Myoelectric Research Articles

Closed Loop Subject-Independent SSVEP Frequency Detection System Using CCA Features and Ensemble Learning Methods

Purpose: In recent years, the use of Steady-State Visual Evoked Potentials (SSVEPs) in Brain-Computer Interface (BCI) systems has dramatically increased across several fields, such as rehabilitation, cognitive impairment, and brain disease or disorder detection, as well as artificial limbs, wheelchairs, and biomechanical systems. In this study, a novel method is proposed to help scientists develop more efficient BCI systems for Machine Learning Operations (MLOps). This study proposed a state-of-the-art method for detecting SSVEP-based stimulation frequencies with statistical models to design an optimal BCI system. Materials and Methods: In this study, the Canonical Correlation Analysis (CCA) method has been implemented to extract features from the accessible-to-the-public Tsinghua University Benchmark dataset. A limited number of subjects are being studied. After completing feature selection methods and selecting the best subset of features using a specified feature selection method, the classification of the best features using machine learning-based classification methods has been completed. Furthermore, it is assumed that scientists will design and implement a system specifically for subjects. Models work for subjects independently. However, because model training is subject-specific, we must execute the proposed methods on each subject separately. Results: The findings indicate that the novel suggested BCI system achieves an average accuracy of 83±9% in stimulation detection, which is higher than that of the traditional CCA approach with an accuracy of 80±11% (p<0.05). Conclusion: Based on the findings, we demonstrated an increase in accuracy with the novel method. It was also discovered that by using the proposed techniques, it is possible to keep MLOps systems as an advantage.

Time series generative adversarial network for muscle force prognostication using statistical outlier detection

AbstractMachine learning approaches, such as artificial neural networks (ANN), effectively perform various tasks and provide new predictive models for complicated physiological systems. Examples of Robotics applications involving direct human engagement, such as controlling prosthetic arms, athletic training, and investigating muscle physiology. It is now time for automated systems to take over modelling and monitoring tasks. However, there is a problem with the massive amount of time series data collected to build accurate forecasting systems. There may be inconsistencies in forecasting muscle forces due to the enormous amount of data. As a result, anomaly detection techniques play a significant role in detecting anomalous data. Detecting anomalies can help reduce redundancy and free up large storage space for storing relevant time‐series data. This paper employs several anomaly detection techniques, including Isolation Forest (iforest), K‐Nearest Neighbour (KNN), Open Support Vector Machine (OSVM), Histogram, and Local Outlier Factor (LOF). These techniques have been used by Long Short‐Term Memory (LSTM), Auto‐Regressive Integrated Moving Average (ARIMA), and Prophet models. The dataset used in this study contained raw measurements of body movements (kinematics) and the forces generated during walking (kinetics) of 57 healthy people (29 Female, 28 Male) without walking abnormalities or recent leg injuries. To increase the data samples, we used TimeGAN that generates synthetic time series data with temporal dependencies, aiding in training robust predictive models for muscle force prediction. The results are then compared with different evaluation metrics for five different samples. It is found that anomaly detection techniques with LSTM, ARIMA, and Prophet models provided better performance in forecasting muscle forces. The iforest method achieved the best Pearson's Correlation Coefficient (r) of 0.95, which is a competitive score with existing systems that perform between 0.7 and 0.9. The methodology provides a foundation for precision medicine, enhancing prognostic capability over relying solely on population averages.

