Posture Analysis Research Articles

Occupational Health Challenges Faced by Farm Women during Brinjal Harvesting in Telangana, India

Aim: The study aimed to assess the drudgery experienced by women farmers during brinjal harvesting, focusing on musculoskeletal challenges, postural risks, and occupational health hazards. It sought to evaluate the ergonomic risks involved and suggest interventions to reduce physical strain. Study Design: The study employed an explanatory research design to analyze the ergonomic challenges and health hazards faced by women during brinjal harvesting. Place and Duration of Study: The study was conducted in Kaveliguda village, Shamshabad Mandal, Rangareddy District, Telangana, during the brinjal harvesting season in 2023. Methodology: Thirty farm women engaged in brinjal harvesting were purposively selected as respondents. Data were collected using structured schedules, including self-reported health symptoms, and evaluated using the Rapid Upper Limb Assessment (RULA) for postural analysis. Quantitative data were analyzed through frequency distributions and percentages. Results: The study revealed significant musculoskeletal issues, including pain, stiffness, and cramps in the back, neck, legs, and arms. RULA scores indicated high ergonomic risks, necessitating immediate corrective actions. Minimal use of personal protective equipment (PPE) exposed workers to various occupational hazards. Conclusion: The study concluded that ergonomic interventions, such as improved tools and increased PPE usage, were essential to reducing drudgery, enhancing productivity, and improving the well-being of women farmers.

Construction of tennis pose estimation and action recognition model based on improved ST-GCN

With the rapid growth of computer vision and deep learning technologies, the application of pose estimation and action recognition in sports training has become increasingly widespread. Due to factors such as complex movements, fast speed, and limb occlusion, pose estimation and action recognition in tennis face significant challenges. Therefore, this study first introduces selective dropout and pyramid region of interest pooling layer strategies in fast region convolutional neural networks. Secondly, a pose estimation algorithm based on multi-scale fusion pose residual network 50 is designed, and finally a spatiotemporal graph convolutional network model is constructed by fusing channel attention module and multi-scale dilated convolution module. The data showed that the average detection accuracy of the improved attitude residual network 50 was 70.4%, and the accuracy of object detection for small, medium, and large objects was 57.4%, 69.3%, and 79.2%, respectively. The continuous action recognition accuracy and inter action fluency detection time of the improved spatiotemporal graph convolutional network were 93.8% and 19.2 ms, respectively. When the sample size was 1000, its memory usage was 1378 MB and the running time was 32.7 ms. Experiments have shown that the improved model achieves high accuracy and robustness in tennis action recognition tasks, especially in complex scenes and limb occlusion conditions, where the model shows significant advantages. This study aims to provide an efficient and accurate motion recognition technology for tennis posture analysis and intelligent training.

Evaluation of Athletes' Gender-Related Postural Differences.

Postural differences have been evident between female and male athletes, but there is insufficient information on how these differences are characterized in different sports branches. Our aim in this study was to compare the static postures of female and male athletes paired into identical sports. We included 172 athlete participants (86 female and 86 males) who were paired by same chronological age, sporting age, and sports branch. We performed posture analyses of these athletes, using the DIERS formetric 4D device. Specifically, we evaluated and recorded their trunk length, sacrum distance, sagittal imbalance, coronal imbalance, pelvic obliquity, pelvic torsion, pelvic inclinations, pelvic rotation, kyphotic angle, lordotic angle, vertebral rotation, and trunk torsion. We found comparable sagittal imbalance, coronal imbalance, pelvic rotation, vertebral rotation, and trunk torsion similar in females and males (p > .05), but females had a wider sacrum, their pelvic structures were more oblique and anteriorly tilted, and they had more pelvic inclination angles and bigger kyphosis and lordosis angles. Male athletes had longer trunk lengths and more pelvic torsion. The kyphosis and lordosis angles of the female athletes were bigger than those of male athletes, but they were still within normal limits. The lumbar lordosis angles of male athletes were below normal limits. In conclusion, female athletes exhibited a more balanced spinal posture than male athletes.

