Methods for medical applications

Background: Hip osteoarthritis (OA) is a degenerative joint disease that affects approximately 25% of individuals over their lifetime, with prevalence expected to rise due to population aging. Gait analysis is recognized as a valuable tool for understanding the biomechanical impact of OA, providing insights into how patients adapt to pain and dysfunction. Objectives: This study aims to investigate gait differences between hip OA patients and healthy controls using markerless systems and machine learning techniques to classify patients based on gait data. Methods: Thirty-six hip OA patients and thirteen healthy controls were analyzed, and machine learning models were trained using gait parameters. Results: The Support Vector Classifier model achieved the highest classification F1-score (96%), demonstrating the potential of combining gait analysis with machine learning to support hip OA diagnosis. Conclusion: The findings of the present study suggest that machine learning applied to gait analysis data could enhance diagnosis and management of OA in clinical practice.

Keywords: Diagnosis , Gait Analysis , Hip Osteoarthritis , Machine Learning , Total Hip Arthroplasty

Background: Serious games (SGs) and telerehabilitation play a key role in the recovery of lost functions in neurological patients, with personalisation and difficulty adjustment being essential features. Objectives: This work investigates the feasibility of integrating a large language model (LLM) into an Assessment Serious Game (ASG) to analyse exercise data and recommend personalised rehabilitation programs. Methods: Medical knowledge was acquired through meetings with professionals to identify target pathologies and parameters. The ASG was integrated with GroqCloud; the prompt is designed to act as physiotherapist and SG developer to make real-time adjustments and suggest the setting configurations of other SGs. A preliminary test assessed the system's capabilities. Results: The LLM effectively recognises real-time adjustments and follows instructions for SGs parameter settings. However, limitations remain in the degree of adjustments and numerical parameter suggestions. Conclusion: The analysis demonstrates the feasibility of a designed LLM prompt to adjust SG difficulty and recommend setup parameters, while highlighting areas for improvement in reliability and accuracy.

Keywords: Exergaming , Large Language Model , Neurological Rehabilitation , Telerehabilitation , Upper Extremity

Background: Associative memory is essential for episodic memory formation, and training in this domain has been shown to enhance memory in both clinical and non-clinical populations. However, the application of VR-based training in this memory domain remains underexplored. Objectives: This study aimed to develop and evaluate a VR-based intervention to improve associative memory, focusing on verbal-visual and visual-auditory stimuli associations. Methods: Five healthy younger adults (mean age = 24.6) completed 8 trials of object-name and object-sound matching tasks in a virtual environment. Results: Significant performance improvements were observed across trials, with object-name matching showing higher recognition accuracy and faster response times than object-sound matching. Conclusion: Both tasks demonstrated increased accuracy with reduced response times with training. This study underscores the importance of tailoring VR-based cognitive training to specific associative tasks, offering promising applications in memory rehabilitation and cognitive enhancement.

Keywords: Memory Training , Paired-Associate Learning , Virtual Reality

Respiratory rehabilitation is essential for patients after cardiac surgery to avoid postoperative pulmonary complications. However, the majority of rehabilitation programs are self-administered at home, which often results in low compliance. This research work aims to develop a smartphone application to support patients during respiratory rehabilitation sessions with a three-ball incentive spirometer. The developed application leverages the smartphone camera and a keypoints detection algorithm to track spirometer exercises, guiding patients through the session. The app also stores quantitative data, making them available to medical staff on a telemedicine web platform. The application relies on the YOLOv8-pose model to detect seven spirometer keypoints, allowing for accurate tracking of ball positions and corresponding inspiration rates. We trained and optimized three YOLOv8-pose models (Nano, Small, and Medium) using a custom dataset and four image input sizes. Each model was evaluated on a test set of real-world scenarios. YOLOv8-pose Nano, with an input size of 192 pixels, was selected for app deployment, achieving a recall of 98.49%, precision of 98.51%, mAP50 of 98.38%, mAP50-95 of 85.26%, and average inference time on a consumer smartphone of 279±73ms. These results demonstrate the potential of using a lightweight YOLOv8-pose model for real-time monitoring and compliance improvement of respiratory rehabilitation.

