搜索结果：Shoulder & elbow

This paper presents Funabot-Upper, a wearable haptic suit that enables users to perceive 14 upper-body motions, including those of the trunk, shoulder, elbow, and wrist. Inducing kinesthetic perception through wearable haptic devices has attracted attention, and various devices have been developed in the past. However, these have been limited to verifications on single body parts, and few have applied the same method to multiple body parts as well. In our previous study, we developed a technology that uses the contraction of artificial muscles to deform clothing in three dimensions. Using this technology, we developed a haptic suit that induces kinesthetic perception of 7 motions in multiple upper body. However, perceptual mixing caused by stimulating multiple human muscles has occurred between the shoulder and the elbow. In this paper, we established a new, simplified design policy and developed a novel haptic suit that induces kinesthetic perceptions in the trunk, shoulder, elbow, and wrist by stimulating joints and muscles independently. We experimentally demonstrated the induced kinesthetic perception and examined the relationship between stimulation and perceived kinesthetic p

This paper presents a novel rehabilitation robot designed to address the challenges of Passive Range of Motion (PROM) exercises for frozen shoulder patients by integrating advanced scapulohumeral rhythm stabilization. Frozen shoulder is characterized by limited glenohumeral motion and disrupted scapulohumeral rhythm, with therapist-assisted interventions being highly effective for restoring normal shoulder function. While existing robotic solutions replicate natural shoulder biomechanics, they lack the ability to stabilize compensatory movements, such as shoulder shrugging, which are critical for effective rehabilitation. Our proposed device features a 6 Degrees of Freedom (DoF) mechanism, including 5 DoF for shoulder motion and an innovative 1 DoF Joint press for scapular stabilization. The robot employs a personalized two-phase operation: recording normal shoulder movement patterns from the unaffected side and applying them to guide the affected side. Experimental results demonstrated the robot's ability to replicate recorded motion patterns with high precision, with Root Mean Square Error (RMSE) values consistently below 1 degree. In simulated frozen shoulder conditions, the rob

Portable pneumatic systems for 2 degree-of-freedom (DOF) soft shoulder exosuits remain underexplored, and face fundamental trade-offs between torque output and dynamic response that are further compounded by the need for multiple actuators to support complex shoulder movement. This work addresses these constraints through a volume-optimized spindle-shaped angled actuator (SSAA) geometry: by reducing actuator volume by 35.7% (357mL vs. 555mL), the SSAA maintains 94.2% of output torque while achieving 35.2% faster dynamic response compared to uniform cylindrical designs. Building on the SSAA, we develop a curved abduction actuator (CAA) based on the SSAA geometry and a horizontal adduction actuator (HAA) based on the pouch motor principle, integrating both into a dual-DOF textile-based shoulder exosuit (390 g). The exosuit delivers multi-modal assistance spanning shoulder abduction, flexion, and horizontal adduction, depending on the actuation. User studies with 10 healthy participants reveal that the exosuit substantially reduces electromyographic (EMG) activity across both shoulder abduction and flexion tasks. For abduction with HAA only, the exosuit achieved up to 59% muscle activ

Shoulder surfing has been studied extensively, however, it remains unexplored whether and how it impacts users. Understanding this is important as it determines whether shoulder surfing poses a significant concern and, if so, how best to address it. By surveying smartphone users in the UK, we explore how shoulder surfing impacts a) the privacy perceptions of victim users and b) their interaction with smartphones. We found that the impact of being shoulder surfed is highly individual. It is perceived as unavoidable and frequently occurring, leading to increased time for task completion. Individuals are concerned for their own and other peoples privacy, seeing shoulder surfing as a gateway to more serious threats like identity or device theft. Participants expressed a willingness to alter their behaviour and use software based protective measures to prevent shoulder surfing, yet, this comes with a set of user defined criteria, such as effectiveness, affordability, reliability, and availability. We discuss future work directions for user-centred shoulder surfing mitigation.

