搜索结果：Geriatric orthopaedic surgery & rehabilitation

Data scarcity challenges the development and implementation of innovative healthcare solutions. In geriatrics, fall-related injuries are a major cause of hospitalization, functional decline, and mortality in older adults. Optimizing post-operative discharge planning can mitigate these outcomes, but limited data hinders predictive model development. Here, we explored generative machine learning approaches to augment data from the SURGE-Ahead project (Supporting SURgery with Geriatric Co-Management and AI), an initiative addressing geriatric perioperative care. Data from the German geriatric trauma register (AltersTraumaZentrum; ATZ) were incorporated using two strategies: (i) combining SURGE-Ahead and ATZ register data with imputation (ComImp) and (ii) generating synthetic data from SURGE-Ahead alone or combined SURGE-Ahead and the ATZ register datasets with Adversarial random forests (ARF). Predictive models, including multinomial logistic regression, random forest, and a prior-fitted transformer (TabPFN), were trained and evaluated using standard performance metrics: accuracy, area under the receiver operating characteristic curve (ROC AUC), Brier score, and the logistic loss. Ran

Configurable robots are made up of robotic modules that can be assembled or can configure themselves into multiple robot configurations. In this research plan, a method for upper-body rehabilitation will be discussed in the form of a modular robot with different morphologies. The advantage and superiority of designing an example of a robotic module for upper body rehabilitation is the ability to reset the modular robot system. In this research, a number of modules will be designed and implemented according to the needs of one-hand rehabilitation with different degrees of freedom. The design modules' performance and efficiency will be evaluated by simulating, making samples, and testing them. This article's research includes presenting a modular upper body rehabilitation robot in the wrist, elbow, and shoulder areas, as well as providing a suitable kinematic and dynamic model of the upper body rehabilitation robot to determine human-robot interaction forces and movement. The research also involves analyzing the mathematical model of the upper body rehabilitation robot to identify advanced control strategies that rely on force control and torque control. After reviewing the articles

This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following total knee replacement (TKR), utilizing a database of 263 video recordings for evaluation. A smartphone is employed within AIRS to perform RT-3DR of living spaces and has a body-matched avatar to provide visual feedback about the excercise. This avatar is necessary in (a) optimizing exercise configurations, including camera placement, patient positioning, and initial poses, and (b) addressing privacy concerns and promoting compliance with the AI Act. The system guides users through the recording process to ensure the collection of properly recorded videos. AIRS employs two feedback mechanisms: (i) visual 3D feedback, enabling direct comparisons between prerecorded clinical exercises and pati

Precision rehabilitation offers the promise of an evidence-based approach for optimizing individual rehabilitation to improve long-term functional outcomes. Emerging techniques, including those driven by artificial intelligence, are rapidly expanding our ability to quantify the different domains of function during rehabilitation, other encounters with healthcare, and in the community. While this seems poised to usher rehabilitation into the era of big data and should be a powerful driver of precision rehabilitation, our field lacks a coherent framework to utilize these data and deliver on this promise. We propose a framework that builds upon multiple existing pillars to fill this gap. Our framework aims to identify the Optimal Dynamic Treatment Regimens (ODTR), or the decision-making strategy that takes in the range of available measurements and biomarkers to identify interventions likely to maximize long-term function. This is achieved by designing and fitting causal models, which extend the Computational Neurorehabilitation framework using tools from causal inference. These causal models can learn from heterogeneous data from different silos, which must include detailed documenta

Physical rehabilitation exercises suggested by healthcare professionals can help recovery from various musculoskeletal disorders and prevent re-injury. However, patients' engagement tends to decrease over time without direct supervision, which is why there is a need for an automated monitoring system. In recent years, there has been great progress in quality assessment of physical rehabilitation exercises. Most of them only provide a binary classification if the performance is correct or incorrect, and a few provide a continuous score. This information is not sufficient for patients to improve their performance. In this work, we propose an algorithm for error classification of rehabilitation exercises, thus making the first step toward more detailed feedback to patients. We focus on skeleton-based exercise assessment, which utilizes human pose estimation to evaluate motion. Inspired by recent algorithms for quality assessment during rehabilitation exercises, we propose a Transformer-based model for the described classification. Our model is inspired by the HyperFormer method for human action recognition, and adapted to our problem and dataset. The evaluation is done on the KERAAL d

