Search Results (38)

Recent advances in drug development allowed for the identification of THRβ-selective thyromimetic TG68 as a very promising lipid lowering and anti-amyloid agent. In the current study, we first investigated the neuroprotective effects of TG68 on in vitro human models of neuroinflammation and β-amyloid neurotoxicity in order to expand our knowledge of the therapeutic potential of this novel thyromimetic. Subsequently, we examined metabolic and inflammatory profiles, along with cognitive changes, using a high-fat diet (HFD) mouse model of obesity. Our data demonstrated that TG68 was able to prevent either LPS/TNFα-induced inflammatory response or β-amyloid-induced cytotoxicity in human microglial (HMC3) cells. Next, we demonstrated that in HFD-fed mice, treatment with TG68 (10 mg/kg/day; 2 weeks) significantly reduced anxiety-like behavior in stretch–attend posture (SAP) tests while producing a 12% BW loss and a significant decrease in blood glucose and lipid levels. Notably, these data highlight a close relationship between improved serum metabolic parameters and a reduction of anxious behavior. Moreover, TG68 administration was observed to efficiently counteract HFD-altered central and peripheral expressions in mice with selected biomarkers of metabolic dysfunction, inflammation, and neurotoxicity, revealing promising neuroprotective effects. In conclusion, our work provides preliminary evidence that TG68 may represent a novel therapeutic opportunity for the treatment of interlinked diseases such as obesity and neurodegenerative diseases. Full article

Human activity recognition (HAR) is an important field in the application of millimeter-wave radar. Radar-based HARs typically use Doppler signatures as primary data. However, some common similar human activities exhibit similar features that are difficult to distinguish. Therefore, the identification of similar activities is a great challenge. Given this problem, a recognition method based on convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and class activation mapping (CAM) is proposed in this paper. The spectrogram is formed by processing the radar echo signal. The high-dimensional features are extracted by CNN, and then the corresponding feature vectors are fed into the BiLSTM to obtain the recognition results. Finally, the class activation mapping is used to visualize the decision recognition process of the model. Based on the data of four similar activities of different people collected by mm-wave radar, the experimental results show that the recognition accuracy of the proposed model reached 94.63%. Additionally, the output results of this model have strong robustness and generalization ability. It provides a new way to improve the accuracy of human similar posture recognition. Full article

Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. The objective of this study was to develop a sensor-based framework for the detection and measurement of high-flexion postures frequently adopted in occupational settings. IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (mDTW) distance-based Nearest Neighbour algorithm for the identification of twelve postures. Data from 50 participants were divided to develop and evaluate the mDTW model. Overall accuracies of 82.3% and 55.6% were reached when classifying movements from the testing and validation datasets, respectively, which increased to 86% and 74.6% when adjusting for imbalances between classification groups. The highest misclassification rates occurred between flatfoot squatting, heels-up squatting, and stooping, while the model was incapable of identifying sequences of walking based on a single stride template. The developed mDTW model proved robust in identifying high-flexion postures performed by participants both included and precluded from algorithm development, indicating its strong potential for the quantitative measurement of postural adoption in real-world settings. Full article

Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the model to dynamically adjust the weights according to the different input images to locate the human keypoints more precisely. Secondly, GMamba as the backbone network can effectively solve the long-sequence problem. The direct use of convolutional downsampling reduces selectivity for different stages of information flow. We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. The fusion of multi-channel information helped the model obtain rich pose information. Finally, we introduced an adaptive threshold focus loss (ATFL) to dynamically adjust the weights of different keypoints. We assigned higher weights to error-prone keypoints to strengthen the model’s attention to these points. Thus, we effectively improved the accuracy of keypoint identification in cases of occlusion, complex background, etc., and significantly improved the overall performance of attitude estimation and anti-interference ability. Experimental results showed that the AP and AP50 of the proposed algorithm on the COCO 2017 validation set were 72.2 and 92.6. Compared with the typical algorithm, it was improved by 1.1% on AP50. The proposed method can effectively detect the keypoints of the human body, and provides stronger robustness and accuracy for the estimation of human posture in complex scenes. Full article

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects. Full article

The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model’s robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model’s robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores. Full article

