Real-time Posture Research Articles

A Real-Time Posture Detection Algorithm Based on Deep Learning

With the development of machine vision and multimedia technology, posture detection and related algorithms have become widely used in the field of human posture recognition. Traditional video surveillance methods have the disadvantages of slow detection speed, low accuracy, interference from occlusions, and poor real-time performance. This paper proposes a real-time pose detection algorithm based on deep learning, which can effectively perform real-time tracking and detection of single and multiple individuals in different indoor and outdoor environments and at different distances. First, a corresponding pose recognition dataset for complex scenes was created based on the YOLO network. Then, the OpenPose method was used to detect key points of the human body. Finally, the Kalman filter multi-object tracking method was used to predict the state of human targets within the occluded area. Real-time detection of human postures (sitting, stand up, standing, sit down, walking, fall down, and lying down) is achieved with corresponding alarms to ensure the timely detection and processing of emergencies.

Know Your Grip: Real-Time Holding Posture Recognition for Smartphones

This paper introduces a model that predicts four common smartphone-holding postures, aiming to enhance user interface adaptability. It is unique in being completely independent of platform and hardware, utilizing the inertial measurement unit (IMU) for real-time posture detection based on sensor data collected around tap gestures. The model identifies whether the user is holding and operating the smartphone with one hand or using both hands in different configurations. For model training and validation, sensor time series data undergo extensive feature extraction, including statistical, frequency, magnitude, and wavelet analyses. These features are incorporated into 74 distinct sets, tested across various machine learning frameworks—k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF)—and evaluated for their effectiveness using metrics such as cross-validation scores, test accuracy, Kappa statistics, confusion matrices, and ROC curves. The optimized model demonstrates a high degree of accuracy, successfully predicting the holding hand with a 95.7% success rate. This approach highlights the potential of leveraging sensor data to improve mobile user experiences by adapting interfaces to natural user interactions.

Virtual Yoga Assistant using Machine Learning and Artificial Intelligence

In recent years, yoga has become an integral part of life for people worldwide. This growing interest has created a demand for scientific analysis of yoga postures. Pose detection techniques offer a promising approach to identify and assist people in performing yoga poses with greater accuracy. However, posture recognition remains a challenging task due to the limited availability of datasets and the difficulty of real-time posture detection. To address this, a large dataset has been developed with over 5,500 images representing ten different yoga poses. A tf-pose estimation algorithm is used to create a skeletal overlay on each image in real time, drawing the human body’s skeleton to extract joint angles. These joint angles serve as features for training various machine learning models. The dataset is split, with 80% used for training and 20% for testing. This approach has been tested on multiple machine learning classification models, achieving an accuracy of 99.04% with a Random Forest Classifier.

A Field-Programmable Gate Array-Based Adaptive Sleep Posture Analysis Accelerator for Real-Time Monitoring.

This research presents a sleep posture monitoring system designed to assist the elderly and patient attendees. Monitoring sleep posture in real time is challenging, and this approach introduces hardware-based edge computation methods. Initially, we detected the postures using minimally optimized sensing modules and fusion techniques. This was achieved based on subject (human) data at standard and adaptive levels using posture-learning processing elements (PEs). Intermittent posture evaluation was performed with respect to static and adaptive PEs. The final stage was accomplished using the learned subject posture data versus the real-time posture data using posture classification. An FPGA-based Hierarchical Binary Classifier (HBC) algorithm was developed to learn and evaluate sleep posture in real time. The IoT and display devices were used to communicate the monitored posture to attendant/support services. Posture learning and analysis were developed using customized, reconfigurable VLSI architectures for sensor fusion, control, and communication modules in static and adaptive scenarios. The proposed algorithms were coded in Verilog HDL, simulated, and synthesized using VIVADO 2017.3. A Zed Board-based field-programmable gate array (FPGA) Xilinx board was used for experimental validation.

Ergonomics in Facial Plastic and Reconstructive Surgery: A Clinical Evaluation.

Surgeons experience a risk for physical strain and injury secondary to physical demands in the workplace. To minimize injury and maximize career longevity, physicians should be aware of ergonomics pitfalls and postural correction methods. This study investigates ergonomic trends in Facial Plastic and Reconstructive Surgery (FPRS) clinic by quantifying surgeons' and trainees' cervicothoracic spine posture. Participants completed a 22-item questionnaire to evaluate current ergonomic practices. A lightweight device was calibrated and attached to the mid-scapular region of participants, providing real-time posture feedback. The percentage of time in upright posture was recorded during clinical and operative workdays. Upright posture was defined as neutral spine positioning with acceptable mild to moderate deviations. Two FPRS attending surgeons, 1 FPRS fellow, and 11 otolaryngology residents participated over 12 months. Discomfort was most commonly reported in the neck, shoulders, and upper back during clinic. Symptoms were self-treated by changing body position, wearing specialized footwear, adjusting height of the chair or examination table, or ignoring discomfort. Eighty-two percent were unaware of ergonomic guidelines or appropriate considerations. Time spent in upright posture was significantly higher in clinic (84.9%) than in the OR (53.5%) (p < 0.001). Upright posture declined after reaching 6 work hours (p = 0.029); no such patterns were observed in the OR (p = 0.946). Although time spent in upright posture was objectively poorer in the OR, these data suggest ergonomics are an important consideration in the outpatient setting, with surgeons experiencing discomfort during and after clinic. Further investigation is warranted to identify actionable changes and promote healthy ergonomics. N/A Laryngoscope, 2024.