Fatigue Analysis of Prosthetic Osseointegration with a Novel Protection System

Recent advancements in osseointegration technology offer a promising alternative to traditional prosthetic limbs, leading to improved comfort and functionality for amputees. However, the primary concern lies in the risk of catastrophic failure and falling due to fractures within the osseointegration system. To address this challenge, a protective system has been designed. The new design includes an internally rod part with a notch at the bottom to ensure failure at that point, made of ductile cast iron. A cover made of Ti6Al4V alloy is placed to ensure fracture isolation within the protection nail during sudden loading, preventing unexpected falls. After conducting tensile tests for the mentioned alloys and materials, and then relying on the ground reaction force in the numerical analysis using SolidWorks software, Finite Element Analysis (FEA) simulations confirmed the effectiveness of this design, with the expected fracture point occurring in the protection nail after exceeding 54973 walking cycles, surpassing the expected maintenance lifespan of the prosthetic limb. This design represents a significant step forward in enhancing the safety and reliability of integrated prosthetic limbs, ultimately leading to improved mobility and peace of mind for amputees.Recent advancements in osseointegration technology offer a promising alternative to traditional prosthetic limbs, leading to improved comfort and functionality for amputees. However, the primary concern lies in the risk of catastrophic failure and falling due to fractures within the osseointegration system. To address this challenge, a protective system has been designed. The new design includes an internally rod part with a notch at the bottom to ensure failure at that point, made of ductile cast iron. A cover made of Ti6Al4V alloy is placed to ensure fracture isolation within the protection nail during sudden loading, preventing unexpected falls. After conducting tensile tests for the mentioned alloys and materials, and then relying on the ground reaction force in the numerical analysis using SolidWorks software, Finite Element Analysis (FEA) simulations confirmed the effectiveness of this design, with the expected fracture point occurring in the protection nail after exceeding 54973 walking cycles, surpassing the expected maintenance lifespan of the prosthetic limb. This design represents a significant step forward in enhancing the safety and reliability of integrated prosthetic limbs, ultimately leading to improved mobility and peace of mind for amputees.

Comprehensive rehabilitation model of children in need of upper limb prosthetics due to congenital diseases

Rehabilitation in the prosthetics process can significantly enhance patients’ functional abilities and their skill in managing prostheses. Nevertheless, most international rehabilitation recommendations for prosthetic users are aimed at adults. Prior to this research, Russian scientific studies had not proposed a comprehensive model that would integrate current scientific knowledge on the rehabilitation of children needing upper limb prosthetics due to congenital diseases.The aim of the study to develop an organizational-functional model of phased interdisciplinary medical rehabilitation for children requiring upper limb prosthetics due to congenital diseases.Materials and methods. Organizational-functional modeling based on the analysis of regulatory legal acts, scientific research, and guidelines for the medical rehabilitation of children with upper limb peculiarities.Results. The organizational-functional model of rehabilitation includes 3 stages: preparation for prosthetics, rehabilitation upon receiving the prosthetics, and family support during the use of the prosthetics. The main goals of each stage are developed, specialists responsible for solving specific tasks at each stage are identified, and organizations (divisions) responsible for the implementation of each stage are determined. Methodological materials for the parents of children from the target group have been created. Materials aim at optimizing interaction with families and their psychological support. A scheme for remote family support throughout the entire period of prosthetic use has been developed.Conclusion. The developed organizational-functional model of phased interdisciplinary medical rehabilitation for children needing upper limb prosthetics due to congenital diseases is designed to ensure adherence to the fundamental principles of rehabilitation – early start, phased approach, continuity, interdisciplinarity, individualized approach, as well as active family involvement in the rehabilitation process.

Multi-objective optimization of prosthetic multi-cells foam liner

Restoring mobility using prosthetic limbs anchored in custom sockets is the primary goal of amputee rehabilitation. The contact pressure at the stump-liner interface is critical for improving prosthetic socket design and enhancing amputee satisfaction and comfort. This study aimed to develop and optimize a new customized multicellular foam liner model by identifying the optimal configuration of the liner’s cells to achieve minimal contact pressure between the transtibial limb (stump) and the prosthetic interface while simultaneously minimizing the stump’s immersion in the foam liner. The study employed a multi-objective optimization using the NSGA-II genetic algorithm to optimize the material configurations of the liner cells. The objective function was based on a finite element (FE) model of the customized foam liner, composed of 40 cells, each using one of six foam materials with varying firmness. The optimization process yielded nine different configurations representing the Pareto front. Contact pressure and immersion, represented by displacement in the load direction, were reduced from 140 KPa and 9 mm to 10 KPa and less than 1 mm, respectively. The results obtained in this study motivate future clinical trials on the efficacy of customized multicellular foam liners. These findings provide in-depth insights into prosthesis design and customization, potentially leading to further development in the use of foam rather than silicone for liner manufacturing.

Three-dimensional printing technology applied to the production of prosthesis: A systemic narrative review.

