Posture Estimation Research Articles

Analysis of Cushioned Landing Strategies of Cats Based on Posture Estimation

This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking it using animal posture estimation, we derive the cushioning strategy that cats use to handle landing impacts. The results indicate that the strategy effectively transforms high-intensity impacts into prolonged low-intensity impacts, thereby safeguarding the brain and internal organs. We adapt this cushioning strategy for robotic platforms through reasonable assumptions and simplifications. Simulations are conducted in both gravitational and zero gravity environments, demonstrating that the optimized strategy not only reduces ground impact and prolongs the cushioning duration but also effectively suppresses the robot’s rebound. In zero gravity, the strategy enhances stable attachment to target surfaces. This research introduces a novel biomimetic control strategy for landing control in the on-orbit operations of space robots.

PCNet: a human pose compensation network based on incremental learning for sports actions estimation

Human pose estimation has a wide range of applications. Existing methods perform well in conventional domains, but there are certain defects when they are applied to sports activities. The first is lack of estimation of the extremity posture, making it impossible to comprehensively evaluate the movement posture; the second is insufficient occlusion handling. Therefore, we propose a human pose compensation network based on incremental learning, which obtains shared weights to extract detailed features under the premise of limited extremity training data. We propose a higher-order feature compensator (HOF-compensator) to embed the attributes of the extremity into the torso and limbs topology structure, building a complete higher-order feature. In addition, to improve the occlusion handling performance, we propose an occlusion feature enhancement attention mechanism (OFE-attention) that can identify occluded keypoints and enhance attention to occlusion areas. We design comparative experiments on three public datasets and a self-built sports dataset, achieving the highest mean accuracy among all comparative methods. In addition, we design a series of ablation analysis and visualization displays to verify that our method performs best in sports pose estimation.

Retraction Note: Dynamic light collection system based on human posture estimation application in martial arts action teaching simulation

Retraction Note: Dynamic light collection system based on human posture estimation application in martial arts action teaching simulation

Posture Estimation Model Combined With Machine Learning Estimates the Radial Abduction Angle of the Thumb With High Accuracy.

The thumb function is complex, and accurate evaluation through images or videos is difficult. Pose estimation, a technology that uses artificial intelligence (AI) to estimate skeletal detection of the body, is gaining popularity.In this study, we combined the pose estimation library MediaPipe-Hands and five machine learning (ML) models to predict the radial abduction angle of the thumb.Radial abduction movements of 20 hands from 10 healthy volunteers were captured on video and processed into 5,000 images. Angle measurements by goniometer were used as true values to evaluate the angle reliability of the MediaPipe-Hands and the angle reliability of the MediaPipe-Hands combined with ML. The correlation coefficient (CC) between the angle measured by goniometry and the angle calculated by MediaPipe-Hands was 0.84. In contrast, applying ML to MediaPipe-Hands resulted in models with improved accuracy, and all models showed high CCs (0.94-099) with angle measurements taken by goniometry. The ML model also predicted the abduction angles when the camera was taken from three different angles. In visualizing the features that the AI deemed important, the ML model predicted the abduction angle by focusing on the tip distance between the thumb and index finger along with the angle of the metacarpophalangeal joint between the thumb and middle finger. These results enable angle estimation even without frontal imaging with a camera, and expansion of this system may lead to real-time functional assessment in telemedicine and rehabilitation without the need for physical contact.

초등학교 체육수업에서 인공지능 자세추정 앱을 적용한 교육적 효과 탐색 - 네트형 경쟁 활동 배구 언더핸드 동작을 중심으로

Purpose: The purpose of this study is to explore the educational effects and assess the improvement of movements by applying an AI-based app that estimates posture and provides feedback on underhand volleyball skills to elementary school students. Methods: The research participants consisted of 177 students (age: 12 years old, height: 152.8±7.01 cm, weight: 46.72±11.2 kg, BMI: 19.90±3.68 kg/㎡) attending Elementary School located in A City. First, the volleyball underhand pass motion was analyzed to confirm the order and method for teaching the correct posture, and based on this, feedback to be used in the app was constructed. Next, through previous research on the types and operating principles of artificial intelligence posture estimation models Learned how to create an app to run on a web page. Based on this, a plan was established and an app to be used in class was developed directly. Results: The results that the experimental group showed an increase of approximately 7 minutes in practice time, whereas the comparison group showed a decrease of about 6 minutes (F = 20.873, p < .001). In terms of students' perceptions and degree of change after the app-based class, the observability domain increased after the app-based class (F = 49.216, p < .001). The testability domain also increased after the app-based class (F = 71.017, p < .001). Conclusion: Through the use of an AI posture estimation app in net-type competitive activities, students were able to perform the correct underhand volleyball technique, and their learning effects during the lessons also improved.