Rehabilitation robots are often used in game-like interactions for rehabilitation to increase a person's motivation to complete rehabilitation exercises. By adjusting exercise difficulty for a specific user throughout the exercise interaction, robots can maximize both the user's rehabilitation outcomes and the their motivation throughout the exercise. Previous approaches have assumed exercises have generic difficulty values that apply to all users equally, however, we identified that stroke survivors have varied and unique perceptions of exercise difficulty. For example, some stroke survivors found reaching vertically more difficult than reaching farther but lower while others found reaching farther more challenging than reaching vertically. In this paper, we formulate a causal tree-based method to calculate exercise difficulty based on the user's performance. We find that this approach accurately models exercise difficulty and provides a readily interpretable model of why that exercise is difficult for both users and caretakers.

In recent years, gamification has become very popular for rehabilitating different cognitive and motor problems. It has been shown that rehabilitation is effective when it starts early enough and it is intensive and repetitive. However, the success of rehabilitation depends also on the motivation and perseverance of patients during treatment. Adding serious games to the rehabilitation procedure will help the patients to overcome the monotonicity of the treatment procedure. On the other hand, if a variety of games can be used with a robotic rehabilitation system, it will help to define tasks with different levels of difficulty with greater variety. In this paper we introduce a procedure for connecting a rehabilitation robot to several web-based games. In other words, an interface is designed that connects the robot to a computer through a USB port. To validate the usefulness of the proposed approach, a researcher designed survey was used to get feedback from several users. The results demonstrate that having several games besides rehabilitation makes the procedure of rehabilitation entertaining.

To support aging-in-place, adult children often provide care to their aging parents from a distance. These informal caregivers desire plug-and-play remote care solutions for privacy-preserving continuous monitoring that enabling real-time activity monitoring and intuitive, actionable information. This short paper presents insights from three iterations of deployment experience for remote monitoring system and the iterative improvement in hardware, modeling, and user interface guided by the Geriatric 4Ms framework (matters most, mentation, mobility, and medication). An LLM-assisted solution is developed to balance user experience (privacy-preserving, plug-and-play) and system performance.

Traumatic Brain Injury (TBI) is a major contributor to mortality among older adults, with geriatric patients facing disproportionately high risk due to age-related physiological vulnerability and comorbidities. Early and accurate prediction of mortality is essential for guiding clinical decision-making and optimizing ICU resource allocation. In this study, we utilized the MIMIC-III database to identify geriatric TBI patients and applied a machine learning framework to develop a 30-day mortality prediction model. A rigorous preprocessing pipeline-including Random Forest-based imputation, feature engineering, and hybrid selection-was implemented to refine predictors from 69 to 9 clinically meaningful variables. CatBoost emerged as the top-performing model, achieving an AUROC of 0.867 (95% CI: 0.809-0.922), surpassing traditional scoring systems. SHAP analysis confirmed the importance of GCS score, oxygen saturation, and prothrombin time as dominant predictors. These findings highlight the value of interpretable machine learning tools for early mortality risk stratification in elderly TBI patients and provide a foundation for future clinical integration to support high-stakes decision

Patients with neurological conditions require rehabilitation to restore their motor, visual, and cognitive abilities. To meet the shortage of therapists and reduce their workload, a robotic rehabilitation platform involving the clinical trail making test is proposed. Therapists can create custom trails for each patient and the patient can trace the trails using a robotic device. The platform can track the performance of the patient and use these data to provide dynamic assistance through the robot to the patient interface. Therefore, the proposed platform not only functions as an evaluation platform, but also trains the patient in recovery. The developed platform has been validated at a rehabilitation center, with therapists and patients operating the device. It was found that patients performed poorly while using the platform compared to healthy subjects and that the assistance provided also improved performance amongst patients. Statistical analysis demonstrated that the speed of the patients was significantly enhanced with the robotic assistance. Further, neural networks are trained to classify between patients and healthy subjects and to forecast their movements using the data

