A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders

Evaluation of Convolutional Neural Network-Based Posture Identification Model of Older Adults: From Silhouette of Sagittal Photographs

Background/Objectives: Posture is a significant indicator of health status in older adults. This study aimed to develop an automatic posture assessment tool based on sagittal photographs by validating recognition models using convolutional neural networks. Methods: A total of 9140 images were collected with data augmentation, and each image was labeled as either Ideal or Non-Ideal posture by physical therapists. The hidden and output layers of the models remained unchanged, while the loss function and optimizer were varied to construct four different model configurations: mean squared error and Adam (MSE & Adam), mean squared error and stochastic gradient descent (MSE & SGD), binary cross-entropy and Adam (BCE & Adam), and binary cross-entropy and stochastic gradient descent (BCE & SGD). Results: All four models demonstrated an improved accuracy in both the training and validation phases. However, the two BCE models exhibited divergence in validation loss, suggesting overfitting. Conversely, the two MSE models showed stability during learning. Therefore, we focused on the MSE models and evaluated their reliability using sensitivity, specificity, and Prevalence-Adjusted Bias-Adjusted Kappa (PABAK) based on the model's output and correct label. Sensitivity and specificity were 85% and 84% for MSE & Adam and 67% and 77% for MSE & SGD, respectively. Moreover, PABAK values for agreement with the correct label were 0.69 and 0.43 for MSE & Adam and MSE & SGD, respectively. Conclusions: Our findings indicate that the MSE & Adam model, in particular, can serve as a useful tool for screening inspections.

The Ergonomic Evaluation of Attendings and Trainees Across the Vitreoretinal Service as Measured by a Wearable Device

BACKGROUND AND OBJECTIVE

A cross-sectional prospective study to examine ergonomic differences in vitreoretinal settings: surgery, clinic, and dedicated procedure clinic.

PATIENTS AND METHODS

Three vitreoretinal surgeons, three fellows, and one resident at a tertiary eye care facility. Participants wore an Upright Go 2 posture device and posture was recorded in each setting between July 1 to August 31, 2023.

RESULTS

Time in upright and poor postures was tracked. Significant differences were found in postural score for attendings between work settings (P < 0.01). Trainees showed no significant difference between settings. Poor posture in surgery was linked to microscope use and scleral buckle placement; in the clinic, it was associated with pan-retinal photocoagulation and injection minutes; in procedure clinic, it was ophthalmologist-dependent and those performing injections.

CONCLUSIONS

Ergonomic considerations are crucial in vitreoretinal practice. Attendings and trainees should focus on posture in surgery and clinic settings to enhance career longevity. [Ophthalmic Surg Lasers Imaging Retina 2025;56:80-85.].

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning

BACKGROUND

Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations.

METHODS

In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer.

RESULTS

After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878.

CONCLUSIONS

We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

A Smart Chair to Monitor Sitting Posture by Capacitive Textile Sensors

In this paper, a smart office chair with movable textile sensors to monitor sitting position during the workday is presented. The system consists of a presence textile capacitive sensor with different levels of activation with a signal conditioning device. The proposed system was integrated into an office chair to detect postures that could provoke musculoskeletal disorders or discomfort. The microcontroller measured the capacitance by means of a cycle count method and provided the position information in real time. The information could be analysed to set up warnings to prevent incorrect postures or the necessity to move. Five participants assumed a series of postures, and the results showed the workability of the proposed smart chair. The chair can be provided as a new tool for companies, hospitals, or other institutions to detect incorrect postures and monitor the postures of people with reduced mobility. This tool can optimise control procedures or prevent occupational risks.

Posture monitoring in healthcare: a systematic mapping study and taxonomy

Palliative treatments for back pain usually include exercise, analgesics, physiotherapy, prostheses, and surgery in severe cases. Technologies for postural monitoring are growing, and they are important in preventing back pain and mitigating permanent damage. Remote work, especially after the COVID-19 pandemic, made people spend more time than usual in chairs and environments not certified by the health aspects of work. This research investigated through a Systematic Mapping Study (SMS) contributions in posture monitoring for healthcare in smart environments, including the different methods to obtain the posture, the limitations, and the target audience of the proposed models. The SMS was conducted in eight databases, including articles from January 2012 to March 2022. The initial search yielded 3161 articles, of which 34 were selected after applying the filtering criteria. Moreover, this study presents the challenges related to posture behavior monitoring, identifying studies and implementations that apply assistive technology for postural monitoring and improving the health and life of remote workers. In addition, three commercial postural devices are presented, and what challenges they currently face. Regarding healthcare, results showed a prevalence of using the Internet of Things (IoT) devices such as wireless sensor networks and inertial measurement unit (IMU) sensors. This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis.

A Work-Related Musculoskeletal Disorders (WMSDs) Risk-Assessment System Using a Single-View Pose Estimation Model

Musculoskeletal disorders are an unavoidable occupational health problem. In particular, workers who perform repetitive tasks onsite in the manufacturing industry suffer from musculoskeletal problems. In this paper, we propose a system that evaluates the posture of workers in the manufacturing industry with single-view 3D human pose-estimation that can estimate the posture in 3D using an RGB camera that can easily acquire the posture of a worker in a complex workplace. The proposed system builds a Duckyang-Auto Worker Health Safety Environment (DyWHSE), a manufacturing-industry-specific dataset, to estimate the wrist pose evaluated by the Rapid Limb Upper Assessment (RULA). Additionally, we evaluate the quality of the built DyWHSE dataset using the Human3.6M dataset, and the applicability of the proposed system is verified by comparing it with the evaluation results of the experts. The proposed system provides quantitative assessment guidance for working posture risk assessment, assisting the continuous posture assessment of workers.