Analysis of anticipatory and compensatory postural adjustment in women of different age groups using surface electromyography

Postural balance is crucial for daily activities, relying on the coordination of sensory systems. Balance impairment, common in the elderly, is a leading cause of mortality in this population. To analyze balance, methods like postural adjustment analysis using electromyography (EMG) have been developed. With age, women tend to experience reduced mobility and greater muscle loss compared to men. However, few studies have focused on postural adjustments in women of different ages using EMG of the lower limbs during laterolateral and anteroposterior movements. This gap could reveal a decrease in muscle activation time with aging, as activation time is vital for postural adjustments. This study aimed to analyze muscle activation times in women of different ages during postural adjustments. Thirty women were divided into two groups: young and older women. A controlled biaxial force platform was used for static and dynamic balance tests while recording lower limb muscle activity using EMG. Data analysis focused on identifying muscle activation points and analyzing postural adjustment times. Results showed significant differences in muscle activation times between the two groups across various muscles and platform tilt conditions. Younger women had longer muscle activation times than older women, particularly during laterolateral platform inclinations. In anteroposterior movements, older women exhibited longer activation times compared to their laterolateral performance, with fewer differences between the groups. Overall, older women had shorter muscle activation times than younger women, suggesting a potential indicator of imbalance and increased fall risk.

Highly durable MXene-based pressure sensor enabled by homogeneous interfacial locking

Pressure sensors with linear high sensitivity and a broad response range enable the capture of subtle pressure changes across various scenarios, making them essential for accurate detection. Conventional methods typically employ porous and layered designs to boost sensitivity but they can compromise stability due to mechanical mismatches and interface incompatibilities between diverse materials. To address these issues, we propose a homogeneous interfacial locking strategy that simultaneously improves the linear response range and stability of the sensor. This approach uses aqueous polyurethane (WPU) as the interface-locking material, which matches the material properties of the substrate polyurethane (PU) sponge. This compatibility anchors the deposited MXene nanosheets to the sponge framework, establishing a robust mechanical and electrical interface. Additionally, the partial filling of the sponge’s pores with WPU enhances its mechanical compressibility. As a result, the sensor achieves high sensitivity (6226 kPa−1), a wide detection range (up to 146 kPa) and excellent durability (over 100,000 cycles), making it suitable for long-term human posture monitoring and analysis. This strategy offers a promising pathway for developing pressure sensors with high sensing performance and improved stability.

The biomechanics of public speaking: Enhancing political communication and persuasion through posture and gesture analysis

Effective public speaking is critical in political communication, where non-verbal cues such as posture, gestures, facial expressions, and body movements significantly influence audience perception. Despite the known impact of these biomechanics, research in diverse cultural contexts remains limited. This study addresses this gap by examining the biomechanics of public speaking in the Philippines, focusing on 16 political speakers and 211 audience participants from varied demographic backgrounds. The study uses advanced motion capture technology, high-definition video recordings, and audience perception surveys to investigate how specific non-verbal elements affect audience engagement and perception. Challenges include accurately measuring and isolating the effects of these biomechanical factors amidst varied cultural and linguistic influences. The findings reveal that an upright posture significantly enhances perceived confidence (r = 0.72, p = 0.003) and trustworthiness (r = 0.65, p = 0.004), while high-frequency gestures positively correlate with clarity (β = 0.47, p = 0.008) and persuasiveness (β = 0.66, p = 0.003). Head movements, such as nodding, significantly increase audience engagement (F-value = 5.73, p = 0.002), and high-intensity smiling enhances emotional responses (t-value = 4.86, p = 0.001). These results underscore the importance of biomechanics in political communication, demonstrating that specific gestures and postures are critical in conveying confidence and persuasiveness. The study contributes to the field by offering insights into the strategic use of body language, particularly in multicultural settings, to enhance audience engagement and speaker effectiveness.