Keywords: Incentive Spirometer , Keypoint Detection , Respiratory Rehabilitation , Telemonitoring , YOLO

Anterior cruciate ligament reconstruction (ACL-R) has a high success rate, but a subset of patients report unsatisfactory outcomes, highlighting the need for improved predictive tools. Recent studies have shown that bony morphology plays an important role in both the risk of ACL injury and post-reconstruction outcomes. This study aims to develop a framework for understanding the relationship between morphology and patient satisfaction. We explore the use of machine learning (ML) models to predict patient satisfaction following ACL reconstruction based on preoperative bone morphology. Using 3D models from high-resolution Magnetic Resonance (MR) imaging, 21 morphological parameters of the femur and tibia were analysed, including linear, surface and volumetric measurements. These were correlated with patient satisfaction as assessed by the Knee injury and Osteoarthritis Outcome Score Global (KOOS Global) questionnaire, administered six months after surgery. To streamline the analysis, automated systems based on statistical shape models and deep learning were employed for landmark placement and segmentation. A dataset of 45 patients was used to train and evaluate three ML models: Random Forest, Logistic Regression and Gradient Boosting. Dimensionality reduction identified 10 key morphological features and Gradient Boosting outperformed the other models, achieving accuracy of 0.89, precision of 0.90 and recall of 0.89. The results demonstrate the potential of combining advanced imaging, automated measurement and ML for personalized prediction of patient satisfaction. The predictive model shows that primarily femoral features, along with intercondylar notch volume and tibial measurements, play a critical role in determining patient satisfaction. This approach may guide surgical planning, patient counselling, intervention strategies and rehabilitation. Although promising, further research with larger datasets, additional parameters and advanced ML techniques is needed to improve model robustness and clinical applicability.

Keywords: 3D modeling , Anterior cruciate ligament , Machine learning , Morphology , Prediction

The magnesium AZ31 alloy is one of the most studied biodegradable materials for biomedical applications. It exhibits excellent biocompatibility and osseointegration and has an elastic modulus comparable to that of human bone. Furthermore, magnesium is essential for the metabolism in many biological mechanisms, and the Mg2+ ions, which are produced from its degradation, are useful to promote healing and tissue growth. However, the AZ31 alloy has the drawback of quickly degrading in the presence of biological fluids. Corrosion leads to a reduction of the implant's mechanical properties, compromising its functionality. Unlike other surface treatments that provide only a barrier function, the layered double hydroxides (LDHs) also offer the opportunity to release drugs. This work investigated the growth of LDHs on the AZ31 alloy by using three different nutrient solutions with (i) Zn(NO3)2*6H2O, (ii) Mg(NO3)2*6H2O, and (iii) FeCl3. The treatments were carried out at 90°C for 12 h. The chemical composition and structure of treated surfaces were examined by scanning electron microscopy (SEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS). The results of these analyses are presented and discussed.

Keywords: AZ31 , biodegradable magnesium alloys , LDH , XPS

The high corrosion rate of magnesium alloys in body fluids represents a serious drawback for biomedical applications. Grain refinement through of equal channel angular pressing (ECAP) processing seems to be quite promising to overcome this issue. This work investigates the conditions for depositing layered double hydroxides (LDHs) coatings through coprecipitation and hydrothermal treatment on AZ31 alloy subjected to 1, 2, and 4 ECAP passes. Owing to their tunable structure and ion-exchange capacity, LDHs have the capability to load and release drugs. Synthesis is made by using two distinct synthesis times (30 min and 12 h) and then the samples are examined by light microscopy, scanning electron microscopy, and X-ray diffraction. The results show that LDH deposition is strongly affected by the substrate microstructure. LDH nucleation occurs on screw dislocations emerging at the surface of substrate: fine and homogeneous distribution of LDH crystals is achieved if, in addition to homogeneous nuclei distribution, surface diffusion occurs. This condition is favored by the 0002-texture component. The deposition on AZ31 alloy subjected to 1 ECAP pass with a synthesis time of 30 min gives rise to a complete surface coating and a crystal morphology that seems suitable for medical and biomedical applications.

