Posture Recognition Research Articles

Retracted: Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

Retracted: Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision

A comprehensive assessment method for manned confined space layouts taking into account operational posture comfort supported by artificial intelligence algorithm

Given its specialized design application, the layout design of confined spaces for human occupancy has a significant impact on the efficiency of operators, especially in the design processes aimed at aviation, aerospace, navigation, and other significant engineering fields. The influence of operational posture on layout comfort is crucial, yet existing assessment methods have limited considerations for the impact of operational posture on layout comfort. Therefore, this study proposes a layout decision support method that integrates posture recognition, simulation, and simulation-based comfort comprehensive assessment. By combining intelligent recognition technology, simulation technology, and comprehensive evaluation methods, it aims to provide a highly reliable comprehensive evaluation result for layout design and decision recommendations. To validate the effectiveness of the method, we conducted a comfort experimental evaluation involving seven individuals in the layout design of human-occupied confined spaces at Northwestern Polytechnical University. The results indicate that the proposed method offers a more objective and comprehensive means for layout design and optimization. This Improvement method not only addresses the deficiencies in existing spatial layout evaluation methods and provides a theoretical basis for architectural layout planning evaluation but also offers new perspectives and methodological support for scientific layout design and planning.

A Low-Cost Inertial Measurement Unit Motion Capture System for Operation Posture Collection and Recognition.

In factories, human posture recognition facilitates human-machine collaboration, human risk management, and workflow improvement. Compared to optical sensors, inertial sensors have the advantages of portability and resistance to obstruction, making them suitable for factories. However, existing product-level inertial sensing solutions are generally expensive. This paper proposes a low-cost human motion capture system based on BMI 160, a type of six-axis inertial measurement unit (IMU). Based on WIFI communication, the collected data are processed to obtain the displacement of human joints' rotation angles around XYZ directions and the displacement in XYZ directions, then the human skeleton hierarchical relationship was combined to calculate the real-time human posture. Furthermore, the digital human model was been established on Unity3D to synchronously visualize and present human movements. We simulated assembly operations in a virtual reality environment for human posture data collection and posture recognition experiments. Six inertial sensors were placed on the chest, waist, knee joints, and ankle joints of both legs. There were 16,067 labeled samples obtained for posture recognition model training, and the accumulated displacement and the rotation angle of six joints in the three directions were used as input features. The bi-directional long short-term memory (BiLSTM) model was used to identify seven common operation postures: standing, slightly bending, deep bending, half-squatting, squatting, sitting, and supine, with an average accuracy of 98.24%. According to the experiment result, the proposed method could be used to develop a low-cost and effective solution to human posture recognition for factory operation.

Development of a multifunctional nursing bed system for in-bed position recognition and automatic repositioning

Bedridden elderly need to be repositioned at regular intervals to prevent pressure ulcers. However, repositioning, being one of the most physically demanding tasks in nursing, faces many challenges, including work-related injuries and limited manpower. To address these problems, we have developed a multifunctional nursing bed system. This system not only assists in repositioning and transitioning positions but also features in-bed position recognition, enabling automatic repositioning based on recognition results. In this paper, the repositioning process was analysed, and the nursing bed mechanism and its control design were developed. An algorithmic framework for in-bed position recognition, including preprocessing and convolutional neural networks, is proposed. The experimental results demonstrate that the nursing bed system facilitates comfortable repositioning and position transition for subjects. In a short-term experiment, the system demonstrated an accuracy rate of 96.83% for in-bed position recognition. Meanwhile, in a long-term experiment, it achieved a success rate of 93.89% for automatic repositioning. These findings indicate that the proposed system demonstrates substantial potential in nursing care, particularly in pressure ulcer prevention.

Smart Body Posture Recognition and Guiding System

In an era prioritizing health and wellness, the integration of the Smart Body Posture Recognition & Guiding System into self- service health kiosks signifies a paradigm shift in healthcare management. This groundbreaking research project leverages cutting-edge MediaPipe Body Tracking technology to revolutionize health monitoring capabilities. At its core, MediaPipe Body Tracking enables precise real-time identification and tracking of human body key points and movements. This breakthrough technology ensures unparalleled accuracy in health parameter measurements within the kiosk interface. A standout feature of this system is its embedded alert mechanism, surpassing conventional health monitoring by actively detecting and notifying individuals about poor body posture. Providing immediate corrective feedback, this feature significantly impacts wellness, fostering a proactive approach to posture and overall health management. This research delves into the intricacies of MediaPipe Body Tracking's algorithms and sensors, elucidating the technology's real-time feedback mechanism. The system's ability to address poor posture not only prompts immediate corrective action but also raises awareness about its long-term health implications. Beyond individual health monitoring, this innovation promises a transformative shift in healthcare paradigms, encouraging proactive health measures and empowering individuals to take charge of their well-being. The Smart Body Posture Recognition & Guiding System represents a significant advancement at the nexus of technology and healthcare, redefining how societies approach proactive health management. Keywords- MediaPipe, Posture Recognition, Heathcare