K-means clustering is an unsupervised clustering method that requires an initial decision of number of clusters. One method to determine the number of clusters is the elbow method, a heuristic method that relies on visual representation. The method uses the number based on the elbow point, the point closest to 90 degrees that indicates the most optimum number of clusters. This research improves the elbow method such that it becomes an objective method. We use the analytical geometric formula to calculate an angle between lines and real analysis principle of derivative to simplify the elbow point determination. We also consider every possibility of the elbow method graph behaviour such that the algorithm is universally applicable. The result is that the elbow point can be measured precisely with a simple algorithm that does not involve complex functions or calculations. This improved method gives an alternative of more reliable cluster determination method that contributes to more optimum k-means clustering.

Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radiologists. AI-driven tools offer a scalable way to assist early detection and reduce diagnostic delays. We address this gap through a dedicated AI system for shoulder radiographs. Methods: We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays. Architectures include Faster R-CNN (ResNet50-FPN, ResNeXt), EfficientDet, and RF-DETR. To enhance detection, we applied bounding box and classification-level ensemble techniques such as Soft-NMS, WBF, and NMW fusion. Results: The NMW ensemble achieved 95.5% accuracy and an F1-score of 0.9610, outperforming individual models across all key metrics. It demonstrated strong recall and localization precision, confirming its effectiveness for clinical fracture detection in shoulder X-rays. Conclusion: The results show ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance. The model's accuracy and deployment readiness position it well for integration into real-time diagnostic workflows. The curr

This paper presents a comprehensive analysis of the contact force profile of a single-cell bidirectional soft pneumatic actuator, specifically designed to aid in the abduction and adduction of the shoulder for pediatric exosuits. The actuator was embedded in an infant-scale test rig featuring two degrees of freedom: an actuated revolute joint supporting shoulder abduction/adduction and a passive (but lockable) revolute joint supporting elbow flexion/extension. Integrated load cells and an encoder within the rig were used to measure the force applied by the actuator and the shoulder joint angle, respectively. The actuator's performance was evaluated under various anchoring points and elbow joint angles. Experimental results demonstrate that optimal performance, characterized by maximum range of motion and minimal force applied on the torso and upper arm, can be achieved when the actuator is anchored at two-thirds the length of the upper arm, with the elbow joint positioned at a 90-degree angle. The force versus pressure and joint angle graphs reveal nonlinear and hysteresis behaviors. The findings of this study yield insights about optimal anchoring points and elbow angles to minimi

Shoulder disorders, such as frozen shoulder (a.k.a., adhesive capsulitis), are common conditions affecting the health of people worldwide, and have a high incidence rate among the elderly and workers engaged in repetitive shoulder tasks. In regions with scarce medical resources, achieving early and accurate diagnosis poses significant challenges, and there is an urgent need for low-cost and easily scalable auxiliary diagnostic solutions. This research introduces videos captured by consumer-grade devices as the basis for diagnosis, reducing the cost for users. We focus on the innovative application of Multimodal Large Language Models (MLLMs) in the preliminary diagnosis of shoulder disorders and propose a Hybrid Motion Video Diagnosis framework (HMVDx). This framework divides the two tasks of action understanding and disease diagnosis, which are respectively completed by two MLLMs. In addition to traditional evaluation indicators, this work proposes a novel metric called Usability Index by the logical process of medical decision-making (action recognition, movement diagnosis, and final diagnosis). This index evaluates the effectiveness of MLLMs in the medical field from the perspect

A common challenge in home-based rehabilitation is muscle compensation induced by pain or fatigue, where patients with weakened primary muscles recruit secondary muscle groups to assist their movement, causing issues such as delayed rehabilitation progress or risk of further injury. In a home-based setting, the subtle compensatory actions may not be perceived since physiotherapists cannot directly observe patients. To address this problem, this study develops a novel wearable strain sensor-based shoulder patch to detect fatigue-induced muscle compensation during bicep curl exercises. Built on an observation that the amplitude of a strain sensor's resistance is correlated to the motion of a joint that the sensor is attached to, we develop an algorithm that can robustly detect the state when significant changes appear in the shoulder joint motion, which indicates fatigue-induced muscle compensation in bicep curls. The developed shoulder patch is tested on 13 subjects who perform bicep curl exercises with a 5 kg dumbbell until reaching fatigue. During the experiment, the performance of the shoulder patch is also benchmarked with optical tracking sensors and surface electromyography (s