Keywords: AZ31 , equal channel angular pressing , layered double hydroxides , microstructures

Featured Application: Negative-stiffness lattice structures represent a groundbreaking class of mechanical metamaterials capable of redistributing pressure in highly efficient and adaptive ways. Thanks to their unique ability to undergo localized deformation while maintaining global structural integrity, these lattices are ideal for applications requiring uniform load distribution, energy absorption, and enhanced comfort. From biomedical implants to aerospace panels and protective equipment, their tunable mechanical response opens new frontiers in design and performance. Diabetes mellitus often leads to peripheral neuropathy that compromises protective sensation in the feet and raises ulcer risk through mechanical overload. While prior research has introduced cellular-metamaterial-based shoe midsoles for dynamic plantar pressure redistribution, this study advances the field by delivering a complete, application-oriented workflow for physical prototyping and mechanical validation of such structures. Our pipeline integrates analytical synthesis of curved-beam unit cells, process calibration, and fabrication via thermoplastic polyurethane (TPU) fused-filament fabrication, producing customized, test-ready lattices suitable for future gait-simulation studies and in vivo assessment. Printed TPU tests showed a Young's modulus of 44.5 MPa, ultimate tensile strength of 4.9 MPa, and strain at break ≈ 20% (Shore 84.5 A/37.2 D). The cellular unit's compressive response was quantified by theoretical force-threshold estimates and controlled compression tests, enabling data-driven selection of unit cell geometry and arrangement for effective offloading. The response is rate-dependent: higher loading speed increases peak force and hysteresis, indicating that loading rate should be treated as a design parameter to tune dynamic behavior for the target application. Although the analytical model overestimates forces by roughly 50% on average relative to experiments, it accurately captures the influence of key geometric parameters on peak force. Accordingly, experimental data can identify cell strategic geometric parameters (i.e., Q), while the achievable maximum force can be predicted from the model by applying an appropriate correction factor.

Keywords: diabetes mellitus , lattice structures , midsoles , negative stiffness , thermoplastic polyurethane (TPU)

Featured Application: The work finds potential application in the design and manufacture of patient-specific scaffolds in the biomedical field using desktop MEX AM technologies. The design of scaffolds and prostheses benefits from the opportunities provided by additive manufacturing technologies. Specifically, scaffold design using cellular structures based on lattices has become a significant focus. These lattice-based scaffolds exhibit intricate and complex shapes with controlled macro-porosity. In this study, a method is presented that enables the modeling of a graded-density lattice structure for material extrusion additive manufacturing, without relying on a geometric lattice model. The methodology utilizes computed tomography (CT) scans as inputs to obtaining a 3D scalar field and a surface model. The lattice structure is designed and generated within the computer-aided manufacturing (CAM) software, ensuring consistent machine toolpaths. The 3D scalar field, representing a relative density map derived from CT Hounsfield units, drives the variation of the extrusion parameters generated by the CAM, achieving a graded-density lattice.

Keywords: additive manufacturing , computed tomography , functionally graded lattice structures , fused filament fabrication , graded lattices , volumetric modeling

Objective: To compare augmented reality (AR) and classical intraoperative C-arm surgical navigation and evaluate whether head-mounted display improves surgical accuracy in the placement of a rod-like object, such as K-wire, using an anatomically accurate elbow phantom. Participants and Methods: Data were collected between January 10, 2024, and March 15, 2024. We developed an AR system, X-ray simulation system and surgical phantom to test K-wire placement in 3 locations of the distal humerus and proximal ulnar bones. An initial phase with only X-ray as guidance was performed as case control; in later phases, the candidates were allowed to also use the head-mounted display. The evaluation parameters were time, placement angle, number of X-ray images taken, number of attempts, and distance from anatomical structures. Results: In total, 19 physicians participated in the study. We analyzed 193 K-wire placements attempts that resulted in 150 estimated correct positions. This reflects a real-world scenario where multiple placements might be attempted to correctly place a K-wire. Compared with standard procedure, the use of AR resulted in −53.8 seconds in K-wire placement time, −47% of angular error from the K-wire target, −80% X-ray images taken to reach the estimate correct position, and decrease in distance variability of −81%, of the K-wire from anatomical structures of interest.

