is carried out using India’s indigenously developed supercomputer PARAM Shivay by the Centre for Development of Advanced Computing (C-DAC), Pune, India, under the National Supercomputing Mission (NSM), with a peak performance of 837 TeraFlops. The EduNet dataset (registered under the trademark of the DRSTATM dataset), a self-developed video dataset for classroom activities with 20 action classes, is used to train the model. Due to the unavailability of similar datasets containing both students’ and teachers’ actions, training, testing, and validation are only carried out on the EduNet dataset with 83.5% accuracy. To the best of our knowledge, this is the first attempt to develop an end-to-end algorithm that recognises both the students’ and teachers’ activities in the classroom environment, and it mainly focuses on school levels (K-12). In addition, a comparison with other approaches in the same domain shows our work’s novelty. This novel algorithm will also influence the researcher in exploring research on the “Convergence of High-Performance Computing and Artificial Intelligence”. We also present future research directions to integrate the STAR-3D algorithm with robots for classroom monitoring.

A Self-Supervised Human Activity Recognition Approach via Body Sensor Networks in Smart City

In smart cities, pervasive sensing and wearable computing techniques are increasingly being employed to monitor and recognize human activities through body sensor networks, which have been widely used in urban safety, healthcare, and manufacturing. However, most researchers regard human activity recognition (HAR) as a high-cost research task requiring a large amount of labeled data, which is often unrealistic in real-world applications. To address this problem, we propose a self-supervised learning framework for HAR (SS-HAR). SS-HAR initially takes the unlabeled data generated by data augmentation as the input of the network, mines the supervised information of the unlabeled data under the effect of self-supervised learning, and uses the obtained backbone network as a feature extractor to extract activity features for subsequent classification. After that, we use part of the labeled data as the training set and extract the activity features using the backbone network for training and fitting the classifier. Then we utilize the rest of the data to verify the feasibility and effectiveness of the proposed self-supervised learning method. We have conducted multiple experiments on three publicly available datasets and one self-collected basketball activity dataset SZU_HAD_Basketball. The experimental results show that the SS-HAR method is able to achieve higher classification accuracy and stability than supervised and semi-supervised methods. Specifically, on the UCI dataset, SS-HAR achieves better classification performance compared to other approaches which improve the classification accuracy by 1% over the supervised method and by 5%-6% over the semi-supervised method respectively.

A novel approach for simultaneous human activity recognition and pose estimation via skeleton-based leveraging WiFi CSI with YOLOv8 and mediapipe frameworks

A novel approach for simultaneous human activity recognition and pose estimation via skeleton-based leveraging WiFi CSI with YOLOv8 and mediapipe frameworks

SpATr: MoCap 3D human action recognition based on spiral auto-encoder and transformer network

Recent technological advancements have significantly expanded the potential of human action recognition through harnessing the power of 3D data. This data provides a richer understanding of actions, including depth information that enables more accurate analysis of spatial and temporal characteristics. In this context, We study the challenge of 3D human action recognition. Unlike prior methods, that rely on sampling 2D depth images, skeleton points, or point clouds, often leading to substantial memory requirements and the ability to handle only short sequences, we introduce a novel approach for 3D human action recognition, denoted as SpATr (Spiral Auto-encoder and Transformer Network), specifically designed for fixed-topology mesh sequences. The SpATr model disentangles space and time in the mesh sequences. A lightweight auto-encoder, based on spiral convolutions, is employed to extract spatial geometrical features from each 3D mesh. These convolutions are lightweight and specifically designed for fix-topology mesh data. Subsequently, a temporal transformer, based on self-attention, captures the temporal context within the feature sequence. The self-attention mechanism enables long-range dependencies capturing and parallel processing, enabling scalability for long sequences. The proposed method is evaluated on three prominent 3D human action datasets: Babel, MoVi, and BMLrub, from the Archive of Motion Capture As Surface Shapes (AMASS). Our results analysis demonstrates the competitive performance of our SpATr model in 3D human action recognition while maintaining efficient memory usage. The code and the training results are publicly available at https://github.com/h-bouzid/spatr.