Decision Support System (DSS) for Improving Production Ergonomics in the Construction Sector

Ergonomics is essential to improving workplace safety and efficiency by reducing the risks associated with physical tasks. This study presents a decision support system (DSS) aimed at enhancing production ergonomics in the construction sector through an analysis of high-risk postures. Using the Ovako Work Posture Analysis System (OWAS), the Revised NIOSH Lifting Equation (NIOSH equation) and Rapid Entire Body Assessment (REBA), the DSS identifies ergonomic risks by assessing body postures across common construction tasks. Three specific postures—X, Y and Z—were selected to represent typical construction activities, including lifting, squatting and repetitive tool use. Posture X, involving a forward-leaning stance with arms above the shoulders and a 25 kg load, was identified as critical, yielding the highest OWAS and NIOSH values, thus indicating an immediate need for corrective action to mitigate risks of musculoskeletal injuries. The DSS provides recommendations for workplace adjustments and posture improvements, demonstrating a robust framework that can be adapted to other postures and industries. Future developments may include application to other postures and sectors, as well as the use of artificial intelligence to support ongoing ergonomic assessments, offering a promising solution to enhance Occupational Safety and Health policies.

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.

ArmSpy++: Enhanced PIN Inference through Video-based Fine-grained Arm Posture Analysis

As one of the most common ways for user authentication, Personal Identification Number (PIN), due to its simplicity and convenience, has suffered from plenty of side-channel attacks, which pose a severe threat to people’s privacy and property. The success of existing attacks is usually built upon the premise of no occlusion between the attacker and the victim’s hand gesture, but it increases the difficulty of launching the attack and the possibility of exposure. To overcome such limitation, we propose ArmSpy++, an improved video-assisted PIN inference attack built upon our previous research, ArmSpy. Specifically, ArmSpy++ employs new modules to leverage more features like the keystroke-induced elbow bending, wrist speed variation, and the spatial relationship between different arm joints, to correctly detect Keystrokes. ArmSpy++ delves into the perspective relationship and natural typing habits to ensure a high success rate of PIN inference. We also re-designed the inferred PIN pattern coordination mechanism to accurately deduce the PINs. By using a pre-trained HigherHRNet model for posture estimation ArmSpy++ eliminates the necessity of additional training. The extensive experiments demonstrate that ArmSpy++ can achieve over 83.1% average accuracy with 3 attempts and even 92.5% for some victims, indicating the severity of the threat posed by ArmSpy++.

Analyze the physical interaction between the user and the furniture design to optimize comfort and functionality

The physical interaction between users and furniture is pivotal in determining comfort and functionality, particularly in environments where individuals spend extended periods, such as offices, homes, and public spaces. This study aims to analyze how different furniture designs impact user comfort, postural stability, and long-term usability. By employing a hybrid research framework combining observational studies, simulations, and advanced technological tools such as motion tracking, pressure mapping, and biomechanical sensors, the research provides a comprehensive evaluation of user-furniture interaction. The study involved 178 participants with diverse demographic backgrounds, allowing for a broad range of body types, ages, and activity levels to be examined. Key findings indicate that ergonomic features such as adjustable seat height, lumbar support, and reclining mechanisms significantly enhance comfort, particularly when customized to the user’s anthropometric profile. For example, adjustable seat height reduced pressure on the thighs, improving comfort by 8.5% over prolonged periods. Additionally, lumbar support was the most compelling feature in alleviating muscle strain, improving overall comfort by 9.0%. The analysis of long-term comfort revealed that postures supporting dynamic movements, such as using a standing desk, maintained higher comfort levels over time compared to static postures like leaning forward, which showed a marked increase in muscle fatigue. Postural stability analysis showed that sitting at a 90° angle provided the best balance of stability and long-term comfort, with a usability rating 8.4. In contrast, leaning forward exhibited the lowest postural stability and the highest discomfort, making it unsuitable for prolonged tasks.

A Wushu Leg Gesture Recognition Algorithm Based on Random Forest and Bone Feature Extraction (RF-SFE)

Addressing the challenges of complex movement trajectories and rapid action changes in martial arts performances, this study introduces a novel posture recognition algorithm based on Random Forest and Skeletal Feature Extraction (RF-SFE) for martial arts leg movements. Unlike traditional posture recognition methods that struggle with accuracy, RF-SFE aims to provide intelligent analysis of training postures to assist practitioners in efficient training. The algorithm initially employs advanced skeletal feature extraction techniques to identify and articulate the relative positions and movements specific to martial arts. These extracted spatial features enhance the flexibility in modeling the unique dynamics of martial arts. Subsequently, Random Forest classification is utilized to categorize different leg movements, leveraging its strength in handling high-dimensional data and providing robust classification. Comparative experiments on diverse martial arts posture datasets demonstrate a significant improvement in recognition rates over baseline methods. This validates the effectiveness of the RF-SFE method in recognizing martial arts postures, offering scientific guidance for practitioners’ training regimens.

