Search Results (63)

Background/Objectives: The simulation of human movement offers transformative potential for the design of medical devices, particularly in understanding the cause–effect dynamics in individuals with neurological or musculoskeletal impairments. This study presents a simulation-driven framework to determine the optimal ankle–foot orthosis (AFO) stiffness for mitigating the risk of ankle sprains due to excessive subtalar inversion during high-impact activities, such as landing from a free fall. Methods: We employed biomechanical simulations to assess the influence of translational stiffness on subtalar inversion control, given that inversion angles exceeding 25 degrees are strongly correlated with injury risk. Simulations were conducted using a musculoskeletal model with and without a passive AFO; the stiffness varied in three anatomical directions. A Design of Experiments (DoE) approach was utilized to capture nonlinear interactions among stiffness parameters. Results: The results indicated that increased translational stiffness significantly reduced inversion angles to safer levels, though direction–dependent effects were noted. Based on these insights, we developed a 4D visualization tool that integrates simulation data with an interactive color–coded interface to depict ”safe design” zones for various AFO stiffness configurations. This tool supports clinicians in selecting stiffness values that optimize both safety and functional performance. Conclusions: The proposed framework enhances clinical decision-making and engineering processes by enabling more accurate and individualized AFO designs. Full article

Background/Objectives: This study focuses on the motion planning and control of an active ankle–foot orthosis (AFO) that leverages biomechanical insights to mitigate footdrop, a deficit that prevents safe toe clearance during walking. Methods: To adapt the motion of the device to the user’s walking speed, a geometric model was used, together with real-time measurement of the user’s gait cycle. A geometric speed-adaptive model also scales a trapezoidal ankle-velocity profile in real time using the detected gait cycle. The algorithm was tested at three different walking speeds, with a prototype of the AFO worn by a test subject. Results: At walking speeds of 0.44 and 0.61 m/s, reduced tibialis anterior (TA) muscle activity was confirmed by electromyography (EMG) signal measurement during the stance phase of assisted gait. When the AFO was in assistance mode after toe-off (initial and mid-swing phase), it provided an average of 48% of the estimated required power to make up for the deliberate inactivity of the TA muscle. Conclusions: Kinematic analysis of the motion capture data showed that sufficient foot clearance was achieved at all three speeds of the test. No adverse effects or discomfort were reported during the experiment. Future studies should examine the device in populations with footdrop and include a comprehensive evaluation of safety. Full article

Integrating bioinspired design and additive manufacturing into engineering education fosters innovation to meet the growing demand for accessible, personalized assistive technologies. This paper presents the outcomes of an international course, “3D Prosthetics and Orthotics”, offered to undergraduate students in the Biomimetic program at Westfälische Hochschule (Germany), in collaboration with the 3D Orthotics and Prosthetics Laboratory at the Federal University of São Paulo—UNIFESP (Brazil). The course combined theoretical and hands-on modules covering digital modeling (CAD), simulation (CAE), and fabrication (CAM), enabling students to develop bioinspired assistive devices through a Project-based learning approach. Working in interdisciplinary teams, students addressed real-world rehabilitation challenges by translating biological mechanisms into engineered solutions using additive manufacturing. Resulting prototypes included a hand prosthesis based on the Fin Ray effect, a modular finger prosthesis inspired by tendon–muscle antagonism, and a cervical orthosis designed based on stingray morphology. Each device was digitally modeled, mechanically analyzed, and physically fabricated using open-source and low-cost methods. This initiative illustrates how biomimetic mechanisms and design can be integrated into education to generate functional outcomes and socially impactful health technologies. Grounded in the Mao3D open-source methodology, this experience demonstrates the value of combining nature-inspired principles, digital fabrication, Design Thinking, and international collaboration to advance inclusive, low-cost innovations in assistive technology. Full article