A New Time-Series Classification Approach for Human Activity Recognition with Data Augmentation

A New Time-Series Classification Approach for Human Activity Recognition with Data Augmentation

WiPhase: A Human Activity Recognition Approach by Fusing of Reconstructed WiFi CSI Phase Features

WiPhase: A Human Activity Recognition Approach by Fusing of Reconstructed WiFi CSI Phase Features

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

Extension of physical activity recognition with 3D CNN using encrypted multiple sensory data to federated learning based on multi-key homomorphic encryption

Background and Objective: The Internet of medical things is enhancing smart healthcare services using physical wearable sensor-based devices connected to the Internet. Machine learning techniques play an important role in the core of these services for remotely consulting patients thanks to the pattern recognition from on-device data, which is transferred to the central servers from local devices. However, transferring personally identifiable information data to servers could become a source for hackers to steal from, manipulate and perform illegal activities. Federated learning is a new branch of machine learning that creates directly training models from on-device data and aggregates these learned models on the servers without centralized data. Another way to protect data confidentiality on computer systems is data encryption. Data encryption transforms data into another form that only users with authority to a decryption key can read. In this work, we propose a novel method enabling preservation of client privacy and protection of client biomedical data from illegal hackers while transmitting through the Internet.Methods: We propose a method applying 3-dimensional convolutional neural networks for human activity recognition using multiple sensory data. In order to protect the data, we apply the bitwise XOR operator encryption technique. Then, we extend our 3-dimensional convolutional neural network methods to both traditional federated learning and the federated learning based on multi-key homomorphic encryption using the proposed encrypting data.Results: Based on leave-one-out-cross-validation, the 3-dimensional method obtains an accuracy of 94.6% and of 94.9% (without data encrypting and without federated learning) tested on two different benchmarked datasets, Sport and DaLiAC respectively. Accuracy is decreased slightly to 89.5% (from 94.6% of the baseline) when we use the proposed encrypting data method. However, the encryption-data-based method still has a potential result compared to the state-of-the-art which only uses raw data. In addition, the proposed full federated learning scheme of this work shows that illegal persons who somehow can get the trained model transmitted via networks cannot infer the private result.Conclusions: This novel method for sensory data representation which translates temporal and frequency bio-signal values to voxel intensities that can encode 3-dimensional activity images. Secondly, the proposed 3-dimensional convolutional neural network methods outperform other deep-learning-based human activity recognition approaches. Finally, extensive experiments show the proposed data-encrypted federated learning approach can achieve feasibility in terms of efficiency in privacy preservation.

Multi-Camera-Based Human Activity Recognition for Human-Robot Collaboration in Construction.

As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human-robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human-robot collaboration in construction.

A multi-resolution fusion approach for human activity recognition from video data in tiny edge devices

Human Activity Recognition (HAR) is the process of automatic recognition of Activities of Daily Living (ADL) from human motion data captured in various data modalities by wearable and ambient sensors. Advances in Deep Learning, especially Convolutional Neural Networks (CNNs) and sequential models have revolutionalized HAR from video data sources. Although these models are incredibly accurate and utilize both spatial and temporal information, they require huge computation and memory resources — making them unsuitable for edge or wearable applications. Tiny Machine Learning (TinyML) aims to optimize these models in terms of compute and memory requirements – aiming to make them suitable for always-on resource constrained devices – leading to a reduction in communication latency and network traffic for HAR frameworks. In this paper, we propose a two-stream multi-resolution fusion architecture for HAR from video data modality. The context stream takes a resized image as input and the fovea stream takes the cropped center portion of the resized image as input, reducing the overall dimensionality. We tested two quantization methods: Post-Training Quantization (PTQ) and Quantization Aware Training (QAT) to optimize these models for deployment in edge devices and tested the performance in two challenging video datasets: KTH and UCF11. We performed ablation studies to validate the two-stream model performance. We deployed the proposed architecture in commercial resource constrained devices and monitored their performance in terms of inference latency and power consumption. The results indicate that the proposed architecture clearly outperforms other relevant single-stream models tested in this work in terms of accuracy, precision, recall, and F1-Score while also reducing the overall model size.