Advanced by rich perception and precise execution, robots possess immense potential to provide professional and customized rehabilitation exercises for patients with mobility impairments caused by strokes. Autonomous robotic rehabilitation significantly reduces human workloads in the long and tedious rehabilitation process. However, training a rehabilitation robot is challenging due to the data scarcity issue. This challenge arises from privacy concerns (e.g., the risk of leaking private disease and identity information of patients) during clinical data access and usage. Data from various patients and hospitals cannot be shared for adequate robot training, further compromising rehabilitation safety and limiting implementation scopes. To address this challenge, this work developed a novel federated joint learning (FJL) method to jointly train robots across hospitals. FJL also adopted a long short-term memory network (LSTM)-Transformer learning mechanism to effectively explore the complex tempo-spatial relations among patient mobility conditions and robotic rehabilitation motions. To validate FJL's effectiveness in training a robot network, a clinic-simulation combined experiment was

Facial nerve paresis is a severe complication that arises post-head and neck surgery; This results in articulation problems, facial asymmetry, and severe problems in non-verbal communication. To overcome the side effects of post-surgery facial paralysis, rehabilitation requires which last for several weeks. This paper discusses an unsupervised approach to rehabilitating patients who have temporary facial paralysis due to damage in mimetic muscles. The work aims to make the rehabilitation process objective compared to the current subjective approach, such as House-Brackmann (HB) scale. Also, the approach will assist clinicians by reducing their workload in assessing the improvement during rehabilitation. This paper focuses on the clustering approach to monitor the rehabilitation process. We compare the results obtained from different clustering algorithms on various forms of the same data set, namely dynamic form, data expressed as functional data using B-spline basis expansion, and by finding the functional principal components of the functional data. The study contains data set of 85 distinct patients with 120 measurements obtained using a Kinect stereo-vision camera. The method d

Robotic rehabilitation can deliver high-dose gait therapy and improve motor function after a stroke. However, for many devices, high costs and lengthy setup times limit clinical adoption. Thus, we designed, built, and evaluated the Passive Mechanical Add-on for Treadmill Exercise (P-MATE), a low-cost passive end-effector add-on for treadmills that couples the movement of the paretic and non-paretic legs via a reciprocating system of elastic cables and pulleys. Two human-device mechanical interfaces were designed to attach the elastic cables to the user. The P-MATE and two interface prototypes were tested with a physical therapist and eight unimpaired participants. Biomechanical data, including kinematics and interaction forces, were collected alongside standardized questionnaires to assess usability and user experience. Both interfaces were quick and easy to attach, though user experience differed, highlighting the need for personalization. We also identified areas for future improvement, including pretension adjustments, tendon derailing prevention, and understanding long-term impacts on user gait. Our preliminary findings underline the potential of the P-MATE to provide effective

Rehabilitation training for patients with motor disabilities usually requires specialized devices in rehabilitation centers. Home-based multi-purpose training would significantly increase treatment accessibility and reduce medical costs. While it is unlikely to equip a set of rehabilitation robots at home, we investigate the feasibility to use the general-purpose collaborative robot for rehabilitation therapies. In this work, we developed a new system for multi-purpose upper-limb rehabilitation training using a generic robot arm with human motor feedback and preference. We integrated surface electromyography, force/torque sensors, RGB-D cameras, and robot controllers with the Robot Operating System to enable sensing, communication, and control of the system. Imitation learning methods were adopted to imitate expert-provided training trajectories which could adapt to subject capabilities to facilitate in-home training. Our rehabilitation system is able to perform gross motor function and fine motor skill training with a gripper-based end-effector. We simulated system control in Gazebo and training effects (muscle activation level) in OpenSim and evaluated its real performance with h