Deep Learning-Based Classification of Powerlifting Movements Using Mediapipe

The analysis of sports movements is of great importance for optimizing sports performance, minimizing injury risks, and ensuring that athletes work with correct techniques. Powerlifting is a power sport consisting of fundamental movements such as bench press, deadlift, and squat. These movements are inherently complex and challenging to execute. Therefore, it is of great importance to perform these movements with the correct technique and safely. The aim of this study was to classify these movements using deep learning methods to ensure that the basic movements in powerlifting sports (bench press, deadlift, and squat) are applied with correct techniques and to minimize the risk of injury. In this study, feature extraction was performed on powerlifting movements using the deep learning-based SqueezeNet model, followed by classification using machine learning methods. The dataset was compiled from 876 images of bench press, deadlift, and squat movements sourced from various online platforms. Additionally, the dataset was expanded through data augmentation techniques, and key points of posture estimation were added to the images using the Mediapipe library. The obtained datasets were classified using Neural Network, Logistic Regression, Support Vector Machine and Random Forest algorithms and model performances were evaluated using various metrics. The findings revealed that the Neural Network model demonstrated superior performance, achieving the highest accuracy (0.989). Additionally, the integration of pose estimation and data augmentation techniques significantly enhanced classification accuracy and overall model performance. The findings of this study show that deep learning methods are powerful tools in sports movement analysis and can make significant contributions to the evaluation of athletes' performance.

Early gait analysis after total knee arthroplasty based on artificial intelligence dynamic image recognition

To explore early postoperative gait characteristics and clinical outcomes after total knee arthroplasty (TKA). From February 2023 to July 2023, 26 patients with unilateral knee osteoarthritis (KOA) were treated with TKA, including 4 males and 22 females, aged from 57 to 85 years old with an average of (67.58±6.49) years old;body mass index (BMI) ranged from 18.83 to 38.28 kg·m-2 with an average of (26.43±4.15) kg·m-2;14 patients on the left side, 12 patients on the right side;according to Kellgren-Lawrence(K-L) classification, 6 patients with grade Ⅲ and 20 patients with grade IV;the courses of disease ranged from 1 to 14 years with an average of (5.54±3.29) years. Images and videos of standing up and walking, walking side shot, squatting and supine kneeling were taken with smart phones before operation and 6 weeks after operation. The human posture estimation framework OpenPose were used to analyze stride frequency, step length, step length, step speed, active knee knee bending angle, stride length, double support phase time, as well as maximum hip flexion angle and maximum knee bending angle on squatting position. Western Ontario and McMaster Universities (WOMAC) arthritis index and Knee Society Score (KSS) were used to evaluate clinical efficacy of knee joint. All patients were followed up for 5 to 7 weeks with an average of (6.00±0.57) weeks. The total score of WOMAC decreased from (64.85±11.54) before operation to (45.81±7.91) at 6 weeks after operation (P<0.001). The total KSS was increased from (101.19±9.58) before operation to (125.50±10.32) at 6 weeks after operation (P<0.001). The gait speed, stride frequency and stride length of the affected side before operation were (0.32±0.10) m·s-1, (96.35±24.18) steps·min-1, (0.72±0.14) m, respectively;and increased to (0.48±0.11) m·s-1, (104.20±22.53) steps·min-1, (0.79±0.10) m at 6 weeks after operation (P<0.05). The lower limb support time and active knee bending angle decreased from (0.31±0.38) s and (125.21±11.64) ° before operation to (0.11±0.04) s and (120.01±13.35) ° at 6 weeks after operation (P<0.05). Eleven patients could able to complete squat before operation, 13 patients could able to complete at 6 weeks after operation, and 9 patients could able to complete both before operation and 6 weeks after operation. In 9 patients, the maximum bending angle of crouching position was increased from 76.29° to 124.11° before operation to 91.35° to 134.12° at 6 weeks after operation, and the maximum bending angle of hip was increased from 103.70° to 147.25° before operation to 118.61° to 149.48° at 6 weeks after operation. Gait analysis technology based on artificial intelligence image recognition is a safe and effective method to quantitatively identify the changes of patients' gait. Knee pain of KOA was relieved and the function was improved, the supporting ability of the affected limb was improved after TKA, and the patient's stride frequency, stride length and stride speed were improved, and the overall movement rhythm of both lower limbs are more coordinated.

