Search Results (1,082)

Companies increasingly implement exoskeletons in their production lines to reduce musculoskeletal disorders. Studies have been conducted on the general ergonomic effects of exoskeletons in production environments; however, it remains challenging to predict the biomechanical effects these devices may have in specific jobs. This article proposes the parametric modeling of an active lumbar exoskeleton using the Forces ergonomic method, which calculates the ergonomic risk using motion capture in the workplace, considering the internal joint forces. The exoskeleton was studied to model it in the Forces method using a four-phase approach based on experimental observations (Phase 1) and objective data collection via motion capture with inertial sensors and load cells for lifting load movements. From the experimentation the angles of each body segment, the effort perceived by the user, and the activation conditions were obtained (Phase 2). After modeling development (Phase 3), the experimental results regarding the force and risk were evaluated obtaining differences between model and experimental data of 0.971 ± 0.171 kg in chest force and 1.983 ± 0.678% in lumbar risk (Phase 4). This approach provides a tool to evaluate the biomechanical effects of this device in a work task, offering a parametric and direct approximation of the effects prior to implementation. Full article

The application of lower-limb exoskeleton robots in rehabilitation is becoming more prevalent, where the precision of control and the speed of response are essential for effective movement tracking. This study tackles the challenge of optimizing both control accuracy and response speed in trajectory tracking for lower-limb exoskeleton hip robots. We introduce an optimization strategy that integrates the Sparrow Search Algorithm (SSA) with fuzzy Proportional-Integral-Derivative (PID) control. This approach addresses the inefficiencies and time-consuming process of manual parameter tuning, thereby improving trajectory tracking performance. First, recognizing the complexity of hip joint motion, which involves multiple degrees of freedom and intricate dynamics, we employed the Lagrangian method. This method is particularly effective for handling nonlinear systems and simplifying the modeling process, allowing for the development of a dynamic model for the hip joint. The SSA is subsequently utilized for the online self-tuning optimization of both the proportional and quantization factors within the fuzzy PID controller. Simulation experiments confirm the efficacy of this strategy in tracking hip joint trajectories during flat walking and standing hip flexion rehabilitation exercises. Experimental results from diverse test populations demonstrate that SSA-fuzzy PID control improves response times by 27.8% (for flat walking) and 30% (for standing hip flexion) when compared to traditional PID control, and by 6% and 2%, respectively, relative to fuzzy PID control. Regarding tracking accuracy, the SSA-fuzzy PID approach increases accuracy by 81.4% (for flat walking) and 80% (for standing hip flexion) when compared to PID control, and by 57.5% and 56.8% relative to fuzzy PID control. The proposed strategy significantly improves both control accuracy and response speed, offering substantial theoretical support for rehabilitation training in individuals with lower-limb impairments. Moreover, in comparison to existing methods, this approach uniquely tackles the challenges of parameter tuning and optimization, presenting a more efficient solution for trajectory tracking in exoskeleton systems. Full article

This systematic review explores recent advancements in control methods for rehabilitation exoskeleton robots, which assist individuals with motor impairments through guided movement. As robotics technology progresses, precise, adaptable, and safe control techniques have become accessible for effective human–robot interaction in rehabilitation settings. Key control methods, including computed torque and adaptive control, excel in managing complex movements and adapting to diverse patient needs. Robust and sliding mode controls address stability under unpredictable conditions. Traditional approaches, like PD and PID control schemes, maintain stability, performance, and simplicity. In contrast, admittance control enhances user–robot interaction by balancing force and motion. Advanced methods, such as model predictive control (MPC) and Linear Quadratic Regulator (LQR), provide optimization-based solutions. Intelligent controls using neural networks, Deep Learning, and reinforcement learning offer adaptive, patient-specific solutions by learning over time. This review provides an in-depth analysis of these control strategies by examining advancements in recent scientific literature, highlighting their potential to improve rehabilitation exoskeletons, and offering future recommendations for greater efficiency, responsiveness, and patient-centered functionality. Full article