Biomechanical factors influencing mental health in college students and the role of physical activity in cognitive resilience

The Mental Health (MH) of college students is increasingly becoming a public health concern, with rising rates of depression, anxiety, and stress. This study aims to explore the relationship between Biomechanical Factors (BF), Physical Activity (PA), and MH outcomes in college students, addressing gaps in current research that frequently overlook the biomechanical features of physical well-being. A cross-sectional observational design was employed, involving 200 college students aged 18–24. SP underwent comprehensive assessments, including postural analysis, movement pattern evaluation, and MH screening. Physical activity levels and cognitive resilience were also measured to evaluate their roles in mediating and moderating the relationships between BF and MH. Key findings revealed that poor biomechanical alignment, such as forward head posture and movement asymmetry, were significantly associated with higher levels of depression, anxiety, and stress. Correlation analysis showed that forward head posture correlated positively with depression (r = 0.52, p < 0.01) and anxiety (r = 0.47, p < 0.01). Movement asymmetry was also associated with MH disturbances (depression: r = 0.45, p < 0.01). PA mediated the relationship between BF and MH, with significant indirect effects via PA for forward head posture (0.18, p < 0.05). Cognitive resilience emerged as a significant moderator, buffering the negative impact of biomechanical inefficiencies on MH outcomes. Within-subject comparisons indicated improvements in BF and MH scores over a one-month follow-up, with decreases in forward head posture (−2.2 ± 1.5 degrees, p < 0.05) and depression scores (−1.5 ± 1.2, p < 0.01).

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.

A study on sleep posture analysis using fibre bragg grating arrays based mattress

Prolonged sleeping postures or unusual postures can lead to the development of various ailments such as subacromial impingement syndrome, sleep paralysis in the elderly, nocturnal gastroesophageal reflux, sore development, etc Fibre Bragg Gratings (a variety of optical sensors) have gained huge popularity due to their small size, higher sensitivity and responsivity, and encapsulation flexibilities. However, in the present study, FBG Arrays (two FBGs with 10 mm space between them) are employed as they are advantageous in terms of data collection, mitigating sensor location effects, and multiplexing features. In this work, Liquid silicone encapsulated FBG arrays are placed in the head (E), shoulder (C, D), and lower half body (A, B) region for analyzing the strain patterns generated by different sleeping postures namely, Supine (P1), Left Fetus (P2), Right Fetus (P3), and Over stomach (P4). These strain patterns were analyzed in two ways, combined (averaging the data from each FBG of the array) and Individual (data from each FBG was analyzed separately). Both analyses suggested that the FBGs in the arrays responded swiftly to the strain changes that occurred due to changes in sleeping postures. 3D histograms were utilized to track the strain changes and analyze different sleeping postures. A discussion regarding closely related postures and long hour monitoring has also been included. Arrays in the lower half (A, B) and shoulder (C, D) regions proved to be pivotal in discriminating body postures. The average standard deviation of strain for the different arrays was in the range of 0.1 to 0.19 suggesting the reliable and appreciable strain-handling capabilities of the Liquid silicone encapsulated arrays.