As populations age, particularly in countries like Japan, mobility impairments related to ankle joint dysfunction, such as foot drop, instability, and reduced gait adaptability, have become a significant concern. Active ankle–foot orthoses (AAFO) offer targeted support during walking; however, most existing systems rely on rule-based or threshold-based control, which are often limited to sagittal plane movements and lacking adaptability to subject-specific gait variations. This study proposes an approach driven by neuromuscular activation using surface electromyography (EMG) and a Gated Recurrent Unit (GRU)-based deep learning model to predict plantar pressure distributions at the heel, midfoot, and toe regions during gait. EMG signals were collected from four key ankle muscles, and plantar pressures were recorded using a customized sandal-integrated force-sensitive resistor (FSR) system. The data underwent comprehensive preprocessing and segmentation using a sliding window method. Root mean square (RMS) values were extracted as the primary input feature due to their consistent performance in capturing muscle activation intensity. The GRU model successfully generalized across subjects, enabling the accurate real-time inference of critical gait events such as heel strike, mid-stance, and toe off. This biomechanical evaluation demonstrated strong signal compatibility, while also identifying individual variations in electromechanical delay (EMD). The proposed predictive framework offers a scalable and interpretable approach to improving real-time AAFO control by synchronizing assistance with user-specific gait dynamics. Full article

Upper limb assistive exoskeletons help stroke patients by assisting arm movement in impaired individuals. However, effective control of these systems to help stroke survivors is a complex task. In this paper, a novel approach is proposed to enhance the control of upper limb assistive exoskeletons by using torque estimation and prediction in a proportional–integral–derivative (PID) controller loop to more optimally integrate the torque of the exoskeleton robot, which aims to eliminate system uncertainties. First, a model for torque estimation from Electromyography (EMG) signals and a predictive torque model for the upper limb exoskeleton robot for the elbow are trained. The trained data consisted of two-dimensional high-density surface EMG (HD-sEMG) signals to record myoelectric activity from five upper limb muscles (biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres) during voluntary isometric contractions for twelve healthy subjects performing four different isometric tasks (supination/pronation and elbow flexion/extension) for one minute each, which were trained on long short-term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent units (GRU) deep neural network models. These models estimate and predict torque requirements. Finally, the estimated and predicted torque from the trained network is used online as input to a PID control loop and robot dynamic, which aims to control the robot optimally. The results showed that using the proposed method creates a strong and innovative approach to greater independence and rehabilitation improvement. Full article

Objective: The resistive knee orthosis, as a novel rehabilitation device, is designed to provide resistance to joint movement during continuous walking, thereby enhancing the postoperative recovery effect. This study aims to explore the impact of such orthoses on the joint coordination patterns of martial artists after anterior cruciate ligament (ACL) reconstruction. Methods: A total of 44 martial artists who underwent ACL reconstruction were recruited and divided into an experimental group (EG, n = 22, using resistive braces) and a control group (CG, n = 22, using conventional braces). Assessments were conducted preoperatively (T0) and at 15 days (T1), 30 days (T2), and 60 days (T3) postoperatively. The changes in joint coordination patterns during the gait cycle were analyzed, and the ACL force was estimated using a musculoskeletal model. Results: At T2 and T3, compared with the CG, the EG exhibited a significantly larger peak knee flexion angle (p < 0.05). At T3, the EG showed higher hip–ankle in-phase coordination (p < 0.05), increased proximal hip–knee coordination (p < 0.05), and decreased knee–ankle anti-phase coordination (p < 0.05). In addition, the ACL force in the EG was significantly lower. Conclusions: The resistive knee orthosis can effectively improve the joint coordination of martial artists after ACL reconstruction and reduce the ACL force. Full article

Hand orthoses are often recommended as a rehabilitation measure for patients with rheumatoid arthritis (RA). However, existing research on the efficacy of hand orthoses has predominantly focused on 3D-printed devices and post-intervention clinical functional assessments, which tend to be subjective. There is a notable lack of biomechanical studies evaluating the effects of wearing orthoses. Therefore, in this study, the finite element method was used to analyze the biomechanical properties of an RA hand. A hand orthosis was designed based on the principle of three-point force, and a composite model of the RA hand and orthosis was constructed to verify its effectiveness. The results showed that the peak stress and displacement of the RA hand were 3.22–183.21% and 28.81–124.23% higher than those of the normal hand. Compared with the RA hand under direct force, the peak stress of the RA hand after wearing orthosis was generally reduced by 3.05–55.60%, and the peak displacement was generally reduced by 20.35–71.43%, verifying the effectiveness of the orthosis. Additionally, variations in the magnitude of three-point forces influenced the orthopedic effects. This study proves the effectiveness of hand orthosis and provides some theoretical data for subsequent research and treatment of rheumatoid arthritis. Full article