This paper presents GARD, an upper limb end-effector rehabilitation device developed for stroke patients. GARD offers assistance force along or towards a 2D trajectory during physical therapy sessions. GARD employs a non-backdrivable mechanism with novel motor velocity-control-based algorithms, which offers superior control precision and stability. To our knowledge, this innovative technical route has not been previously explored in rehabilitation robotics. In alignment with the new design, GARD features two novel control algorithms: Implicit Euler Velocity Control (IEVC) algorithm and a generalized impedance control algorithm. These algorithms achieve O(n) runtime complexity for any arbitrary trajectory. The system has demonstrated a mean absolute error of 0.023mm in trajectory-following tasks and 0.14mm in trajectory-restricted free moving tasks. The proposed upper limb rehabilitation device offers all the functionalities of existing commercial devices with superior performance. Additionally, GARD provides unique functionalities such as area-restricted free moving and dynamic Motion Restriction Map interaction. This device holds strong potential for widespread clinical use, poten

The rehabilitation treatment helps to heal minor sports and occupational injuries. In a traditional rehabilitation process, a therapist will assign certain actions to a patient to perform in between hospital visits, and it will rely on the patient to remember actions correctly and the schedule to perform them. Unfortunately, many patients forget to perform actions or fail to recall actions in detail. As a consequence, the rehabilitation treatment is hampered or, in the worst case, the patient may suffer from additional injury caused by performing incorrect actions. To resolve these issues, we propose a HRNet-based rehabilitation monitoring system, which can remind a patient when to perform the actions and display the actions for the patient to follow via the patient's smartphone. In addition, it helps the therapist to monitor the progress of the rehabilitation for the patient. Our system consists of an iOS app and several components at the server side. The app is in charge of displaying and collecting action videos. The server computes the similarity score between the therapist's actions and the patient's in the videos to keep track of the number of repetitions of each action. Thes

An emergent challenge in geriatric care is improving the quality of care, which requires insight from stakeholders. Qualitative methods offer detailed insights, but they can be biased and have limited generalizability, while quantitative methods may miss nuances. Network-based approaches, such as quantitative ethnography (QE), can bridge this methodological gap. By leveraging the strengths of both methods, QE provides profound insights into need-finding interviews. In this paper, to better understand geriatric care attitudes, we interviewed ten nursing assistants, used QE to analyze the data, and compared their daily activities in real life with training experiences. A two-sample t-test with a large effect size (Cohen's d=1.63) indicated a significant difference between real-life and training activities. The findings suggested incorporating more empathetic training scenarios into the future design of our geriatric care simulation. The results have implications for human-computer interaction and human factors. This is illustrated by presenting an example of using QE to analyze expert interviews with nursing assistants as caregivers to inform subsequent design processes.

In recent years, the research of rehabilitation robot technology has become a hotspot in the field of rehabilitation medicine engineering and robotics. To assist active rehabilitation in patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method to control the injured lower extremity through its contralateral healthy upper limbs. Firstly, the movement data of upper limbs and lower limbs of healthy people in normal walking state are obtained by gait measurement experiment. Secondly, the eigenvectors of upper limb and lower limb movements in a single movement cycle are extracted respectively. Thirdly, the linear mapping relationship between the upper limbs movement and the lower limbs movement is identified using the least squares method. Finally, the simulation experiment of self-service rehabilitation training is implemented on MATLAB/Simulink. The results indicate that the identified linear mapping model can achieve good accuracy and adaptability. The self-service rehabilitation training method is effective for helping patients with unilateral limb injury to make rehabilitation training on themselves.

Concept erasure in text-to-image diffusion models is crucial for mitigating harmful content, yet existing methods often compromise generative quality. We introduce Semantic Surgery, a novel training-free, zero-shot framework for concept erasure that operates directly on text embeddings before the diffusion process. It dynamically estimates the presence of target concepts in a prompt and performs a calibrated vector subtraction to neutralize their influence at the source, enhancing both erasure completeness and locality. The framework includes a Co-Occurrence Encoding module for robust multi-concept erasure and a visual feedback loop to address latent concept persistence. As a training-free method, Semantic Surgery adapts dynamically to each prompt, ensuring precise interventions. Extensive experiments on object, explicit content, artistic style, and multi-celebrity erasure tasks show our method significantly outperforms state-of-the-art approaches. We achieve superior completeness and robustness while preserving locality and image quality (e.g., 93.58 H-score in object erasure, reducing explicit content to just 1 instance, and 8.09 H_a in style erasure with no quality degradation).