Effects of Physical Activity Level and Sedentary Behavior on the Musculoskeletal System in Young Adults

Aim: The aim of this study is to investigate the relationship between the level of physical activity and sedentary behavior in young adults aged 18-35 and to evaluate the effects of this relationship on musculoskeletal pain, posture, muscle shortness, and trunk muscle endurance. Methods: In this cross-sectional descriptive study, conducted at the Faculty of Health Sciences of a university between March 2018 and August 2018, 219 students were included. Data were obtained by using the case report form which determines the demographic characteristics and sedentary behavior of the students, The International Physical Activity Index - short form, posture analysis form, muscle shortness evaluation form, and trunk muscle endurance evaluation form. Results: In the study, 66.2% of patients were female and 33.8% were male. In the evaluation of physical activity level, the ratio of highly active individuals in males, and inactive and insufficiently active individuals in females was significantly higher (p <0.05). Musculoskeletal pain was significantly higher in inactive and insufficiently active individuals (p<0.001). A negative correlation existed between physical activity level and pain frequency, duration, and scores (p<0.001). There was a positive correlation between trunk endurance tests and physical activity and exercise participation. Non-exercisers showed shortening of lumbar extensor muscles and gastrosoleus, with increased thoracic kyphosis (p<0.05). Daily sitting time correlated positively with elbow, wrist, and knee pain, and lumbar lordosis (p<0.05). Those spending more time at the computer experienced higher levels of head-neck and elbow pain, pain duration, muscle shortness, thoracic kyphosis, shoulder protraction, and anterior head tilt (p<0.05). Conclusion: In young adults, increasing physical activity and reducing daily sitting time are necessary to prevent musculoskeletal pain and reduce postural deformities. Keywords: Musculoskeletal diseases, musculoskeletal pain, physical exertion, posture, sedentary behavior, trunk muscular endurance, young adult.

Fusing YOLOv5s-MediaPipe-HRV to classify engagement in E-learning: From the perspective of external observations and internal factors

The rapid advancements in computer vision technology present significant potential for the automatic recognition of learner engagement in E-learning. We conducted a two-stage experiment to assess learner engagement based on behavioural (external observations) and physiological (internal factors) cues. Using computer vision technology and wearable sensors, we extracted three feature sets: action, head posture and heart rate variability (HRV). Subsequently, we integrated our constructed YOLOv5s–MediaPipe behaviour detection model with a physiological detection model based on HRV to comprehensively evaluate learners’ behavioural, affective and cognitive engagement. Additionally, we developed a method and criteria for assessing distraction based on behaviour, ultimately creating a comprehensive, efficient, low-cost and easy-to-use system for the automatic recognition of learner engagement. Experimental results showed that our improved YOLOv5s model achieved a mean average precision of 92.2 %, while halving both the number of parameters and model size. Unlike other deep learning-based methods, using MediaPipe–OpenCV for head posture analysis offers advantages in real-time performance, making it lightweight and easy to deploy. Our proposed long short-term memory classifier, based on sensitive HRV metrics and their normalisation, demonstrated satisfactory performance on the test set, with an accuracy = 80 %, precision = 81 %, recall = 80 % and an F1 score = 80 %.

Establishing the Reliability of the GaitON® Motion Analysis System: A Foundational Study for Gait and Posture Analysis in a Healthy Population.

Gait and posture analysis plays a crucial role in understanding human movement, with significant applications in rehabilitation, sports science, and clinical settings. The GaitON® system, a 2D motion analysis tool, provides an accessible and cost-effective method for assessing gait and posture. However, its reliability in clinical practice, particularly for intra-rater consistency, remains to be evaluated. This study aims to assess the intra-rater reliability of the GaitON® system in a healthy population, focusing on gait and posture parameters. A total of 20 healthy participants (10 males and 10 females) aged 18 to 50 years were recruited for the study. Each participant underwent gait and posture assessments using the GaitON® system on two separate occasions, spaced one week apart. Video recordings from anterior and posterior views were used to analyze gait, while images from anterior, posterior, and lateral views were captured to assess posture with markers placed on key anatomical landmarks. The reliability of the measurements was analyzed using intraclass correlation coefficients (ICC), a standard error of measurement (SEM), and the smallest detectable difference (SDD) method. The GaitON® system demonstrated excellent intra-rater reliability across a wide range of gait and posture parameters. ICC values for gait parameters, including hip, knee, and ankle joint angles, ranged from 0.90 to 0.979, indicating strong consistency in repeated measurements. Similarly, ICC values for posture parameters, such as the head alignment, shoulder position, and ASIS alignment, were above 0.90, reflecting excellent reliability. SEM values were low across all parameters, with the smallest SEM recorded for the hip joint angle (0.37°), and SDD values further confirmed the precision of the system. The GaitON® system provides reliable and consistent measurements for both gait and posture analysis in healthy individuals. Its high intra-rater reliability and low measurement error make it a promising tool for clinical and sports applications. Further research is needed to validate its use in clinical populations and compare its performance to more complex 3D motion analysis systems.

