Search Results (4)

Over recent years, automated Human Activity Recognition (HAR) has been an area of concern for many researchers due to its widespread application in surveillance systems, healthcare environments, and many more. This has led researchers to develop coherent and robust systems that efficiently perform HAR. Although there have been many efficient systems developed to date, still, there are many issues to be addressed. There are several elements that contribute to the complexity of the task, making it more challenging to detect human activities, i.e., (i) poor lightning conditions; (ii) different viewing angles; (iii) intricate clothing styles; (iv) diverse activities with similar gestures; and (v) limited availability of large datasets. However, through effective feature extraction, we can develop resilient systems for higher accuracies. During feature extraction, we aim to extract unique key body points and full-body features that exhibit distinct attributes for each activity. Our proposed system introduces an innovative approach for the identification of human activity in outdoor and indoor settings by extracting effective spatio-temporal features, along with a Multi-Class Support Vector Machine, which enhances the model’s performance to accurately identify the activity classes. The experimental findings show that our model outperforms others in terms of classification, accuracy, and generalization, indicating its efficient analysis on benchmark datasets. Various performance metrics, including mean recognition accuracy, precision, F1 score, and recall assess the effectiveness of our model. The assessment findings show a remarkable recognition rate of around 88.61%, 87.33, 86.5%, and 81.25% on the BIT-Interaction dataset, UT-Interaction dataset, NTU RGB + D 120 dataset, and PKUMMD dataset, respectively. Full article

Wearable electronics is a technology that closely integrates electronic devices with the human body or clothing, which can realize human–computer interaction, health monitoring, smart medical, and other functions. Wearable physical sensors are an important part of wearable electronics. They can sense various physical signals from the human body or the surrounding environment and convert them into electrical signals for processing and analysis. Nanowires (NW) have unique properties such as a high surface-to-volume ratio, high flexibility, high carrier mobility, a tunable bandgap, a large piezoresistive coefficient, and a strong light–matter interaction. They are one of the ideal candidates for the fabrication of wearable physical sensors with high sensitivity, fast response, and low power consumption. In this review, we summarize recent advances in various types of NW-based wearable physical sensors, specifically including mechanical, photoelectric, temperature, and multifunctional sensors. The discussion revolves around the structural design, sensing mechanisms, manufacture, and practical applications of these sensors, highlighting the positive role that NWs play in the sensing process. Finally, we present the conclusions with perspectives on current challenges and future opportunities in this field. Full article

Comfort can be considered as subjective feeling, which could be affected by the external ambient, by the physical activity, and by clothing. Considering the human body heat transfer system, it mainly depends on various parameters including clothing materials, external and internal environment, etc. The purpose of the current paper is to study and establish a quantitative relationship between one of the clothing parameters, ease allowance (air gap values) and the heat transfer through the human body to clothing materials and then to the environment. The study considered clothing which is integrated with the 3D ease allowance from the anthropometric and morphological data. Such incorporating of the clothing’s 3D ease control was essential to properly manage the air space between the body and the proposed clothing thermal regulation model. In the context of thermal comfort, a clothing system consisting of the human body, an ease allowance under clothing, a layer of textile materials, and a peripheral layer adjacent to the textile material was used. For the complete system, the heat transfer from the skin to the environment, which is influenced by thermoregulation of the human body, air gap, tissue, and environmental conditions were also considered. To model and predict the heat transfer between the human body and the temperature of skin and clothes, a 3D adaptive garment which could be adjusted with ease allowance was used. In the paper, a thermoregulatory model was developed and proposed to predict the temperature and heat within clothing material, skin, and air space. Based on the result, in general the main difference in the temperature of clothing and skin from segment to segment is due to the uneven distribution of air layers under the clothing. Full article

Human Pose Estimation (HPE) is defined as the problem of human joints’ localization (also known as keypoints: elbows, wrists, etc.) in images or videos. It is also defined as the search for a specific pose in space of all articulated joints. HPE has recently received significant attention from the scientific community. The main reason behind this trend is that pose estimation is considered as a key step for many computer vision tasks. Although many approaches have reported promising results, this domain remains largely unsolved due to several challenges such as occlusions, small and barely visible joints, and variations in clothing and lighting. In the last few years, the power of deep neural networks has been demonstrated in a wide variety of computer vision problems and especially the HPE task. In this context, we present in this paper a Deep Full-Body-HPE (DFB-HPE) approach from RGB images only. Based on ConvNets, fifteen human joint positions are predicted and can be further exploited for a large range of applications such as gesture recognition, sports performance analysis, or human-robot interaction. To evaluate the proposed deep pose estimation model, we apply it to recognize the daily activities of a person in an unconstrained environment. Therefore, the extracted features, represented by deep estimated poses, are fed to an SVM classifier. To validate the proposed architecture, our approach is tested on two publicly available benchmarks for pose estimation and activity recognition, namely the J-HMDBand CAD-60datasets. The obtained results demonstrate the efficiency of the proposed method based on ConvNets and SVM and prove how deep pose estimation can improve the recognition accuracy. By means of comparison with state-of-the-art methods, we achieve the best HPE performance, as well as the best activity recognition precision on the CAD-60 dataset. Full article