Lip-reading technology, based on visual speech decoding and automatic speech recognition, offers a promising solution to overcoming communication barriers, particularly for individuals with temporary or permanent speech impairments. However, most Visual Speech Recognition (VSR) research has primarily focused on the English language and general-purpose applications, limiting its practical applicability in medical and rehabilitative settings. This study introduces the first Deep Learning (DL) based lip-reading system for the Italian language designed to assist individuals with vocal cord pathologies in daily interactions, facilitating communication for patients recovering from vocal cord surgeries, whether temporarily or permanently impaired. To ensure relevance and effectiveness in real-world scenarios, a carefully curated vocabulary of twenty-five Italian words was selected, encompassing critical semantic fields such as Needs, Questions, Answers, Emergencies, Greetings, Requests, and Body Parts. These words were chosen to address both essential daily communication and urgent medical assistance requests. Our approach combines a spatiotemporal Convolutional Neural Network (CNN) with a bidirectional Long Short-Term Memory (BiLSTM) recurrent network, and a Connectionist Temporal Classification (CTC) loss function to recognize individual words, without requiring predefined words boundaries. The experimental results demonstrate the system's robust performance in recognizing target words, reaching an average accuracy of 96.4% in individual word recognition, suggesting that the system is particularly well-suited for offering support in constrained clinical and caregiving environments, where quick and reliable communication is critical. In conclusion, the study highlights the importance of developing language-specific, application-driven VSR solutions, particularly for non-English languages with limited linguistic resources. By bridging the gap between deep learning-based lip-reading and real-world clinical needs, this research advances assistive communication technologies, paving the way for more inclusive and medically relevant applications of VSR in rehabilitation and healthcare.

Keywords: 3D convolutional neural network , automatic speech recognition , deep learning , Lip-reading , visual speech decoding

Shared ventilation is an interesting topic amongst researchers due to the possibility of healthcare saturation under disaster conditions provoked by viruses, war environments, diffusive respiratory system diseases, etc. However, general outcomes from this practice have shown several risks. The Multiple Emergency Ventilator overcomes these hazards by providing a temporary, pressure-controlled ventilatory support for up to ten patients simultaneously, whilst allowing for a modular and fast assembly during emergencies. This article presents the construction and the assessment of the prototype using patient simulators with different types of bio-mechanical characteristic configurations at ten hospitalization stations. Ventilation parameters at the most distal-to-source station were recorded. Under normal operation, a minute ventilation of 9.6 breaths × L × min−1 was obtained. Critical operation during different types of localized sudden pneumatic disconnections was also investigated. Amongst these, only the event of a disconnection of any simulator at its pneumatic connection to the main distribution pipeline highlighted a poor minute ventilation of 4 breaths × L × min−1. Yet, the probability of such an event is minimal due to the use of safe mechanical constraints at the relevant pneumatic connections. In this first prototype assessment, the device has shown its capabilities to ventilate multiple patients with consistent pressure behaviours even whilst adverse conditions are imposed.

Keywords: Acute respiratory distress syndrome , Emergencies , Intensive care units , Mechanical ventilation

Background: Adherence to home-based respiratory rehabilitation is a significant challenge for cardiac surgery patients. Few studies have attempted to address this problem. Methods: This research introduces a digital solution to improve adherence to respiratory rehabilitation by offering virtual supervision. It leverages the YOLOv11 pose detection algorithm to track the Voldyne® 2500 incentive spirometer and monitor rehabilitation sessions. Usability and accuracy were evaluated with nine healthy volunteers, combining objective performance metrics with the System Usability Scale (SUS) survey. Results: The models demonstrated high accuracy in spirometer tracking and efficient inference times. The app achieved an average SUS score of 83%, indicating good usability, though refinements to the monitoring algorithms are recommended. Conclusion: This low-cost, non-invasive solution shows potential for clinical use. Future efforts will focus on enhancing usability and accuracy to prepare the app for clinical trials.