Physical activity (PA) offers many benefits for human health. However, beginners often feel discouraged when introduced to basic exercise routines. Due to lack of experience and personal guidance, they might abandon efforts or experience musculoskeletal injuries. Additionally, due to phenomena such as pandemics and limited access to supervised exercise spaces, especially for the elderly, the need to develop personalized systems has become apparent. In this work, we develop a monitored physical exercise system that offers real-time guidance and recommendations during exercise, designed to assist users in their home environment. For this purpose, we used posture estimation interfaces that recognize body movement using a computer or smartphone camera. The chosen pose estimation model was BlazePose. Machine learning and signal processing techniques were used to identify the exercise currently being performed. The performances of three machine learning classifiers were evaluated for the exercise recognition task, achieving test-set accuracy between 94.76% and 100%. The research methodology included kinematic analysis (KA) of five selected exercises and statistical studies on performance and range of motion (ROM), which enabled the identification of deviations from the expected exercise execution to support guidance. To this end, data was collected from 57 volunteers, contributing to a comprehensive understanding of exercise performance. By leveraging the capabilities of the BlazePose model, an interactive tool for patients is proposed that could support rehabilitation programs remotely. Full article

The emergence of the Metaverse is raising important questions in the field of human–machine interaction that must be addressed for a successful implementation of the new paradigm. Therefore, the exploration and integration of both technology and human interaction within this new framework are needed. This paper describes an innovative and technically viable proposal for virtual shopping in the fashion field. Virtual hands directly scanned from the real world have been integrated, after a retopology process, in a virtual environment created for the Metaverse, and have been integrated with digital nails. Human interaction with the Metaverse has been carried out through the acquisition of the real posture of the user’s hands using an infrared-based sensor and mapping it in its virtualized version, achieving natural identification. The technique has been successfully tested in an immersive shopping experience with the Meta Quest 2 headset as a pilot project, where a transactions mechanism based on the blockchain technology (non-fungible tokens, NFTs) has allowed for the development of a feasible solution for massive audiences. The consumers’ reactions were extremely positive, with a total of 250 in-person participants and 120 remote accesses to the Metaverse. Very interesting technical guidelines are raised in this project, the resolution of which may be useful for future implementations. Full article

Keystroke dynamics is a soft biometric based on the assumption that humans always type in uniquely characteristic manners. Previous works mainly focused on analyzing the key press or release events. Unlike these methods, we explored a novel visual modality of keystroke dynamics for human identification using a single RGB-D sensor. In order to verify this idea, we created a dataset dubbed KD-MultiModal, which contains 243.2 K frames of RGB images and depth images, obtained by recording a video of hand typing with a single RGB-D sensor. The dataset comprises RGB-D image sequences of 20 subjects (10 males and 10 females) typing sentences, and each subject typed around 20 sentences. In the task, only the hand and keyboard region contributed to the person identification, so we also propose methods of extracting Regions of Interest (RoIs) for each type of data. Unlike the data of the key press or release, our dataset not only captures the velocity of pressing and releasing different keys and the typing style of specific keys or combinations of keys, but also contains rich information on the hand shape and posture. To verify the validity of our proposed data, we adopted deep neural networks to learn distinguishing features from different data representations, including RGB-KD-Net, D-KD-Net, and RGBD-KD-Net. Simultaneously, the sequence of point clouds also can be obtained from depth images given the intrinsic parameters of the RGB-D sensor, so we also studied the performance of human identification based on the point clouds. Extensive experimental results showed that our idea works and the performance of the proposed method based on RGB-D images is the best, which achieved $99.44\%$ accuracy based on the unseen real-world data. To inspire more researchers and facilitate relevant studies, the proposed dataset will be publicly accessible together with the publication of this paper. Full article

As human–robot interaction becomes more prevalent in industrial and clinical settings, detecting changes in human posture has become increasingly crucial. While recognizing human actions has been extensively studied, the transition between different postures or movements has been largely overlooked. This study explores using two deep-learning methods, the linear Feedforward Neural Network (FNN) and Long Short-Term Memory (LSTM), to detect changes in human posture among three different movements: standing, walking, and sitting. To explore the possibility of rapid posture-change detection upon human intention, the authors introduced transition stages as distinct features for the identification. During the experiment, the subject wore an inertial measurement unit (IMU) on their right leg to measure joint parameters. The measurement data were used to train the two machine learning networks, and their performances were tested. This study also examined the effect of the sampling rates on the LSTM network. The results indicate that both methods achieved high detection accuracies. Still, the LSTM model outperformed the FNN in terms of speed and accuracy, achieving 91% and 95% accuracy for data sampled at 25 Hz and 100 Hz, respectively. Additionally, the network trained for one test subject was able to detect posture changes in other subjects, demonstrating the feasibility of personalized or generalized deep learning models for detecting human intentions. The accuracies for posture transition time and identification at a sampling rate of 100 Hz were 0.17 s and 94.44%, respectively. In summary, this study achieved some good outcomes and laid a crucial foundation for the engineering application of digital twins, exoskeletons, and human intention control. Full article