Object Localization and Sensing in Non-Line-of-Sight Using RFID Tag Matrices

RFID-based technology innovated a new field of wireless sensing, which has been applied in posture recognition, object localization, and the other sensing fields. Due to the presence of a Fresnel zone around a magnetic field when the RFID-based system is working, the signal undergoes significant changes when an object moves through two or more different Fresnel zones. Therefore, the moving object can be sensed more easily, and most of the sensing applications required the tag to be attached to the moving object for better sensing, significantly limiting their applications. The existing technologies to detect static objects in agricultural settings are mainly based on X-ray or high-power radar, which are costly and bulky, making them difficult to deploy on a large scale. It is a challenging task to sense a static target without a tag attached in NLOS (non-line-of-sight) detection with low cost. We utilized RFID technologies to sense the static foreign objects in agricultural products, and take metal, rock, rubber, and clod as sensing targets that are common in agriculture. By deploying tag matrices to create a sensing region, we observed the signal variations before and after the appearance of the targets in this sensing region, and determined the targets’ positions and their types. Here, we buried the targets in the media of seedless cotton and wheat, and detected them using a non-contact method. Research has illustrated that, by deploying appropriate tag matrices and adjusting the angle of a single RFID antenna, the matrices’ signals are sensitive to the static targets’ positions and their properties, i.e., matrices’ signals vary with different targets and their positions. Specifically, we achieved a 100% success rate in locating metallic targets, while the success rate for clods was the lowest at 86%. We achieved a 100% recognition rate for the types of all the four objects.

Driver Steering Behaviour Modelling Based on Neuromuscular Dynamics and Multi-Task Time-Series Transformer

Driver steering intention prediction provides an augmented solution to the design of an onboard collaboration mechanism between human driver and intelligent vehicle. In this study, a multi-task sequential learning framework is developed to predict future steering torques and steering postures based on upper limb neuromuscular electromyography signals. The joint representation learning for driving postures and steering intention provides an in-depth understanding and accurate modelling of driving steering behaviours. Regarding different testing scenarios, two driving modes, namely, both-hand and single-right-hand modes, are studied. For each driving mode, three different driving postures are further evaluated. Next, a multi-task time-series transformer network (MTS-Trans) is developed to predict the future steering torques and driving postures based on the multi-variate sequential input and the self-attention mechanism. To evaluate the multi-task learning performance and information-sharing characteristics within the network, four distinct two-branch network architectures are evaluated. Empirical validation is conducted through a driving simulator-based experiment, encompassing 21 participants. The proposed model achieves accurate prediction results on future steering torque prediction as well as driving posture recognition for both two-hand and single-hand driving modes. These findings hold significant promise for the advancement of driver steering assistance systems, fostering mutual comprehension and synergy between human drivers and intelligent vehicles.

Musical Note Position and Duration Recognition Model in Optical Music Recognition Using Convolutional Neural Network

This study aims to solve Optical Music Recognition (OMR) problems using a non-End-to-End (non-E2E) approach. Therefore, separate models for Position Recognition (PR) and Duration Recognition (DR) are constructed, with both employing Convolutional Neural Networks (CNN). In terms of constructing a non-E2E architecture to solve OMR problems, this study obtains superior evaluation results compared to previous research, with the PR and DR models achieving accuracies of 97.88% and 99.23%, respectively. In addition, this study employs template matching in conjunction with several supplementary tasks to identify the positions of musical notes and generate the corresponding note sequences in the intended reading format. Our Optical Music Recognition (OMR) system can accomplish comparable results to the E2E architecture by utilizing these techniques.

Lightweight Neural Network for Sleep Posture Classification Using Pressure Sensing Mat at Various Sensor Densities.

Recently, pressure-sensing mats have been widely used to capture static and dynamic pressure over sleep for posture recognition. Both a full-size mat with a low-density sensing array for figuring out the structure of the whole body and a miniature scale mat with a high-density sensing array for identifying the local characteristics around the chest have been investigated. However, both of the mat systems may face the challenge in the trade-off between the computational complexity (involving the size, density, etc. of the mat) and the performance of sleep posture recognition, where high performance may requires overcomplex computation and result in time latency in real-time sleep posture monitoring. In this paper, a lightweight neural network named ConcatNet, is proposed to realize sleep postures (supine, left, right, and prone) recognition in real time while maintaining a favorable performance. In ConcatNet, the inception module is proposed to extract the image features under multiple receptive fields, while the multi-layer feature fusion module is utilized to fuse deep and shallow features to enhance the model performance. To further improve the efficiency of the model, the depthwise convolution is adopoted. ConcatNet models in 3 different scales (ConcatNet-S, ConcatNet-M, and ConcatNet-L) are built to explore the impact of the sensor density on sleep posture recognition performance. Experimental results exhibit that ConcatNet-M corresponding to medium sensor density ( 16×16 ) achieved the best comprehensive performance, with short-term data cross-validation accuracy at 95.56% and overnight data testing accuracy at 94.68%. The model size is 7.91KB, FLOPs is 56.47K, and the inference time is only 0.38ms, which shows an outstanding performance of real-time sleep posture recognition with minimum consumption, indicating the potential to be deployed in mobile devices.