Knowledge of realistic loads is crucial in the engineering design process of medical devices and for assessing their interaction with the spinal system. Depending on the type of modeling, current numerical spine models generally either neglect the active musculature or oversimplify the passive structural function of the spine. However, the internal loading conditions of the spine are complex and greatly influenced by muscle forces. It is often unclear whether the assumptions made provide realistic results. To improve the prediction of realistic loading conditions in both conservative and surgical treatments, we modified a previously validated forward dynamic musculoskeletal model of the intact lumbosacral spine with a muscle-driven approach in three scenarios. These exploratory treatment scenarios included an extensible lumbar orthosis and spinal instrumentations. The latter comprised bisegmental internal spinal fixation, as well as monosegmental lumbar fusion using an expandable interbody cage with supplementary posterior fixation. The biomechanical model responses, including internal loads on spinal instrumentation, influences on adjacent segments, and effects on abdominal soft tissue, correlated closely with available in vivo data. The muscle forces contributing to spinal movement and stabilization were also reliably predicted. This new type of modeling enables the biomechanical study of the interactions between active and passive spinal structures and technical systems. It is, therefore, preferable in the design of medical devices and for more realistically assessing treatment outcomes. Full article

Elbow injuries are the second most common upper extremity fractures and often require invasive and costly surgical treatments. To explore a non-surgical alternative, we present the development and evaluation of a custom 3D-printed static orthosis made from polylactic acid (PLA) designed using 3D scanning, CAD software-based modeling and material characterization, showing promise for broader application in similar elbow injuries. Full article

Purpose: The effectiveness of the customised solutions compared to the conventional ones and the emergence of advanced production technologies, such as Additive Manufacturing (AM) techniques, strengthened the trend towards an enhanced individualization of the clinical treatments. In the present research, the value of topological optimisation (TO) in the manufacturing process of tailor-made orthopaedic appliance (upper-limb orthosis) was analysed. Methodology: From the morphology of a patient’s arm, orthotic models were developed. Nonparametric optimization (Simulia Tosca) was performed, based on the Finite Element Analysis (FEA) program (Abaqus), and contributed to the development of TO orthotic models with diverse levels of volume reduction fraction. The modelling and manufacturing framework for customising orthotic solutions was evaluated with a discussion on the feasibility of lightweight and high-performance products, encompassing production time and cost. Pilot products were produced with a Material Extrusion (MEX) printer. Findings: TO proved to be a practical and valuable approach for the advanced customisation of orthopaedic devices, offering lightweight solutions able to withstand stresses also during patient rehabilitation and remission. From the rapid prototyping perspective, specific strategies must be adopted to prevent the escalation of production costs and time. Originality: The research delves into the overall benefit of implementing an advanced modelling technique within the context of manufacturing highly customised orthoses, analysing how TO activity impacts the rapid prototyping process. Beyond product evaluation, the analysis explores broader implications, including the assessment of feasibility and the development of strategies for integrating the approach into clinical workflows and hospital settings. Full article

Distal radius fractures (DRF) are one of the most prevalent injuries a person may sustain. The current treatment of DRF involves the use of casts made from Plaster of Paris or fiberglass. The application of these materials is a serious endeavor that influences their intended use, and should be conducted by specially trained personnel. In this research, with the use of the full-body 3D scanner Vitus Smart, 3D modelling software Rhinoceros 3D, and 3D printer Creality CR-10 max, an easy, yet effective workflow of orthosis fabrication was developed. Furthermore, samples that represent segments of the orthosis were subjected to static loading. Lastly, fragments that occurred due to excessive force were characterized with the use of a digital microscope. It was observed that with the implementation of the designed workflow, a faster 3D printing process was present. Samples subjected to mechanical loading had values that exceeded those of conventional Plaster of Paris; the minimum recorded value was 681 N, while the highest was 914 N. Microscopic characterization enabled a clear insight into the occurrence of fragments, as well as their potential risk. Therefore, in this research, an insight into different stages of fabrication, characterization of undesirable events, as well as the risks they may pose were presented. Full article

