Trung-Kien Tran

Automatic Human Action Recognition (HAR) has become the most active research topic in recent years due to its broad applications, ranging from health monitoring and video surveillance to human–robot interaction. In this article, we introduce a novel action recognition method, named MICA-Net, which combines data from multiple sensors to improve the efficiency of the HAR model. MICA-Net is composed of a lightweight 3D Convolutional Neural Network (CNN) , optimized for mobile devices, to extract visual features and a co-attention network that integrates a 2D CNN with a transformer to extract motion features. These extracted features are then continuously fused through dynamic gated Joint Cross-Attention Modules (JCAMs) . These modules capture the intra- and inter-modal relationship while adaptively learning the contribution of each modality across different scenarios. We evaluated our recognition model on four publicly available multimodal datasets, MMAct, UESTC-MMEA-CL, UTD-MHAD, and MuWiGes. On MMAct, our model achieves an impressive F1-score of 89.24% with Cross-Subject and 96.57% with Cross-Session, outperforming current state-of-the-art methods. Similarly, on the UESTC-MMEA-CL, UTD-MHAD, and MuWiGes datasets, it achieves outstanding accuracy of 99.24%, 94.72%, and 98.98%, respectively. To demonstrate the practicality of the model, we design a new compact version of a wrist-worn sensor device with Wi-Fi connectivity to an edge device, enhancing usability in human–machine interaction applications. Real-time deployment indicates its potential for real implementation in the future. Our code is publicly available at https://anonymous.4open.science/r/MICA-Net-682E .

Human Action Recognition (HAR) has emerged as an active research domain in recent years with wide-ranging applications in healthcare monitoring, smart home systems, and hu- man–robot interaction. This paper introduces a method, namely Mamba-MHAR (Mamba based Multimodal Human Action Recognition), a lightweight multimodal architecture aimed at improv- ing HAR performance by effectively integrating data from inertial sensors and egocentric videos. Mamba-MHAR consists of double Mamba-based branches, one for visual feature extraction - VideoMamba, and the other for motion feature extraction - MAMC. Both branches are built upon recently introduced Selective State Space Models (SSMs) to optimize the computational cost, and they are lately fused for final human activity classification. Mamba-MHAR achieves significant efficiency gains in terms of GPU usage, making it highly suitable for real-time deployment on edge and mobile devices. Extensive experiments were conducted on two challenging multimodal datasets UESTC-MMEA-CL and MuWiGes, which contain synchronized IMU and video data recorded in natural settings. The proposed Mamba-MHAR achieves 98.00% accuracy on UESTC-MMEA-CL and 98.58% on MuWiGes, surpassing state-of-the-art baselines. These results demonstrate that a simple yet efficient fusion of multimodal lightweight Mamba-based models provides a promising solution for scalable and low-power applications in pervasive computing environments.

Traumatic brain injury (TBI) is one of the most prevalent health conditions, with severity assessment serving as an initial step for management, prognosis,

In this work, we study the ability to use hand gestures for human-machine interaction from wrist-worn sensors. Towards this goal, we design a wrist-worn prototype to capture RGB video stream of hand gestures. Then we built a new wrist-worn gesture dataset (named WiGes) with various subjects in interaction with home appliances in different environments. To the best of our knowledge, this is the first benchmark released for studying hand gestures from a wrist-worn camera. We then evaluate various CNN models for vision-based recognition. Furthermore, we deeply analyze the models that produce the best trade-off between accuracy, memory requirement, and computational cost. We point out that among studied architectures, MoviNet produces the highest accuracy. Then, we introduce a new MoviNet-based two-stream architecture that takes both RGB and optical flow into account. Our proposed architecture increases the Top-1 accuracy by 1.36% and 3.67% according to two evaluation protocols. Our dataset, baselines, and proposed model analysis give instructive recommendations for human-machine interaction using hand-held devices.

The ability to stop malware as soon as they start spreading will always play an important role in defending computer systems. It must be a huge benefit for organizations as well as society if intelligent defense systems could themselves detect and prevent new types of malware as soon as they reveal only a tiny amount of samples. An approach introduced in this paper takes advantage of One-shot/Few-shot learning algorithms to solve the malware classification problems using a Memory Augmented Neural Network in combination with the Natural Language Processing techniques such as word2vec, n-gram. We embed the malware’s API calls, which are very valuable sources of information for identifying malware’s behaviors, in the different feature spaces, and then feed them to the one-shot/few-shot learning models. Evaluating the model on the two datasets (FFRI 2017 and APIMDS) shows that the models with different parameters could yield high accuracy on malware classification with only a few samples. For example, on the APIMDS dataset, it was able to guess 78.85% correctly after seeing only nine malware samples and 89.59% after fine-tuning with a few other samples. The results confirmed very good accuracies compared to the other traditional methods, and point to a new area of malware research.