Keywords: Cardiac Rehabilitation , Respiratory Therapies , Spirometry , YOLO Network

Background: Telemedicine is increasingly integrated into healthcare, requiring robust training for healthcare professionals. Traditional training methods often lack realism and immersion, highlighting the need for innovative approaches. Objectives: This study aims to integrate Virtual Reality with Moodle to enhance telemedicine education, focusing on chronic cardiac conditions. Methods: A VR-based training system was developed using Unity and WebXR, integrated with Moodle. The system includes a serious game with virtual patients created using Synthesia AI. Telemedicine experts configure training sessions via Moodle, while learners access preparatory materials and engage with the VR environment. Results: A usability test involving 12 participants showed high satisfaction scores (94.42% for learners, 98.29% for experts). Conclusion: This study demonstrates the potential of integrating VR with Moodle to innovate telemedicine education. The approach offers personalized and accessible training experiences for healthcare professionals to improve telemedicine skills.

Keywords: Educational Virtual Reality , Learning Health System , Medical Field Training , Telehealth

Background and Objective: The paper aims to demonstrate the integration of advanced technologies, including computed tomography (CT), computer-aided design (CAD), and additive manufacturing, for precise surgical planning and personalized solutions in the management of Osteogenesis Imperfecta (OI). The main research question is to determine how these technologies can be utilized to achieve successful surgical outcomes in severe cases of OI, such as the one presented in this study. Methods: The study involved an 11-year-old child with OI who suffered from a closed right diaphyseal femur fracture and severe lower extremity abnormalities. Virtual and physical planning procedures were carried out using CAD software based on patient-specific CT models. The length of the proximal and distal segments, cutting planes, and osteotomies were precisely defined to achieve the desired surgical corrections. Additionally, 3D models of the bones and bony segments were manufactured using additive manufacturing to physically recreate the surgical procedure. Results: The surgical treatment involved the correction of the right femur fracture and the left tibia and fibula in the first procedure, followed by the correction of the remaining segments in a second procedure. The Fassier-Duval telescopic intramedullary nail was used to stabilize the fracture and the osteotomy sites. The entire treatment course, from the first surgery to achieving partial weight-bearing, spanned approximately 15 weeks, including the two surgical procedures and staged rehabilitation. Post-surgery, the patient showed significant functional improvement, including the ability to stand and walk with assistance. Conclusion: The integration of advanced technologies in surgical planning for OI patients has shown promising results, leading to improved patient outcomes and reduced complications.

Keywords: Additive manufacturing , Computer aided design , Osteogenesis Imperfecta , Surgery planning

In radiotherapy treatments of mobile lesions, several strategies are used to treat the tumour while sparing the surrounding tissues [1]. The ADAM phantom (Anthropomorphic Dynamic breAthing Model) was developed to test dose delivery in lung cancer treatment. The aim of this study is to create ADAM A symbol representing the male gender, depicted as a circle with an arrow pointing diagonally upwards to the right. (Anthropomorphic Dynamic breAthing Model - Chemical structure diagram showing a hexagonal benzene ring with alternating double bonds. Attached to the ring is a hydroxyl group (OH) and a methyl group (CH3). The structure is labeled with standard chemical notation. male&female), a new modular phantom based on ADAM, to test free-breathing and deep inspiration breath hold (DIBH) breast radiotherapy treatments [2, 3]. The 3D model of ADAM was reconstructed using computed tomography (CT) scans to ensure a high level of anatomical accuracy. This digital model of ADAM served as the starting point for the design and development of ADAM Symbol representing a combination of male and female gender symbols, featuring a circle with an arrow pointing diagonally upward to the right and a cross extending downward. . 3D printable materials with tissue mimicking properties were selected to replicate tissues and organs [4, 5, 6, 7, 8–9]. The torso and chest were printed in PLA and the ribs and spine were printed in PLA StoneFil™ [10]. Tests have shown that ADAM A symbol representing the male gender, depicted as a circle with an arrow pointing diagonally upwards to the right. closely mimics human tissue in terms of Hounsfield Units. Its modular structure allows rapid transition between male and female configurations, making it suitable for realistic simulation of many clinical situations. ADAM A symbol combining the male and female gender symbols, featuring a circle with an arrow pointing diagonally upwards to the right and a cross extending downwards. demonstrates adequate performance for future testing gating and tracking devices in the treatment of moving breast and lung lesions. Chemical structure diagram showing a hexagonal benzene ring with alternating double bonds. Attached to the ring is a hydroxyl group (OH) and a methyl group (CH3). The structure is labeled with standard chemical notation.