The purpose of this scoping review was to investigate the effects of 3-dimensional (3D)-printed prostheses. Articles published up to August 19, 2023, were searched in the PubMed, Cochrane Library, Embase, and Scopus databases. The search terms used were "3D printed prosthesis," "3D printed prostheses," "3D printed prosthe*," "3D printed artificial arm," "3D printed artificial leg," "3D printing prosthesis," "3D printing prostheses," "3D printing prosthe*," "3D printing artificial arm," and "3D printing artificial leg." This review included studies that applied 3D-printed prostheses to upper- or lower-limb amputees. Case reports, conference abstracts, presentations, reviews, and unidentified articles were excluded from the analysis. A total of 937 articles were identified, 11 of which were included after confirming eligibility through the title, abstract, and full text. The results indicated that the 3D-printed prostheses demonstrated the ability to substitute for the functions of impaired limbs, similar to conventional prostheses. Notably, the production cost and weight were reduced compared with those of conventional prostheses, increasing patient satisfaction. The use of 3D-printed prostheses is expected to gain prominence in future clinical practice. However, concerns regarding the durability of 3D-printed prostheses have increased among users. Therefore, there is an ongoing need to explore highly durable materials that can withstand the weight of the user without breaking easily. In addition, advancements are required in technologies that enable the depiction of various skin tones and the production of smaller-sized prostheses suitable for clothing.

Determining the optimal leading limb for gait initiation in unilateral transtibial amputees: A systematic review.

The selection of the leading limb during gait initiation in individuals with unilateral transtibial amputations can significantly affect various biomechanical parameters. However, there is currently no established recommendation for the suitable leading limb in this population. The systematic review was aimed to propose a preferred leading limb for gait initiation in individuals with unilateral transtibial amputations based on biomechanical parameters. Databases including Google Scholar, PubMed, Science Direct, and ISI Web of Knowledge, were searched. The first selection criterion was based on abstracts and titles to address the research question. A total of seven studies were included in this review, and the Downs and Black's checklist was used by three researchers to assess the risk of bias. The review included a total of 61 adults with unilateral transtibial amputations, with a mean age range of 41 to 64.43 years. The confidence level of the included studies was poor, and the observational cohort was the most common study design (n = 5). Most of the studies were not replicable. Four of the included studies recommended the prosthetic limb as the preferred leading limb. Individuals with unilateral transtibial amputations may experience biomechanical benefits, including a more normal center of pressure path, reduced limb loading, and increased ankle energy generation, when leading with their prosthetic limb during gait initiation. However, further research is necessary to establish a more conclusive recommendation for the preferred leading limb in this population.

Multiplexing intensity and frequency sensations for artificial touch by modulating temporal features of electrical pulse trains.

In neural prostheses, intensity modulation of a single channel (i.e., through a single stimulating electrode) has been achieved by increasing the magnitude or width of each stimulation pulse, which risks eliciting pain or paraesthesia; and by changing the stimulation rate, which leads to concurrent changes in perceived frequency. In this study, we sought to render a perception of tactile intensity and frequency independently, by means of temporal pulse train patterns of fixed magnitude, delivered non-invasively. Our psychophysical study exploits a previously discovered frequency coding mechanism, where the perceived frequency of stimulus pulses grouped into periodic bursts depends on the duration of the inter-burst interval, rather than the mean pulse rate or periodicity. When electrical stimulus pulses were organised into bursts, perceived intensity was influenced by the number of pulses within a burst, while perceived frequency was determined by the time between the end of one burst envelope and the start of the next. The perceived amplitude was modulated by 1.6× while perceived frequency was varied independently by 2× within the tested range (20-40 Hz). Thus, the sensation of intensity might be controlled independently from frequency through a single stimulation channel without having to vary the injected electrical current. This can form the basis for improving strategies in delivering more complex and natural sensations for prosthetic hand users.

HAND GRASPS RECOGNITION USING SINGLE-CHANNEL TRANSIENT-STATE SURFACE ELECTROMYOGRAPHY

The need to perform large hand-grasping strategies in daily life has promoted the development of robotic hands and prosthetic hands that mimic natural ones. This work presents a system for the detection of 10 basic hand grasps in healthy subjects based on single-channel transient-state Surface Electromyography (sEMG) signals. The studied grasps are large diameter, ring, knife index finger, writing tripod, power sphere, precision sphere, prismatic pinch, lateral, extension type, and parallel extension grasp. Signals were collected from 10 healthy volunteers using three sEMG electrodes connected to an Electromyography (EMG) shield using MATLAB as data acquisition software. The signals were classified using three different classifiers, i.e. [Formula: see text]-Nearest Neighbors ([Formula: see text]-NN), Diagonal Linear Discriminant Analysis (dl-DA), and Random Forest (RF). The used features were mean absolute value, integrated absolute value, variance, root mean square value, waveform length, Willison amplitude, slope sign change, zero crossing, autoregressive coefficients, and mean frequency. The results show that good classification accuracy is achieved when classifying a reduced number of grasps using a single-channel transient-state sEMG. The average accuracy for the different classifiers varied from 41.5% to 53.3% when classifying 10 grasps and from 78.4% to 91.9% when classifying five grasps using two features as a trainer. It is found that transient-state sEMG signals are efficient in the detection of different hand grasps, reducing the time to generate control signals and the number of channels.