Real-Time Forward Head Posture Detection and Correction System Utilizing an Inertial Measurement Unit Sensor

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications.

Real-Time Posture Identification System for Wheelchair Users Preventing the Generation of Pressure Ulcers

Real-Time Posture Identification System for Wheelchair Users Preventing the Generation of Pressure Ulcers

A lightweight attention-driven distillation model for human pose estimation

Currently, research on human pose estimation tasks primarily focuses on heatmap-based and regression-based methods. However, the increasing complexity of heatmap models and the low accuracy of regression methods are becoming significant barriers to the advancement of the field. In recent years, researchers have begun exploring new methods to transfer knowledge from heatmap models to regression models. Recognizing the limitations of existing approaches, our study introduces a novel distillation model that is both lightweight and precise. In the feature extraction phase, we design the Channel-Attention-Unit (CAU), which integrates group convolution with an attention mechanism to effectively reduce redundancy while maintaining model accuracy with a decreased parameter count. During distillation, we develop the attention loss function, LA, which enhances the model’s capacity to locate key points quickly and accurately, emulating the effect of additional transformer layers and boosting precision without the need for increased parameters or network depth. Specifically, on the CrowdPose test dataset, our model achieves 71.7% mAP with 4.3M parameters, 2.2 GFLOPs, and 51.3 FPS. Experimental results demonstrates the model’s strong capabilities in both accuracy and efficiency, making it a viable option for real-time posture estimation tasks in real-world environments.

Assessing CNN Architectures for Estimating Correct Posture in Cruise Machinists

Cruise machinists operate in dynamic and physically demanding environments where improper posture can lead to musculoskeletal disorders, adversely affecting their health and work efficiency. Current ergonomic assessments in such settings are often generic and not tailored to the unique challenges of maritime operations. This paper presents a novel application of artificial intelligence tools for real-time posture estimation specifically designed for cruise machinists. The primary aim is to enhance occupational health and safety by providing precise, real-time feedback on ergonomic practices. We developed a dataset by capturing video recordings of cruise machinists at work, which were processed to extract skeletal outlines using advanced computer vision techniques. This dataset was used to train deep neural networks, optimizing them for accuracy in diverse and constrained shipboard environments. The networks were tested across various computational platforms to ensure robustness and adaptability. The AI model demonstrated high efficacy in recognizing both correct and incorrect postures under real-world conditions aboard ships. The system significantly outperformed traditional ergonomic assessment tools in terms of speed, accuracy, and the ability to provide instant feedback. The findings suggest that AI-enhanced ergonomic assessments could be a transformative approach for occupational health across various industries.

Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps.

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.

Application of IMU Sensors for Real-Time Posture Monitoring and Musculoskeletal Disorder Prevention in Construction Workers

Application of IMU Sensors for Real-Time Posture Monitoring and Musculoskeletal Disorder Prevention in Construction Workers

Vision-Based Real-Time Posture Tracking for Multiple Construction Workers

Vision-Based Real-Time Posture Tracking for Multiple Construction Workers

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Noninvasive tracking devices are widely used to monitor real-time posture. Yet significant potential exists to enhance postural control quantification through walking videos. This study advances computational science by integrating OpenPose with a Support Vector Machine (SVM) to perform highly accurate and robust postural analysis, marking a substantial improvement over traditional methods which often rely on invasive sensors. Utilizing OpenPose-based deep learning, we generated Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images for 35 young adults in controlled walking experiments. Through Temporal and Spatial Regression (TSR) models, key features were extracted for SVM classification, enabling the distinction between various walking behaviors. This approach resulted in an overall accuracy of 0.990 and a Kappa index of 0.985. Cutting points for the ratio of top angles (TAR) and the ratio of bottom angles (BAR) effectively differentiated between left and right skews with AUC values of 0.772 and 0.775, respectively. These results demonstrate the efficacy of integrating OpenPose with SVM, providing more precise, real-time analysis without invasive sensors. Future work will focus on expanding this method to a broader demographic, including individuals with gait abnormalities, to validate its effectiveness across diverse clinical conditions. Furthermore, we plan to explore the integration of alternative machine learning models, such as deep neural networks, enhancing the system's robustness and adaptability for complex dynamic environments. This research opens new avenues for clinical applications, particularly in rehabilitation and sports science, promising to revolutionize noninvasive postural analysis.