A Review on Challenges in Telerehabilitation and Human Activity Recognition Approaches during Covid-19 Pandemic

Impact of COVID-19 pandemic is widespread imposing limitations on the healthcare services all over the world. Due to this pandemic, governments around the world have imposed restrictions that limit individual freedom and have enforced social distance to prevent the collapse of national health care systems. In such situation, to offer medical care and rehabilitation to the patients, Telerehabilitation (TR) is a promising way of delivering healthcare facilities remotely using telecommunication and internet. Technological advancement has played the vital role to establish this TR technology to remotely assess patient’s physical condition and act accordingly during this pandemic. Likewise, Human Activity Recognition (HAR) is a key part of the recovery process for a wide variety of conditions, such as stroke, arthritis, brain injury, musculoskeletal injuries, Parkinson’s disease, and others. Different approaches of human activity recognition can be utilized to monitor the health and activity levels of such a patient effectively and TR allows to do this remotely. Therefore, in situations where conventional care is inadequate, combination of telerehabilitation and HAR approaches can be an effective means of providing treatment and these opportunities have become patently apparent during the COVID-19 outbreak. However, this new era of technical progress has significant limitations, and in this paper, our main focus is on the challenges of telerehabilitation and the various human activity recognition approaches. This study will help researchers identify a good activity detection platform for a TR system during and after COVID-19, considering TR and HAR challenges.

An efficient deep learning-based approach for human activity recognition using smartphone inertial sensors

Human activity recognition (HAR) has recently witnessed outstanding growth in health and entertainment applications. Owing to the availability of smartphones, many new methods and protocols for using the data from smartphones’ embedded sensors are emerging. Nonetheless, the methods carried out and published in the literature leave a wide area for improvement, in terms of accuracy, resource economy, and adaptation to real-world nuisances. On top of that, a novel classification method that is more economical and efficient is proposed in this paper using both 1D convolutional neural network (1D-CNN) parameters and handcrafted temporal and frequency features with the proficiency of a multilayer perceptron neural network (MLP) classifier. The method proposed requires only tri-axial accelerometer data, allowing it to be deployed even into lower equipment devices; it was tested within the two well-known benchmark datasets: UCI-HAR and Uni-MIB SHAR. Experimental results yield a classification accuracy exceeding 99%, outperforming many of the methods recently shown in the literature.

A multimodal fusion enabled ensemble approach for human activity recognition in smart homes.

How to deal with multi-modality data from different types of devices is a challenging issue for accurate recognition of human activities in a smart environment. In this paper, we propose a multimodal fusion enabled ensemble approach. Firstly, useful features collected from Bluetooth beacons, binary sensors, and smart floor are extracted and presented by fuzzy logic based-method with variable-size temporal windows. Secondly, a group of support vector machine classifiers are used to perform the classification task. Finally, a weighted ensemble method is used to obtain the final prediction. Especially, by applying the geometric framework, we are able to obtain the optimal weights for the ensemble. The proposed approach is evaluated on the UJAmI dataset. The experimental results demonstrate the efficacy and robustness of the proposed method.

Continual spatio-temporal graph convolutional networks

Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantly employ whole temporal sequences as their input, to the setting of online inference entails considerable computational redundancy. In this paper, we tackle this issue by reformulating the Spatio-Temporal Graph Convolutional Neural Network as a Continual Inference Network, which can perform step-by-step predictions in time without repeat frame processing. To evaluate our method, we create a continual version of ST-GCN, CoST-GCN, alongside two derived methods with different self-attention mechanisms, CoAGCN and CoS-TR. We investigate weight transfer strategies and architectural modifications for inference acceleration, and perform experiments on the NTU RGB+D 60, NTU RGB+D 120, and Kinetics Skeleton 400 datasets. Retaining similar predictive accuracy, we observe up to 109× reduction in time complexity, on-hardware accelerations of 26×, and reductions in maximum allocated memory of 52% during online inference.