Topology of surface electromyogram signals: hand gesture decoding on Riemannian manifolds

Objective. Decoding gestures from the upper limb using noninvasive surface electromyogram (sEMG) signals is of keen interest for the rehabilitation of amputees, artificial supernumerary limb augmentation, gestural control of computers, and virtual/augmented realities. We show that sEMG signals recorded across an array of sensor electrodes in multiple spatial locations around the forearm evince a rich geometric pattern of global motor unit (MU) activity that can be leveraged to distinguish different hand gestures. Approach. We demonstrate a simple technique to analyze spatial patterns of muscle MU activity within a temporal window and show that distinct gestures can be classified in both supervised and unsupervised manners. Specifically, we construct symmetric positive definite covariance matrices to represent the spatial distribution of MU activity in a time window of interest, calculated as pairwise covariance of electrical signals measured across different electrodes. Main results. This allows us to understand and manipulate multivariate sEMG timeseries on a more natural subspace—the Riemannian manifold. Furthermore, it directly addresses signal variability across individuals and sessions, which remains a major challenge in the field. sEMG signals measured at a single electrode lack contextual information such as how various anatomical and physiological factors influence the signals and how their combined effect alters the evident interaction among neighboring muscles. Significance. As we show here, analyzing spatial patterns using covariance matrices on Riemannian manifolds allows us to robustly model complex interactions across spatially distributed MUs and provides a flexible and transparent framework to quantify differences in sEMG signals across individuals. The proposed method is novel in the study of sEMG signals and its performance exceeds the current benchmarks while being computationally efficient.

A bioinspired neuromuscular system enabled by flexible electro-optical N2200 nanowire synaptic transistor

Mimicking the functional traits of the muscle system evolves the development of the neuromorphic prosthetic limbs. Herein, a bioinspired neuromuscular system was constructed by connecting an information processor that uses a flexible electro-optical synaptic transistor (FNST) to an effector that uses artificial muscle fibers. In this system, the response of artificial muscle fibers, which imitate the movement of biological muscle fibers, is manipulated by neuromorphic synaptic devices. The FNST is regulated by light pulses and electrical spikes to emulate biological synaptic functions, and thereby applied in secure communication. The feasibility of n-type organic nanowires acting as the channels for neuromorphic devices was demonstrated. Attributing to the flexibility of the n-type organic semiconductor N2200 nanowires, the current of the FNST retains >85% of its initial value after the 5000 bending cycles to radius = 1 cm. The tolerance of bending of the FNST implies its potential applications in wearable electronics. This work offers an approach to potentially advancing electronic skin, neuro-controlled robots, and neuromorphic prosthetic limbs.

Dynamic gait stability and stability symmetry for people with transfemoral amputation: A case-series of 19 individuals with bone-anchored limbs

For some individuals with severe socket-related problems, prosthesis osseointegration directly connects a prosthesis to the residual limb creating a bone-anchored limb (BAL). We compared dynamic gait stability and between-limb stability symmetry, as measured by the Margin of Stability (MoS) and the Normalized Symmetry Index (NSI), for people with unilateral transfemoral amputation before and one-year after BAL implantation. The MoS provides a mechanical construct to assess dynamic gait stability and infer center of mass and limb control by relating the center of mass and velocity to the base of support. Before and one-year after BAL implantation, 19 participants walked overground at self-selected speeds. We quantified dynamic gait stability anteriorly and laterally at foot strike and at the minimum lateral MoS value. After implantation, we observed decreased lateral MoS at foot strike for the amputated (MoS mean(SD) %height; pre: 6.6(2.3), post: 5.9(1.3), d = 0.45) and intact limb (pre: 6.2(1.2), post: 5.8(1.0), d = 0.38) and increased between-limb MoS symmetry at foot strike (NSI mean(SD) %; anterior–pre: 10.3(7.3), post: 8.4(3.6), d = 0.23; lateral–pre: 18.8(12.4), post: 12.4(4.9), d = 0.47) and at minimum lateral stability (pre: 28.1(18.1), post: 19.2(6.8), d = 0.50). Center of mass control using a BAL resulted in dynamic gait stability more similar between limbs and may have reduced the adoption of functional asymmetries. We suggest that improved between-limb MoS symmetry after BAL implantation is likely due to subtle changes in individual limb MoS values at self-selected walking speeds resulting in an overall positive impact on fall risk through improved center of mass and prosthetic limb control.