Recent analyses of mm-wavelength protoplanetary disk observations have revealed several emission excesses on the previously identified dust rings, referred to as dust shoulders. The prevalence of dust shoulders suggests that they trace a common but unclear mechanism. In this work, we combine 3D, multifluid hydrodynamic simulations with radiative transfer calculations to explain the formation of dust shoulders. We find that the ring-shoulder pairs can result from the 3D planet-disk interactions with massive, gap-opening planets. The key driver is the dust filtration effect at the local pressure maximum due to planet-driven outward gas flows. Our work provides a possible explanation for the outer dust shoulders in recent super-resolution analyses of ALMA observations. It also provides insights into the formation of the inner dust shoulder in the PDS 70 disk and highlights the role of 3D effects in planet-disk interaction studies.

The major-axis density profiles of bars are known to be either exponential or 'flat'. We develop an automated non-parametric algorithm to detect flat profiles and apply it to a suite of simulations (with and without gas). We demonstrate that flat profiles are a manifestation of a bar's secular growth, producing a 'shoulder' region (an overdensity above an exponential) in its outskirts. Shoulders are not present when bars form, but develop as the bar grows. If the bar does not grow, shoulders do not form. Shoulders are often accompanied by box/peanut bulges, but develop separately from them and are independent tracers of a bar's growth. They can be observed at a wide range of viewing orientations with only their slope varying significantly with inclination. We present evidence that shoulders are produced by looped x1 orbits. Since the growth rate of the bar moderately correlates with the growth rate of the shoulder strength, these orbits are probably recently trapped. Shoulders therefore are evidence of bar growth. The properties of the shoulders do not, however, establish the age of a bar, because secondary buckling or strong spirals may destroy shoulders, and also because shoulder

Beam intercepting devices rely on cooling systems to effectively dissipate the thermal energy generated during the impact of a high-energy beam. Regardless of the device's size, integrating the cooling system is a complex task, particularly when the resulting device is only a few centimetres in size, as is the case with the positron source target for the Future Circular Collider at CERN, where the current design consists of a tungsten core with two embedded tantalum cooling tubes. Due to the reduced dimensions of the chosen tantalum tubes (OD6.35xID4.35 mm), the selected manufacturing method is compression bending. The present study develops and evaluates a numerical model to manufacture the required elbow. The methodology is divided in four steps: i) minium allowable bending radius calculation, ii) material constitutive law validation, iii) prediction of the resulting distortion due to ovalization and iv) experimental validation via (non) destructive methods. The results indicate that a minimum bending radius of 10 mm is suitable for manufacturing the elbow. The distortion caused by ovalization is within +-0.5 mm, resulting in an important deviation respect to the nominal geometry

Despite advances in upper-limb (UL) prosthetic design, achieving intuitive control of intermediate joints - such as the wrist and elbow - remains challenging, particularly for continuous and velocity-modulated movements. We introduce a novel movement-based control paradigm entitled Compensation Effect Amplification Control (CEAC) that leverages users' trunk flexion and extension as input for controlling prosthetic elbow velocity. Considering that the trunk can be both a functional and compensatory joint when performing upper-limb actions, CEAC amplifies the natural coupling between trunk and prosthesis while introducing a controlled delay that allows users to modulate both the position and velocity of the prosthetic joint. We evaluated CEAC in a generic drawing task performed by twelve able-bodied participants using a supernumerary prosthesis with an active elbow. Additionally a multiple-target-reaching task was performed by a subset of ten participants. Results demonstrate task performances comparable to those obtained with natural arm movements, even when gesture velocity or drawing size were varied, while maintaining ergonomic trunk postures. Analysis revealed that CEAC effectiv

This study focuses on the rotation of the hips and shoulders during a baseball bat swing, analyzing the time-series changes in rotational angles, rotational velocities, and axes using marker position data obtained from a motion capture system with 12 infrared cameras. Previous studies have examined factors such as ground reaction forces, muscle activation patterns, rotational energy, angular velocity, and angles during a swing. However, to the best of our knowledge, the hip and shoulder rotational motions have not been adequately visualized or compared. In particular, there is a lack of analysis regarding the coordination and timing differences between hip and shoulder movements during the swing. Therefore, this study aims to quantitatively compare the hip and shoulder rotational movements during the swing between skilled and unskilled players and visualizes the differences between them. Based on the obtained data, the study aims to improve the understanding of bat swing mechanics by visualizing the coordinated body movements during the swing.