Aiming at the problem of insufficient accuracy caused by the insufficient mining of spatiotemporal features in the process of unsafe behavior and danger identification of construction personnel, the traditional two-stream convolution model is improved, and a two-stream convolution dangerous behavior recognition model based on Faster R-CNN-LSTM is proposed. In this model, the Faster R-CNN network is connected in parallel with the LSTM network. The Faster R-CNN network is used as the spatial flow, and the human spatial motion posture is divided into static and dynamic features to extract the anchor point features, respectively. The fusion of the two is used as the output of the spatial flow. An improved sliding long-term and short-term memory network is used in the time flow to increase the extraction ability of the time series features of the construction personnel. Finally, the two branches are fused in time and space to classify and identify whether the construction personnel wear safety helmets. The results show that the MAP of the improved Faster R-CNN-LSTM network framework is increased by 15%. The original CNN-LSTM network framework detected four targets, but there was one misdetection, with an accuracy of 91.48%. The improved frame detection accuracy reaches 99.99%, and there is no error detection. The proposed method is superior to the pre-improvement and other methods that can effectively identify the unsafe behavior of construction workers on construction sites and also has a good distinction effect on fuzzy actions. Full article

As a biological characteristic, gait uses the posture characteristics of human walking for identification, which has the advantages of a long recognition distance and no requirement for the cooperation of subjects. This paper proposes a research method for recognising gait images at the frame level, even in cases of discontinuity, based on human keypoint extraction. In order to reduce the dependence of the network on the temporal characteristics of the image sequence during the training process, a discontinuous frame screening module is added to the front end of the gait feature extraction network, to restrict the image information input to the network. Gait feature extraction adds a cross-stage partial connection (CSP) structure to the spatial–temporal graph convolutional networks’ bottleneck structure in the ResGCN network, to effectively filter interference information. It also inserts XBNBlock, on the basis of the CSP structure, to reduce estimation caused by network layer deepening and small-batch-size training. The experimental results of our model on the gait dataset CASIA-B achieve an average recognition accuracy of 79.5%. The proposed method can also achieve 78.1% accuracy on the CASIA-B sample, after training with a limited number of image frames, which means that the model is more robust. Full article

Human posture recognition technology is widely used in the fields of healthcare, human-computer interaction, and sports. The use of a Frequency-Modulated Continuous Wave (FMCW) millimetre-wave (MMW) radar sensor in measuring human posture characteristics data is of great significance because of its robust and strong recognition capabilities. This paper demonstrates how human posture characteristics data are measured, classified, and identified using FMCW techniques. First of all, the characteristics data of human posture is measured with the MMW radar sensors. Secondly, the point cloud data for human posture is generated, considering both the dynamic and static features of the reflected signal from the human body, which not only greatly reduces the environmental noise but also strengthens the reflection of the detected target. Lastly, six different machine learning models are applied for posture classification based on the generated point cloud data. To comparatively evaluate the proper model for point cloud data classification procedure—in addition to using the traditional index—the Kappa index was introduced to eliminate the effect due to the uncontrollable imbalance of the sampling data. These results support our conclusion that among the six machine learning algorithms implemented in this paper, the multi-layer perceptron (MLP) method is regarded as the most promising classifier. Full article

Multi-person behavior event recognition has become an increasingly challenging research field in human–computer interaction. With the rapid development of deep learning and computer vision, it plays an important role in the inference and analysis of real sports events, that is, given the video frequency of sports events, when letting it analyze and judge the behavior trend of athletes, often faced with the limitations of large-scale data sets and hardware, it takes a lot of time, and the accuracy of the results is not high. Therefore, we propose a deep clustering learning network for motion recognition under the self-attention mechanism, which can efficiently solve the accuracy and efficiency problems of sports event analysis and judgment. This method can not only solve the problem of gradient disappearance and explosion in the recurrent neural network (RNN), but also capture the internal correlation between multiple people on the sports field for identification, etc., by using the long and short-term memory network (LSTM), and combine the motion coding information in the key frames with the deep embedded clustering (DEC) to better analyze and judge the complex behavior change types of athletes. In addition, by using the self-attention mechanism, we can not only analyze the whole process of the sports video macroscopically, but also focus on the specific attributes of the movement, extract the key posture features of the athletes, further enhance the features, effectively reduce the amount of parameters in the calculation process of self-attention, reduce the computational complexity, and maintain the ability to capture details. The accuracy and efficiency of reasoning and judgment are improved. Through verification on large video datasets of mainstream sports, we achieved high accuracy and improved the efficiency of inference and prediction. It is proved that the method is effective and feasible in the analysis and reasoning of sports videos. Full article

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance. Full article