Proximity Perception-Based Grasping Intelligence: Toward the Seamless Control of a Dexterous Prosthetic Hand

Proximity Perception-Based Grasping Intelligence: Toward the Seamless Control of a Dexterous Prosthetic Hand

Semi-Autonomous Grasping Control of Prosthetic Hand and Wrist Based on Motion Prior Field

Semi-Autonomous Grasping Control of Prosthetic Hand and Wrist Based on Motion Prior Field

Unveiling EMG semantics: a prototype-learning approach to generalizable gesture classification

Objective. Upper limb loss can profoundly impact an individual’s quality of life, posing challenges to both physical capabilities and emotional well-being. To restore limb function by decoding electromyography (EMG) signals, in this paper, we present a novel deep prototype learning method for accurate and generalizable EMG-based gesture classification. Existing methods suffer from limitations in generalization across subjects due to the diverse nature of individual muscle responses, impeding seamless applicability in broader populations. Approach. By leveraging deep prototype learning, we introduce a method that goes beyond direct output prediction. Instead, it matches new EMG inputs to a set of learned prototypes and predicts the corresponding labels. Main results. This novel methodology significantly enhances the model’s classification performance and generalizability by discriminating subtle differences between gestures, making it more reliable and precise in real-world applications. Our experiments on four Ninapro datasets suggest that our deep prototype learning classifier outperforms state-of-the-art methods in terms of intra-subject and inter-subject classification accuracy in gesture prediction. Significance. The results from our experiments validate the effectiveness of the proposed method and pave the way for future advancements in the field of EMG gesture classification for upper limb prosthetics.

Simulation and experimental study on rope driven artificial hand and driven motor.

Prosthetic hands have the potential to replace human hands. Using prosthetic hands can help patients with hand loss to complete the necessary daily living actions. This paper studies the design of a bionic, compact, low-cost, and lightweight 3D printing humanoid hand. The five fingers are underactuated, with a total of 9 degrees of freedom. In the design of an underactuated hand, it is a basic element composed of an actuator, spring, rope, and guide system. A single actuator is providing power for five fingers. And the dynamic simulation is carried out to calculate the motion trajectory effect. In this paper, the driving structure of the ultrasonic motor was designed, and the structural size of the ultrasonic motor vibrator was determined by modal and transient simulation analysis, which replace the traditional brake, realize the lightweight design of prosthetic hand, improve the motion accuracy and optimize the driving performance of prosthetic hand. By replacing traditional actuators with new types of actuators, lightweight design of prosthetic hands can be achieved, improving motion accuracy and optimizing the driving performance of prosthetic hands.

Electromyography signal based hand gesture classification system using Hilbert Huang transform and deep neural networks

This research aims to provide the groundwork for smartly categorizing hand movements for use with prosthetic hands. The hand motions are classified using surface electromyography (sEMG) data. In reaction to a predetermined sequence of fibre activation, every single one of our muscles contracts. They could be useful in developing control protocols for bio-control systems, such human-computer interaction and upper limb prostheses. When focusing on hand gestures, data gloves and vision-based approaches are often used. The data glove technique requires tedious and unnatural user engagement, whereas the vision-based solution requires significantly more expensive sensors. This research offered a Deep Neural Network (DNN) automated hand gesticulation recognition system based on electromyography to circumvent these restrictions. This work primarily aims to augment the concert of the hand gesture recognition system via the use of an artificial classifier. To advance the recognition system's classification accuracy, this study explains how to build models of neural networks and how to use signal processing methods. By locating the Hilbert Huang Transform (HHT), one may get the essential properties of the signal. When training a DNN classifier, these characteristics are sent into it. The investigational results reveal that the suggested technique accomplishes a better categorization rate (98.5 % vs. the alternatives).