Background: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians. Methods: This paper describes the early findings of the NeuroExo brain–machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users’ compliance and system performance. Results: Participants achieved a compliance rate between $21\%$ and $100\%$, with an average of $69\%$, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02). Conclusions: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible. Full article

Exoskeletons play a crucial role in joint healthcare by providing targeted support and rehabilitation for individuals with musculoskeletal diseases. As an assistive device, the accurate monitoring of the user’s joint signals and exoskeleton status using wearable sensors is essential to ensure the efficiency of conducting complex tasks in various scenarios. However, balancing sensitivity and stretchability in wearable devices for exoskeleton applications remains a significant challenge. Here, we introduce a wearable strain sensor for detecting finger and knee joint motions. The sensor utilizes a stretchable elastic conductive network, incorporating multi-walled carbon nanotubes (MWCNTs) into Ecoflex. The concentration of MWCNTs has been meticulously optimized to achieve both a high gauge factor (GF) and stability. With its high sensitivity, the sensor is enabled to be applied in the angle monitoring of finger joints. By integrating the sensor with human knee joints and an exoskeleton device, it can simultaneously detect the flexion and extension movements in real-time. This sensor holds significant potential for enhancing exoskeleton performance and improving joint healthcare technologies. Full article

This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader–follower paradigms seen in today’s systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application. Full article

Wearing an exoskeleton, the human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities such as manual material handling. Currently, exoskeleton systems are commonly integrated with sensor technologies to gather data and assess performances. This is mainly performed to evaluate the physical exoskeletons, and cannot provide real-time feedback during the development phase. Firstly, a powered wearable elbow exoskeleton with variable stiffness is proposed. Through theoretical calculation, the power efficiency formula of exoskeleton is derived. Then, a human musculoskeletal model is built using the AnyBody Modeling System and coupled to the elbow exoskeleton. Under set experimental conditions, the simulation reveals that, when compared with the exoskeleton, the biceps and triceps muscle force parameters of the human model were reduced by 24% and 12%. The muscle activity was diminished by 28–31%, and muscle length shortened by about 6%, in comparison to the condition without the exoskeleton. Finally, through the muscle force experiment, it was verified that the power efficiency of the elbow exoskeleton in the real transport was about 18%. The project reduces costs in the development phase of the exoskeleton and can provide new insights into muscle function and sports biomechanics. Full article

Glycoside hydrolase family 18 (GH18) proteins can hydrolyze the β-1,4-glycosidic bonds of chitin, which is a common structure component of insect exoskeletons and fungal cell walls. In this study, 36 GH18 genes were identified and subjected to bioinformatic analysis based on the genomic data of Glycine max. They were distributed in 16 out of 20 tested soybean chromosomes. According to the amino acid sequences, they can be further divided into five subclades. Class III chitinases (22 members) and class V chitinases (6 members) are the major two subclades. The amino acid size of soybean GH18 proteins ranges from 173 amino acids (aa) to 820 aa and the molecular weight ranges from 19.46 kDa to 91.01 kDa. From an evolutionary perspective, soybean GH18 genes are closely related to Medicago (17 collinear loci with soybean) and Lotus (23 collinear loci with soybean). Promoter analysis revealed that GH18 genes could be induced by environmental stress, hormones, and embryo development. GmGH18-15, GmGH18-24, and GmGH18-33 were screened out due to their nodulation specific expression and further verified by RT-qPCR. These results provide an elaborate reference for the further characterization of specific GH18 genes, especially during nodule formation in soybean. Full article