Workout Classification Using a Convolutional Neural Network in Ensemble Learning.

To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizing MediaPipe, the proposed system extracts the joint coordinates and angles of the human body, which the CNN uses to learn the complex patterns of various exercises. Additionally, this new approach enhances classification performance by combining predictions from multiple image frames using an ensemble learning method. Infinity AI's Fitness Basic Dataset is employed for validation, and the experiments demonstrate high accuracy in classifying exercises such as arm raises, squats, and overhead presses. The proposed model demonstrated its ability to effectively classify exercise postures in real time, achieving high rates in accuracy (92.12%), precision (91.62%), recall (91.64%), and F1 score (91.58%). This indicates its potential application in personalized fitness recommendations and physical therapy services, showcasing the possibility for beneficial use in these fields.

Miner pose detection algorithm based on improved YOLOv7 in the coal mine

The unsafe behavior of workers in underground mines is a major cause of accidents. Recently, deep learning technology has become more and more widely used in the computer field and has achieved remarkable results, providing new opportunities for the research of miner behavior recognition algorithms. To address the current problem of large computation volume and low detection efficiency of multi-person posture detection in underground mines, we propose an improved algorithm that introduces the GSConv convolution module into the YOLOv7-Pose model and replaces the ELEN-W module with the GSELEN module to accelerate model convergence. We also introduce a group-convolution reconstruction SPPCSPC module to reduce parameter load and calculation amount and reduce the original 17 joint points to 14 joints to further realize a lightweight model. The experimental results showed that the improved YOLOv7-Pose model in this study decreased to 135.7 MB, by about 18.8%, while maintaining model accuracy. Therefore, this improvement can achieve a lightweight algorithm and improve real-time posture detection of construction workers in underground mines while ensuring model accuracy.

Modeling of cross-scale human activity for digital twin workshop

Digital Twin Workshop(DTW), as an important approach to digitalization and intelligentization of workshop, has gained significant attention in manufacturing industry. Currently, digital twin models for manufacturing resources have progressed from theoretical research to practical implementation. However, as a crucial component of workshop, modeling of human activity in workshop still faces challenges due to the autonomy and uncertainty of human beings. Therefore, we propose a comprehensive approach to the modeling cross-scale human activity in digital twin workshop, which comprises macro activity and micro activity. Macro activity contains human’s occupation and spatial positions in workshop, while micro activity refers to real-time posture and production actions at work. In this paper, we build and integrate macro activity digital twin model and micro activity digital twin model. With the combination of closed-loop interaction between virtual models and physical entities, we achieve semantic mapping and control of production activities, thereby facilitating practical management of human activity in workshop. Finally, we take certain factory’s manufacturing workshop as an example to introduce the application of the proposed approach.

HEARJACK - The Ultimate Posture Enhancement and Skin Health Jacket for Military Excellence

HEARJACK is an innovative smart jacket designed to enhance the health and well-being of military personnel. This groundbreaking project addresses the crucial need for maintaining proper posture, monitoring heart health, and managing humidity-related skin issues. Spinal alignment and posture play a fundamental role in overall well-being, and deviations from a healthy alignment can lead to various health issues. HEARJACK integrates sensors like the MPU6050 for real-time posture monitoring, DHT11 for humidity insights, and a heart rate sensor for stress assessment. These sensors, combined with an IoT algorithm, enable continuous monitoring and data analysis. The results demonstrate a significant improvement in posture, reduced skin-related health concerns, and enhanced stress management, underscoring the potential of IoT-driven wearables in military healthcare. Further research and development promise to revolutionize health and military technology

A Wearable Sonification System to Improve Movement Awareness: A Feasibility Study

This paper presents the design, development, and feasibility testing of a wearable sonification system for real-time posture monitoring and feedback. The system utilizes inexpensive motion sensors integrated into a compact, wearable package to measure body movements and standing balance continuously. The sensor data is processed through sonification algorithms to generate real-time auditory feedback cues indicating the user’s balance and posture. The system aims to improve movement awareness and physical conditioning, with potential applications in balance rehabilitation and physical therapy. Initial feasibility testing was conducted with a small group of healthy participants performing standing balance tasks with eyes open and closed. Results indicate that the real-time audio feedback improved participants’ ability to maintain balance, especially in the case of closed eyes. This preliminary study demonstrates the potential for wearable sonification systems to provide intuitive real-time feedback on posture and movement to improve motor skills and balance.

Enhancing Yoga Learning with a Low-Complexity, Mobilebased Deep Learning System for Real-Time Posture Identification and Correction

Enhancing Yoga Learning with a Low-Complexity, Mobilebased Deep Learning System for Real-Time Posture Identification and Correction