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++.

Three-Dimensional Posture Estimation of Vehicle Occupants Using Depth and Infrared Images.

Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems.

Reliability and validity of knee valgus angle calculation at single-leg drop landing by posture estimation using machine learning

BackgroundThe consensus on anterior cruciate ligament (ACL) injury prevention involves the suppression of dynamic knee valgus (DKV). The gold standard for evaluating the DKV includes three-dimensional motion analysis systems; however, these are expensive and cannot be used to evaluate all athletes. Markerless motion-capture systems and joint angle calculations using posture estimation have been reported. However, there have been no reports on the reliability and validity of DKV calculations using posture estimation. Research questionThis study aimed to clarify the reliability and validity of DKV calculation using posture estimation. MethodsFifteen participants performed 10 single-leg jump landings from a height of 20 cm, and the knee joint angle was calculated using joint points measured using machine learning (MediaPipe Pose) and motion-capture systems (VICON MX). Two types of angle calculation methods were used: absolute value and change from the initial ground contact (IC). Intra- and inter-rater reliabilities were examined using intraclass correlation coefficients, and concurrent validity was examined using Pearson's correlation coefficients. To examine intra-examiner reliability, we performed single-leg jump landings at intervals of ≥3 days. ResultsThe calculation by MediaPipe Pose was significantly higher than that by the 3-D motion analysis systems (p < 0.05, error range 18.83–19.68°), and there was no main effect of knee valgus angle or time on the excursion angle from IC (p > 0.05). No significant concurrent validity was found in the absolute value, which was significantly correlated with the change in IC. Although the inter-rater reliability of the absolute value was low, the change in IC showed good reliability and concurrent validity. SignificanceThe results of this research suggest that the DKV calculation by pose estimation using machine learning is practical, with normalization by the angle at IC.

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.

Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.

The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator. Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model. We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation. The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.

Application of posture estimation optimization algorithm in the analysis of college air volleyball teaching movements

The advent of computer technology and the modernization of sports education have led to an increasing reliance on intelligent technology in the field of sports education. To improve the application effect of intelligent technology in physical education, a volleyball motion analysis technology combined with pose estimation optimization algorithm is designed. During the process, Kinect collects the joint data of the research object, which is then combined with the commonly used background subtraction and frame difference optimization techniques. This results in the construction of a background model. The background update number in the initial frame is utilized as the reference value. The contour edge is smoothed through the application of a one-dimensional Gaussian kernel function, and a teaching action guidance system is designed. The experimental results showed that the average accuracy of the research method reached 79.7 % and the average recall rate reached 75.2 %. The average relative error of the method was 4.13 % when comparing the accuracy of human body model. The research method is validated to accurately capture and analyze volleyball motion, which can provide some technical help for sports teaching.

Human pose estimation in complex background videos via Transformer-based multi-scale feature integration

Human posture estimation is still a hot research topic. Previous algorithms based on traditional machine learning have difficulties in feature extraction and low fusion efficiency. To address these problems, we proposed a Transformer-based method. We combined three techniques, namely the Transformer-based feature extraction module, the multi-scale feature fusion module, and the occlusion processing mechanism, to capture the human pose. The Transformer-based feature extraction module uses the self-attention mechanism to extract key features from the input sequence, the multi-scale feature fusion module fuses feature information of different scales to enhance the perception ability of the model, and the occlusion processing mechanism can effectively handle occlusion in the data and effectively remove background interference. Our method has shown excellent performance through verification on the standard dataset Human3.6M and the wild video dataset, achieving accurate pose prediction in both complex actions and challenging samples.

3MSP2: Sequential picking planning for multi-fruit congregated tomato harvesting in multi-clusters environment based on multi-views