This article investigates a cable-driven experimental setup to simulate elbow joint assistance in the sagittal plane provided by an exosuit. Cable-driven exosuits, particularly fabric-based designs, significantly enhance rehabilitation by enabling targeted joint exercises and promoting functional recovery. To achieve an optimal design, these devices require an analysis of the cable tension, reaction forces, and moments and their dependency on the anchor position. This study presents a cable-driven experimental setup with two rigid bars and variable anchor positions, designed to mimic the human forearm, upper arm, and elbow joint, to evaluate the performance of a potential cable-driven exosuit. Based on the experimental setup, a static model was developed to validate the measured cable tension and estimate the reaction force at the joint and the moments at the anchor positions. Furthermore, based on the observations, an optimization problem was defined to identify optimal anchor positions to improve the exosuit’s design. The optimal position on the forearm and upper arm is studied between 15% and 50% distance from the elbow joint. Our findings suggest that prioritizing user comfort requires both anchor points to be as far away from the elbow joint as possible, i.e., 50% distance, whereas, for optimal exosuit performance, the forearm anchor position can be adjusted based on the joint angle while keeping the upper arm anchor position at the farthest point. The findings in the current work can be used to decide the anchor point position for designing an elbow exosuit. Full article

Restoring strength, function, and mobility following an illness, accident, or surgery is the primary goal of upper arm rehabilitation. Exoskeletons offer adaptable support, enhancing patient engagement and accelerating recovery. This work proposes an adjustable, wearable robotic exoskeleton powered by electromyography (EMG) data for upper arm rehabilitation. Three activation levels—low, medium, and high—were applied to the EMG data to forecast the Pulse Width Modulation (PWM) based on the range of motion (ROM) angle. Conventional machine learning (ML) models, including K-Nearest Neighbor Regression (K-NNR), Support Vector Regression (SVR), and Random Forest Regression (RFR), were compared with neural network approaches, including Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) to determine the best ML model for the ROM angle prediction. The LSTM model emerged as the best predictor with a high accuracy of 0.96. The system achieved 0.89 accuracy in exoskeleton control and 0.85 accuracy in signal categorization. Additionally, the proposed exoskeleton demonstrated a 0.97 performance in ROM correction compared to conventional methods (p = 0.097). These findings highlight the potential of EMG-based, LSTM-enabled exoskeleton systems to deliver accurate and adaptive upper arm rehabilitation, particularly for senior citizens, by providing personalized and effective support. Full article

To assist patients with lower limb dysfunction in mobility, standing, and walking, this paper proposes a novel device that integrates the functions of lower limb exoskeleton, wheelchair, and sit-to-stand (STS) transfer assistance. We designed a 10-degree-of-freedom lower limb exoskeleton based on gait analysis. To satisfy human–machine compatibility, the hip joint was conceptualized as a remote center-of-motion (RCM) mechanism, the knee joint was developed as a cam mechanism, and the ankle joint was designed as a revolute pair. We constructed a kinematic model of the exoskeleton by adopting the product-of-exponential (POE) formula. The STS transfer assistance mechanism was designed based on Stephenson III six-bar linkage through path synthesis methods. The length of this six-bar mechanism was determined based on using Newton–Jacobi iterative techniques. We connected the STS assistive mechanism to the wheelchair frame, and then, we connected the exoskeleton to the STS transfer assistive mechanism. The experimental results demonstrated that the STS assistance path aligned closely with human buttock trajectories, and the walking assistance paths corresponded with natural human gaits. This device produces a new choice for patients with lower limb dysfunction. Full article

Electromyography (EMG) signals are fundamental in neurorehabilitation as they provide a non-invasive means of capturing the electrical activity of muscles, enabling precise detection of motor intentions. This capability is essential for controlling assistive devices, such as therapeutic exoskeletons, that aim to restore mobility and improve motor function in patients with neuromuscular impairments. The integration of EMG into neurorehabilitation systems allows for adaptive and patient-specific interventions, addressing the variability in motor recovery needs. However, achieving the high fidelity, low latency, and robustness required for real-time control of these devices remains a significant challenge. This paper introduces a novel multi-channel electromyography (EMG) acquisition system implemented on a System-on-Chip (SoC) architecture for robotic neurorehabilitation. The system employs the Zynq-7000 SoC, which integrates an Advanced RISC Machine (ARM) processor, for high-level control and an FPGA for real-time signal processing. The architecture enables precise synchronization of up to eight EMG channels, leveraging high-speed analog-to-digital conversion and advanced filtering techniques implemented directly at the measurement site. By performing filtering and initial signal processing locally, prior to transmission to other subsystems, the system minimizes noise both through optimized processing and by reducing the distance to the muscle, thereby significantly enhancing the signal-to-noise ratio (SNR). A dedicated communication interface ensures low-latency data transfer to external controllers, crucial for adaptive control loops in exoskeletal applications. Experimental results validate the system’s capability to deliver high signal fidelity and low processing delays, outperforming commercial alternatives in terms of flexibility and scalability. This implementation provides a robust foundation for real-time bio-signal processing, advancing the integration of EMG-based control in neurorehabilitation devices. Full article