Keywords: 3D Modelling , 3D Printing , Anthropomorphic Breathing Phantom , Tissue Mimicking

Extended reality (XR) technologies are no longer peripheral innovations but emerging cornerstones of human–technology interaction across critical sectors. This article takes the position that engineering and healthcare represent the most mature and strategically relevant domains for XR adoption, given their safety-critical nature, intensive training requirements, and strong alignment with the human-centric visions of Industry 5.0 and Healthcare 5.0. We synthesize evidence from product design, manufacturing, training, and patient care to demonstrate how XR is reshaping workflows, skills, and therapeutic practices. Beyond surveying applications, we argue that the future of XR depends on its integration with artificial intelligence, digital twins, and multisensory feedback, converging into systems capable of perceiving, reasoning, and adapting to complex physical and human environments. We contend that widespread adoption will remain limited without open standards, validated protocols, and robust evaluation frameworks addressing safety, interoperability, and data governance. By framing XR as both a technological enabler and a societal imperative, this position article calls for coordinated action among researchers, practitioners, and policymakers to realize XR's role in building sustainable, personalized, and participatory innovation ecosystems.

Keywords: digital industry , extended reality , human-centered innovation , human–computer interfaces/interactions , immersive technology , virtual and augmented reality environments , virtual prototyping , XR for training , XR in healthcare , XR in manufacturing

The rapid advancements in computer processing, algorithmic development, and the availability of large-scale datasets have positioned Artificial Intelligence (AI) as a valuable tool across multiple domains, including Medicine. In the field of Nuclear Medicine neuroimaging, with Positron Emission Tomography (PET), AI has demonstrated significant potential in improving diagnostic accuracy for neurodegenerative cognitive disorders. This is especially relevant for the early diagnosis, preclinical detection, and prediction of disease progression in Alzheimer's disease (AD), the most prevalent form of cognitive impairment in individuals over 65 years of age. This narrative review aims to synthesize current advances, explore future directions, and highlight outstanding challenges in the application of Artificial Intelligence to PET imaging for the clinical management of Alzheimer's disease, with particular focus on three key modalities: 18F-FDG PET, Amyloid PET, and Tau PET.

Keywords: 18F-FDG PET , Alzheimer's disease , Amyloid PET , Artificial Intelligence , Tau PET

Background: Differentiating Parkinson's disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with 123I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed 123I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of 123I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging.

Keywords: 123I-Ioflupane , artificial intelligence , BasGanV2™ , DaTQUANT® , dopamine transporters , machine learning , movement disorders , Parkinson's disease , rehabilitation , SPECT

This study presents the development of a prototype anthropomorphic soft robotic gripper intended for applications in rehabilitation and assistive robotics, where safe and adaptive interaction with humans is required. The device consists of three elastomeric fingers, fabricated in TPU via FFF 3D printing and actuated through pneumatic soft actuators that ensure compliant contact with both biological tissue and rigid objects. A custom 3D-printed pneumatic rotary actuator enables finger reconfiguration, thereby extending the range of grasping modalities. The actuation system comprises six 2/2 solenoid valves controlled by an Arduino Uno and integrated into a dedicated pneumatic circuit. Experimental characterization demonstrated a peak grasping force exceeding 17 N on rigid targets, while functional tests in table-picking scenarios confirmed adaptability to objects of varying shapes and sizes. Owing to its anthropomorphic configuration, mechanical compliance, and ease of fabrication and control, the proposed gripper represents a versatile solution for rehabilitation-oriented devices as well as assistive robotic end-effectors in pick-and-place tasks.

Keywords: additive manufacturing , assistive robotics , pneumatic , rehabilitation , soft actuator , soft gripper