Reprocessed X-ray radiation from active galactic nuclei (AGN) carries important information about the properties of the circumnuclear material around the black hole. The X-ray photons travel from the very center of the system and interact with that material often producing strong emission lines. The FeK$α$ Compton shoulder is formed by fluorescent FeK$α$ photons that perform Compton scatterings with the intercepting material and lose energy to form the distinct shoulder shape. In this work we use the ray-tracing code RefleX to explore how the physical properties of the medium, as well as its geometry, affect the shape of the Compton shoulder (CS). We start by running simulations using a simple toroidal reflector, to test the effect of the metal composition, metallicity, column density, dust presence and velocity on the FeK$α$ line and its Compton shoulder. We confirm that the shape of the Compton shoulder is sensitive to the optical depth of the intercepting medium, which can be regulated by either changing the metal composition or the line of sight column density of the circumnuclear material. Next, we create a series of models, which feature different geometrical configurations o

Ultrasound imaging of the medial elbow is crucial for the early diagnosis of Ulnar Collateral Ligament (UCL) injuries. Specifically, measuring the elbow joint space in ultrasound images is used to assess the valgus instability of the elbow caused by UCL injuries. To automate this measurement, a model trained on a precisely annotated dataset is necessary; however, no publicly available dataset exists to date. This study introduces a novel ultrasound medial elbow dataset to measure the joint space. The dataset comprises 4,201 medial elbow ultrasound images from 22 subjects, with landmark annotations on the humerus and ulna, based on the expertise of three orthopedic surgeons. We evaluated joint space measurement methods on our proposed dataset using heatmap-based, regression-based, and token-based landmark detection methods. While heatmap-based landmark detection methods generally achieve high accuracy, they sometimes produce multiple peaks on a heatmap, leading to incorrect detection. To mitigate this issue and enhance landmark localization, we propose Shape Subspace (SS) landmark refinement by measuring geometrical similarities between the detected and reference landmark positions.

International standards for biometric identity documents mandate strict compliance with pose requirements, including the square presentation of a subject's shoulders. However, the literature on automated quality assessment offers few quantitative methods for evaluating this specific attribute. This paper proposes a Shoulder Presentation Evaluation (SPE) algorithm to address this gap. The method quantifies shoulder yaw and roll using only the 3D coordinates of two shoulder landmarks provided by common pose estimation frameworks. The algorithm was evaluated on a dataset of 121 portrait images. The resulting SPE scores demonstrated a strong Pearson correlation (r approx. 0.80) with human-assigned labels. An analysis of the metric's filtering performance, using an adapted Error-versus-Discard methodology, confirmed its utility in identifying non-compliant samples. The proposed algorithm is a viable lightweight tool for automated compliance checking in enrolment systems.

This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subject

Carbon emissions have long been attributed to the increase in climate change. With the effects of climate change escalating in the past few years, there has been an increased effort to find green alternatives to power generation, which has been a major contributor to carbon emissions. One prominent way that has arisen is biomechanical energy, or harvesting energy based on natural human movement. This study will evaluate the feasibility of electric generation using a gear and generator-based biomechanical energy harvester in the elbow joint. The joint was chosen using kinetic arm analysis through MediaPipe, in which the elbow joint showed much higher angular velocity during walking, thus showing more potential as a place to construct the harvester. Leg joints were excluded to not obstruct daily movement. The gear and generator type was decided to maximize energy production in the elbow joint. The device was constructed using a gearbox and a generator. The results show that it generated as much as 0.16 watts using the optimal resistance. This demonstrates the feasibility of electric generation with an elbow joint gear and generator-type biomechanical energy harvester.