Exoskeletons can track the wearer’s movements in real time, thereby enhancing physical performance or restoring mobility for individuals with gait impairments. These wearable assistive devices have demonstrated significant potential in both rehabilitation and industrial applications. This review focuses on the major advancements in exoskeleton technology published since 2020, with particular emphasis on the development of structural designs for lower-limb exoskeletons employed in locomotion assistance. We employed a systematic literature review methodology, categorizing the included studies into three main types: rigid exoskeleton, soft exoskeleton, and tethered platform. The current development status of robotic exoskeletons is analyzed based on publication year, system weight, target assistive joints, and main effects. Furthermore, we examine the factors driving these advancements and their implications for the field. The key challenges and opportunities that may influence the future development of exoskeleton technologies are also highlighted in this review. Full article

Impairment of upper limb function is common after a stroke and is closely linked to decreased functional independence in activities of daily living. Robot-assisted training has been used in clinical settings to improve hand function in stroke patients; however, many existing devices are costly and require specialized training to operate. This study aimed to propose a novel powered hand exoskeleton (EO) and verify its effectiveness on upper extremity function in people with chronic stroke. Thirty participants were randomly assigned to either the experimental group or the control group. Each participant underwent 30 min interventions twice a week for 8 weeks. The experimental group received 15 min of conventional therapy followed by 15 min of training with the powered hand EO, while the control group received 30 min of conventional therapy. The primary outcome measures included the Fugl-Meyer Assessment for upper extremity function (FMA-UE), the Box and Block Test (BBT), and handgrip dynamometer. Assessments were conducted at baseline and then at 4-week intervals throughout the 8-week period. Results showed that, after the 8-week intervention, the average changes in FMA-UE scores for the experimental group were significantly greater than those for the control group (p < 0.01). A clear upward trend in both FMA-UE and BBT scores was observed in the EO group. Statistical analysis revealed significant improvements in the overall, proximal, and distal components of the FMA-UE scores (all p < 0.01) and in BBT scores (both p < 0.05) in the EO group compared to the control group at 4 and 8 weeks, respectively. However, no significant differences in grip strength were observed between the groups at either time point. Our findings suggest that the proposed powered hand EO is both feasible and safe for training the impaired hand in stroke survivors. Given the characteristics of the device, it has potential for use in hand rehabilitation aimed at regaining upper extremity function. Full article

Cuticular hydrocarbons (CHCs) are long-chain lipids found on the exoskeletons of insects, serving primarily as a protective barrier against water loss and environmental factors. In the last few decades, the qualitative and quantitative analysis of CHCs, particularly in blow flies, has emerged as a valuable tool in forensic entomology, offering promising potential for species identification and age estimation of forensically important insects. This review examines the current application of CHC analysis in forensic investigations and highlights the significant advancements in the field over the past few years. Studies have demonstrated that CHC profiles vary with insect development, and while intra-species variability exists due to factors such as age, sex, geographical location, and environmental conditions, these variations can be harnessed to refine post-mortem interval (PMI) estimations and improve the accuracy of forensic entomological evidence. Notably, CHC analysis can also aid in distinguishing between multiple generations of insects on a body, providing insights into post-mortem body movement and aiding in the interpretation of PMI in complex cases. Furthermore, recent studies have investigated the variability and degradation of CHCs over time, revealing how environmental factors—such as temperature, humidity, UV light exposure, and toxicological substances—affect CHC composition, providing valuable insights for forensic investigations. Despite the promise of CHC profiling, several challenges remain, and this review also aims to highlight future research directions to enhance the reliability of this technique in forensic casework. Full article