The Posture Detection Method of Caged Chickens Based on Computer Vision.

At present, raising caged chickens is a common farming method in China. However, monitoring the status of caged chickens is still done by human labor, which is time-consuming and laborious. This paper proposed a posture detection method for caged chickens based on computer vision, which can automatically identify the standing and lying posture of chickens in a cage. For this aim, an image correction method was used to rotate the image and make the feeding trough horizontal in the image. The variance method and the speeded-up robust features method were proposed to identify the feeding trough and indirectly obtain the key area through the feeding trough position. In this paper, a depth camera was used to generate three-dimensional information so that it could extract the chickens from the image of the key area. After some constraint conditions, the chickens' postures were screened. The experimental results show that the algorithm can achieve 97.80% precision and 80.18% recall (IoU > 0.5) for white chickens and can achieve 79.52% precision and 81.07% recall (IoU > 0.5) for jute chickens (yellow and black feathers). It runs at ten frames per second on an i5-8500 CPU. Overall, the results indicated that this study provides a non-invasive method for the analysis of posture in caged chickens, which may be helpful for future research on poultry.

Chest Compression Evaluation based on Pose Estimation

Correct and prompt performance of cardiopulmonary resuscitation yields improvements in mortality and social return rates. Chest compression, a vital cardiopulmonary resuscitation technique, requires regular re-education for skill maintenance. Training with a manikin is feasible for chest compression, but assessing proficiencies without an expert presents challenges. This study aims to facilitate autonomous chest compression training even without expert supervision based on pose estimation. Twenty subjects were recruited for the training and successive performance evaluation of chest compression on a sensor-equipped training manikin, and the corresponding videos were recorded simultaneously. A system was developed to analyze chest compression movements through pose estimation on recorded videos for evaluating interruption presence, compression count, compression tempo, compression depth, and compression recoil. Through comparing three pose estimation models, OpenPose demonstrated the best performance, achieving accuracy rates of 67.08%, 56.67%, 61.25%, 39.17%, and 33.75% for the detection of interruption presence, compression count, appropriate tempo count, appropriate depth count, and appropriate recoil count, respectively. Additionally, posture analysis during compression, unattainable with the sensor-equipped manikin, revealed effectiveness in shooting at a position shifted 45 degrees from the front. The proposed method may serve as a tool for completely automated CPR chest compression training, anticipating an increase in citizens proficient in cardiopulmonary resuscitation.

INVESTIGATION OF THE RELATIONSHIP BETWEEN WORKING POSTURES OF PHYSIOTHERAPISTS WORKING IN SPECIAL EDUCATION AND REHABILITATION CENTERS AND HOSPITALS WITH PAIN, BODY AWARENESS LEVEL, CHRONIC FATIGUE AND DEPRESSION

This study aims to examine the relationship between physiotherapists' working posture and pain, body awareness level, chronic fatigue, and depression levels and to determine whether these parameters depend on the institution where they work. 93 randomly selected physiotherapists between the ages of 24-52 were divided into two groups according to their institutions: Rehabilitation centers/hospitals. Working posture was evaluated with the Ovako Working Posture Analysing System (OWAS), pain was evaluated with the Extended Nordic Musculoskeletal Questionnaire (NMQ-E) and Visual Analogue Scale (VAS), body awareness level was evaluated with the Body Awareness Questionnaire (BAQ), chronic fatigue was evaluated with the Chronic Fatigue Syndrome/Self Assessment Form (CFS), depression level was evaluated with the Beck Depression Inventory (BDI). A relationship was found between OWAS-leg posture and NMQ-E-ankle pain (r=-0.238), between OWAS-back posture-arm posture-action class and VAS (r1=0.25; r2=-0.27; r3=0.23), and between OWAS-back posture-action class and BDI (r1=0.25; r2=0.26) (p