Fast and effective picking planning from candidate fruits is important to improve the efficiency and success rate of robotic selective harvesting. However, it is a challenge for tomato picking decisions to simultaneously meet low- damage and high-performance requirements in multi-fruits congregation and multi-clusters environments. For such robotic application scenario, a novel sequential picking planning method is proposed for tomatoes harvesting sequence and picking posture estimation combined eye-on-shoulder and eye-in-hand views. First, the detection and localization of multiple tomatoes in both views are achieved by Yolov5. Then, a cluster Optimal Sequence Planning method was developed by us based on the view of eye-on-shoulder. The method combined the density clustering idea and the shortest movement path to automatically classify multiple neighboring tomatoes into candidate picking clusters and make decisions on the optimal picking order of the clusters. Finally, we developed a tomato Optimal Sequential Grasping method based on the view of eye-in-hand. The method combines collision-free constraints and a shortest time objective to determine the optimal grasping posture estimate and the optimal post-grasping observation position estimate for the target tomato. In indoor picking experiments, the proposed method improves the collision-free picking success rate of picking by 17.2 % and 36.1 %, and the picking efficiency by 25 % and 41.4 %, respectively, compared with the picking planning methods based on random traversal and scanning traversal. In the picking experiments in industrial tomato greenhouse, the collision-free picking success rate reaches 70.9 %, and the average cycle time for each picking task from target-recognizing to success-picking is 8.6 s. This approach offers a promising solution for picking planning of harvesting robot to lower damages and improve success rate, efficiency in the congregated fruit environment.

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.

Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

Passive Visible Light Tag System for Localization and Posture Estimation

Passive Visible Light Tag System for Localization and Posture Estimation

Implicit calibration method for underwater stereo cameras.

Underwater stereo cameras can effectively capture intricate environments with restricted accessibility, offering an appealing solution for precise perception. Stereo imaging is however susceptible to distortions caused by the refraction of incoming rays. These distortions are nonlinear and challenge the standard single viewpoint projection assumption. In this paper, we propose a data-driven implicit calibration method for underwater stereo cameras. To address the imaging characteristics and aberration distributions across different coordinates of underwater stereo cameras, we have developed the corresponding coordinates regression network and fusion strategy, thereby converting the calibration process into network-based learning. Secondly, we designed an underwater self-luminous calibration target system and the underwater corner point extraction strategy for sample dataset acquisition. We evaluated the proposed method comprehensively in terms of measurement, camera posture estimation, and 3D reconstruction, and compared it with other explicit calibration methods. The experimental results show that the proposed implicit calibration method is superior to other explicit calibration. We demonstrate with real experiments that our method enables efficient camera calibration for underwater vision applications.

Proposal and validation of a new approach in tele-rehabilitation with 3D human posture estimation: a randomized controlled trial in older individuals with sarcopenia

ObjectiveThrough a randomized controlled trial on older adults with sarcopenia, this study compared the training effects of an AI-based remote training group using deep learning-based 3D human pose estimation technology with those of a face-to-face traditional training group and a general remote training group.MethodsSeventy five older adults with sarcopenia aged 60–75 from community organizations in Changchun city were randomly divided into a face-to-face traditional training group (TRHG), a general remote training group (GTHG), and an AI-based remote training group (AITHG). All groups underwent a 3-month program consisting of 24-form Taichi exercises, with a frequency of 3 sessions per week and each session lasting 40 min. The participants underwent Appendicular Skeletal Muscle Mass Index (ASMI), grip strength, 6-meter walking pace, Timed Up and Go test (TUGT), and quality of life score (QoL) tests before the experiment, during the mid-term, and after the experiment. This study used SPSS26.0 software to perform one-way ANOVA and repeated measures ANOVA tests to compare the differences among the three groups. A significance level of p < 0.05 was defined as having significant difference, while p < 0.01 was defined as having a highly significant difference.Results(1) The comparison between the mid-term and pre-term indicators showed that TRHG experienced significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05); GTHG experienced extremely significant improvements in 6-meter walking pace and QoL (p < 0.01); AITHG experienced extremely significant improvements in ASMI, 6-meter walking pace, and QoL (p < 0.01), and a significant improvement in TUGT timing test (p < 0.05). (2) The comparison between the post-term and pre-term indicators showed that TRHG experienced extremely significant improvements in TUGT timing test (p < 0.01); GTHG experienced significant improvements in ASMI and TUGT timing test (p < 0.05); and AITHG experienced extremely significant improvements in TUGT timing test (p < 0.01). (3) During the mid-term, there was no significant difference among the groups in all tests (p > 0.05). The same was in post-term tests (p > 0.05).ConclusionCompared to the pre-experiment, there was no significant difference at the post- experiment in the recovery effects on the muscle quality, physical activity ability, and life quality of patients with sarcopenia between the AI-based remote training group and the face-to-face traditional training group. 3D pose estimation is equally as effective as traditional rehabilitation methods in enhancing muscle quality, functionality and life quality in older adults with sarcopenia.Trial registrationThe trial was registered in ClinicalTrials.gov (NCT05767710).