Background: Patient compliance is a major concern of hand orthosis in first carpometacarpal osteoarthritis. To address this issue, we established a method for creating a custom-made three-dimensional printed splint based on computed tomography. This prospective study evaluates the usefulness of the three-dimensional printed splint compared with the conventional splint. Methods: A total of 12 hands in nine patients were included. The mean age of the patients was 69 years (range: 58–84). Conventional orthoses were made by prosthetists using molds. Three-dimensional printed orthoses (long and short types) were digitally designed from computed tomography data and created using Fused Deposition Modeling. Subjects were instructed to use three types of orthoses for 2 weeks each. They completed questionnaires that indicated pain, function, percentage of daytime spent using the orthosis, satisfaction score, and discomfort caused by wearing orthoses. Results: The pain on motion showed an improvement of approximately 20% for all orthoses. There was no significant difference in pain scale, function, percentages of daytime spent using each orthosis, and satisfaction score among the three types of orthoses. Discomfort caused by wearing orthosis was more frequent in conventional orthosis than in 3D-printed orthosis, and there was a significant difference between the conventional type and the long-type 3D-printed orthosis. Conclusions: This study suggests that 3D-printed splints provide comparable pain relief to conventional splints with reduced discomfort. However, limitations such as small sample size, short follow-up, and reliance on CT imaging highlight the need for further research. Full article

Background/Objectives: This paper proposes a method for managing gait imbalances by integrating the Internet of Things (IoT) and machine learning technologies. Ankle–foot orthosis (AFO) devices are crucial medical braces that align the lower leg, ankle, and foot, offering essential support for individuals with gait imbalances by assisting weak or paralyzed muscles. This research aims to revolutionize medical orthotics through IoT and machine learning, providing a sophisticated solution for managing gait issues and enhancing patient care with personalized, data-driven insights. Methods: The smart ankle–foot orthosis (AFO) is equipped with a surface electromyography (sEMG) sensor to measure muscle activity and an Inertial Measurement Unit (IMU) sensor to monitor gait movements. Data from these sensors are transmitted to the cloud via fog computing for analysis, aiming to identify distinct walking phases, whether normal or aberrant. This involves preprocessing the data and analyzing it using various machine learning methods, such as Random Forest, Decision Tree, Support Vector Machine (SVM), Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and Transformer models. Results: The Transformer model demonstrates exceptional performance in classifying walking phases based on sensor data, achieving an accuracy of 98.97%. With this preprocessed data, the model can accurately predict and measure improvements in patients’ walking patterns, highlighting its effectiveness in distinguishing between normal and aberrant phases during gait analysis. Conclusions: These predictive capabilities enable tailored recommendations regarding the duration and intensity of ankle–foot orthosis (AFO) usage based on individual recovery needs. The analysis results are sent to the physician’s device for validation and regular monitoring. Upon approval, the comprehensive report is made accessible to the patient, ensuring continuous progress tracking and timely adjustments to the treatment plan. Full article

This study proposes a wearable gait assessment method using inertial measurement units (IMUs) to evaluate gait ability in daily environments. By focusing on the estimation of the margin of stability (MoS), a key kinematic stability parameter, a method using a convolutional neural network, was developed to estimate the MoS from IMU acceleration time-series data. The relationship between MoS and other stability indices, such as the Lyapunov exponent and the multi-site time-series (MSTS) index, using data from five IMU sensors placed on various body parts was also examined. To simulate diverse gait conditions, treadmill speed was varied, and a knee–ankle–foot orthosis was used to restrict left knee extension, inducing gait asymmetry. The model achieved over 90% accuracy in classifying MoS in both forward and lateral directions using three-axis acceleration data from the IMUs. However, the correlation between MoS and the Lyapunov exponent or MSTS index was weak, suggesting that these indices may capture different aspects of gait stability. Full article

Additive manufacturing (AM) has emerged as one of the core components of Industry 4.0, which allows extreme customization of products and offers the full potential of design freedom. Recently, AM has been utilized to manufacture patient-specific, customized assistive devices in the field of rehabilitation. This work presents the design and development process of customized wrist-hand orthosis (WHO) elaborately using AM technology. The main focus of this research was to perform an experimental evaluation of the WHO devices for fitting and customer satisfaction. In this case, the primary anatomic measurement of the wrist was obtained using an infrared-based 3D scanner. Then a 3D model of the orthotic device was prepared in nTop using different lattice parameters. Finally, these devices were additively manufactured in liquid crystal display (LCD) 3D resin printers for superior surface quality. To maximize user comfort and mechanical robustness at the same time, mechanical tests were simulated to assess the WHO’s mechanical characteristics and confirm its operation during typical hand activities. Full article