Multi-person dance tiered posture recognition with cross progressive multi-resolution representation integration.

Recognizing postures in multi-person dance scenarios presents challenges due to mutual body part obstruction and varying distortions across different dance actions. These challenges include differences in proximity and size, demanding precision in capturing fine details to convey action expressiveness. Robustness in recognition becomes crucial in complex real-world environments. To tackle these issues, our study introduces a novel approach, i.e., Multi-Person Dance Tiered Posture Recognition with Cross Progressive Multi-Resolution Representation Integration (CPMRI) and Tiered Posture Recognition (TPR) modules. The CPMRI module seamlessly merges high-level features, rich in semantic information, with low-level features that provide precise spatial details. Leveraging a cross progressive approach, it retains semantic understanding while enhancing spatial precision, bolstering the network's feature representation capabilities. Through innovative feature concatenation techniques, it efficiently blends high-resolution and low-resolution features, forming a comprehensive multi-resolution representation. This approach significantly improves posture recognition robustness, especially in intricate dance postures involving scale variations. The TPR module classifies body key points into core torso joints and extremity joints based on distinct distortion characteristics. Employing a three-tier tiered network, it progressively refines posture recognition. By addressing the optimal matching problem between torso and extremity joints, the module ensures accurate connections, refining the precision of body key point locations. Experimental evaluations against state-of-the-art methods using MSCOCO2017 and a custom Chinese dance dataset validate our approach's effectiveness. Evaluation metrics including Object Keypoint Similarity (OKS)-based Average Precision (AP), mean Average Precision (mAP), and Average Recall (AR) underscore the efficacy of the proposed method.

Detection method of students' online learning state based on posture recognition

Detection method of students' online learning state based on posture recognition

Wireless Tag Sensor Network for Apnea Detection and Posture Recognition Using LSTM

Wireless Tag Sensor Network for Apnea Detection and Posture Recognition Using LSTM

Detection method of students' online learning state based on posture recognition

Detection method of students' online learning state based on posture recognition

A Empirical Research on AI-Powered Athletic Posture Detection in Sports Training

Abstract: The current investigation delineates the efficacy of AI-facilitated detection of athletic postures within the realm of sports training. Employing a synthesis of literature review and empirical methodologies, data were amassed and scrutinized, affirming the study’s validity. The salient outcomes are manifold: (1) The frame difference algorithm efficaciously discerns inter-frame variances, evidencing pronounced adaptability and robustness, thereby enabling the recognition of weightlifting postures. (2) Confronting the challenge of negligible inter-frame disparities inherent in the frame difference algorithm, the research introduces a novel detection technique predicated on the cumulative inter-frame differences, which precisely pinpoints regions of posture alteration in weightlifting athletes. (3) Leveraging the dynamic space model of optical flow, the study ascertains the directional channel predicated on optical flow trajectory analyses, facilitating the identification of three distinct weightlifting postures: squatting, descending, and standing. (4) In alignment with the distinctive postural attributes of weightlifting athletes, a human posture paradigm was formulated, and a BP neural network classifier was deployed for both training and evaluative purposes, culminating in the successful differentiation of athlete from non-athlete entities within the training milieu. (5) The application of AI in posture recognition was extended to the scrutiny of pivotal postures and motions in weightlifting athletes, with experimental findings revealing a 98.21% accuracy rate in the recognition of force-exertion postures via the inter-frame difference method, and a flawless 100% accuracy in the identification of the apex and squatting postures. The enumeration of detected postures—encompassing knee extension, knee flexion, force application, squatting, and standing—through the poselet keyframe extraction approach, corresponded with the video count. Prospectively, AI’s role in athletic posture detection promises to augment coaches’ and athletes’ comprehension of their proficiencies and deficiencies, thereby steering training refinement and bolstering both the efficacy of training and the athletes’ caliber.

A flexible wearable sensor based on anti-swelling conductive hydrogels for underwater motion posture visualization assisted by deep learning

Anti-swelling hydrogel sensors are especially suitable for underwater motion monitoring and posture recognition, and have a broad application prospect in underwater human-computer interaction and intelligent sensing.

Human Sleep Posture Recognition Exploiting Spatial Morphology and Local Micro-Doppler Characteristics Using MIMO Radar

Human Sleep Posture Recognition Exploiting Spatial Morphology and Local Micro-Doppler Characteristics Using MIMO Radar

An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

An Approach for Human Posture Recognition Based on the Fusion PSE-CNN-BiGRU Model

Benchmarking ML Approaches to UWB-Based Range-Only Posture Recognition for Human Robot-Interaction

Benchmarking ML Approaches to UWB-Based Range-Only Posture Recognition for Human Robot-Interaction

Driver Posture Recognition: A review

Driver Posture Recognition: A review

Application of Human Posture Recognition and Classification in Performing Arts Education

Application of Human Posture Recognition and Classification in Performing Arts Education