AI-powered Neuroprosthetics for brain-computer interfaces (BCIs)

Imagine a world where individuals with lost or impaired sensory or motor function can regain independence and control through technology. This is the promise of neuroprosthetics, a rapidly evolving field that bridges the gap between the nervous system and external devices. Neuroprosthetics encompass a range of implanted or external devices designed to: Substitute for a malfunctioning part of the nervous system. Assist in the recovery or enhancement of lost or impaired function. Augment existing capabilities, creating new possibilities. These devices interact with the nervous system using various methods, including: Electrical stimulation: Directly stimulating nerves or brain tissue to evoke desired responses. Recording brain activity: Capturing electrical signals generated by the brain for further processing and interpretation. Common applications of neuroprosthetics include: Cochlear implants: Restoring hearing in individuals with severe hearing loss. Deep brain stimulation (DBS): Treating movement disorders like Parkinson's disease and essential tremor. Bionic limbs: Providing control of prosthetic arms and legs for individuals with limb loss. Brain-computer interfaces (BCIs): Enabling communication and control of external devices using brain signals alone. Neuroprosthetics offer a glimpse into the future of medicine and technology. With ongoing advancements, these devices have the potential to revolutionize how we treat neurological conditions, restore lost abilities, and even enhance human potential. However, significant challenges remain, including ensuring long-term safety, improving accuracy and reliability, and addressing ethical considerations. As research continues, neuroprosthetics holds immense potential to improve the lives of millions and redefine what it means to be human. The integration of artificial intelligence (AI) with neuroprosthetics has marked a significant milestone in the development of brain-computer interfaces (BCIs). This emerging synergy aims to enhance the quality of life for individuals with disabilities by restoring lost sensory, motor, and cognitive functions. This review article explores the advancements in AI-powered neuroprosthetics for BCIs, focusing on their design, functionality, and the ethical considerations that accompany their integration into medical practice.

Ultrasensitive, Highly Stable, and Stretchable Strain Sensor Using Gated Liquid Metal Channel

AbstractDeveloping stretchable strain sensors with high sensitivity and stability is crucial for various applications such as prosthetic hands, human health monitoring, and human‐machine interactions. However, achieving these qualities simultaneously remains challenging. Here, an inherently stretchable strain sensor is presented that integrates ultrahigh sensitivity and robust stability, enabling stretch, press, or bend sensing capabilities. This sensor employs a softer elastomeric channel filled with liquid metal (LM) as the conductive path. A stiffer elastomer convex integrated into the channel serves as a strain‐manipulated gate, controlling opening gap of electrical current flow path. During deformation, the softer elastomer undergoes cross sectional reduction due to the Poisson effect, while the stiffer convex gate retains its geometry. This heterogeneous deformation behavior leads to significant contraction or closure of the LM channel, resulting in increased resistance and a remarkable enhancement in sensitivity by more than two orders of magnitude. The all‐soft design maintains exceptional stability even under extended or repetitive substantial deformations. With the ability to monitor subtle and large human body movements, detect grip actions of soft grippers reliably, and monitor the gradual and extended growth process of plants, this sensor holds significant potential for advancements in flexible electronics.

Cyborg insect repeatable self-righting locomotion assistance using bio-inspired 3D printed artificial limb

Cyborg insects have emerged as a promising solution for rescue missions, owing to their distinctive and advantageous mobility characteristics. These insects are outfitted with electronic backpacks affixed to their anatomical structures, which endow them with imperative communication, sensing, and control capabilities essential for effecting survivor retrieval. Nevertheless, the attachment of supplementary loads to the insect’s body can exert adverse effects on their intrinsic self-righting locomotion when confronted with fall or shock scenarios. To address this challenge, the present study introduces a bio-inspired 3D-printed artificial limb that serves to facilitate the maneuverability of cyborg insects amidst unpredictable conditions. Drawing inspiration from the natural self-righting motion exhibited by Coccinellidae, we have successfully identified a solution that can be transferred to the electronic backpack utilized by G. portentosa. Incorporation of the bio-inspired artificial wing-like limb has notably enabled the cyborg insect to achieve a remarkable tilting angle of 112°, thereby significantly amplifying the success ratio of self-righting under conditions closely emulating those prevalent in disaster areas. Moreover, we have replicated the expansion and contraction kinematics to ensure seamless motion progression within confined spaces. Importantly, the fabricated device proffered in this study has been meticulously designed for facile reproducibility employing commonly available tools, thereby serving as an inspirational catalyst for fellow researchers engaged in the advancement of 3D-printed limb development aimed at expanding the functional capacities of cyborg insects.