Myoelectric Research Articles

Constructal design of a PCM cylindrical heat sink with three‐branched, two‐stages dendritic tubes

AbstractAmputees who use prosthetic limbs suffer from the problem of high contact temperature between the socket of the prosthetic limb and the amputated part and lack of evaporation of sweat. These conditions lead to discomfort and failure to perform functions properly. In addition, these conditions help generate ulcers and accumulate harmful bacteria in this area. This paper presents a heatsink design to extract heat from the contact area. A cylindrical heat sink is designed for phase‐changing materials with three branched tubes in two stages. The current heat sink is used to cool the contact area between the amputated part and the socket in the lower prostheses. Three distributions of pipe branches are proposed. The distribution and pipe lengths were obtained using a constructal design method. In the constructal design, the lengths of the branched tubes were the degrees of freedom, the objective function was the minimization of the inlet temperature to the heat sink, and the constraint was the volume of the cylindrical heat sink. The metabolic heat transfer during exercise was estimated and its value was used to calculate the size of the cylindrical heatsink and the selection of the phase change material by testing three of them: water, tridecane, and dodecane. It was found that water gives the highest latent heat of melting and the lowest volume in addition to its availability. On the other hand, two cooling fluids were tested: water and air. It was found that water as a cooling fluid gave the lowest flow and the largest heat capacity. Constructal theory was used to design a cylindrical heat sink using branched tubes for the coolant in two steps: the first with three branches, and the second with nine branches. The degree of freedom for constructal theory was the length of the branches through the choice of their end locations. It was found that the branches of the highest length led to a reduction in temperature from 40°C to 15.48°C compared with the single tube, which reduced the temperature to 23.87°C. All tests recorded a pressure drop within the acceptable range of 3.1–5.43 Pa for the branches examined. The research demonstrated that using constructal theory achieved the best thermal dissipation within a restricted volume.

Biometric analysis hand parameters in young adults for prosthetic hand and ergonomic product applications.

This study aimed to evaluate the superficial anatomy, kinesiology, and functions of the hand to reveal its morphometry and apply the findings in various fields such as prosthetic hand and protective hand support product design. We examined 51 young adults (32 females, 19 males) aged between 18-30. Hand photographs were taken, and measurements were conducted using ImageJ software. Pearson correlation analysis was performed to determine the relationship between personal information and the parameters. The results of the measurements showed the average lengths of finger segments: thumb (49.5±5.5 mm), index finger (63.9±4.1 mm), middle finger (70.7±5.2 mm), ring finger (65.5±4.8 mm), and little finger (53.3±4.3 mm). Both females and males, the left index finger was measured longer than the right index finger. The right ring finger was found to be longer than the left in both sexes. Additionally, length differences between fingers in extended and maximally adducted positions were determined: thumb-index finger (56.1±6.2 mm), index-middle finger (10.7±4.1 mm), middle-ring finger (10.8±1.4 mm), and ring-little finger (25.6±2.7 mm). Other findings included the average radial natural angle (56.4°±10.5°), ulnar natural angle (23.4°±7.1°), radial deviation angle (65.2°±8.2°), ulnar deviation angle (51.2°±9.6°), and grasping/gripping angle (49.1°±5.8°). The average angles between fingers in maximum abduction positions were also measured: thumb-index finger (53.4°±6.5°), index-middle finger (17.2°±2.6°), middle-ring finger (14.3°±2.3°), and ring-little finger (32.1°±7.0°). The study examined the variability in the positioning of proximal interphalangeal joints during maximum metacarpophalangeal and proximal interphalangeal flexion, coinciding with maximum distal interphalangeal extension movements. The focal points of our observations were the asymmetrical and symmetrical arches formed by these joints. This study provides valuable hand parameters in young adults, which can be utilized in various applications such as prosthetic design, ergonomic product development, and hand-related research. The results highlight the significance of considering individual factors when assessing hand morphology and function.

Magnetorheological fluid in prostheses: A state-of-the-art review

Magnetorheological fluids (MRF) are intelligent materials that can vary their yield stress in response to an applied magnetic field. This characteristic, combined with active and multifunctional control, allows the development of actuators with fast response time, low energy consumption, long service life, and reduced dimensions and weights. Various studies have been conducted to improve MR dampers in prosthetic applications, including knees, ankle-foot, hands, and sockets. Here, we present a critical review of the progress of MRFs in the prosthetic field. In addition, research in prostheses’ design, optimization, and control of magnetorheological actuators is investigated, along with MRF modeling, mode of operation, type of MR actuator, classification, and working principle of MRF-based devices. Although MRFs are considered promising materials for designing novel prosthetic devices, this review shows that applications have been predominantly focused on lower limb prostheses. We conclude by discussing possible future applications and challenges that must be faced to enable and improve commercial applications based on MRF technology.

Friction coefficient estimation before gross slip for slippage prediction during prosthetic hand grasping

Slippage feedback is critical for prosthetic or bionic robotic hand to realize steady grasp. An important way to judge the slippage is to detect the maximum coefficient of static friction (µs) which is commonly measured when or after gross slip happened. Here, we proposed a new method to estimate the µs before the gross slip in order to realize slippage prediction, leaving more time for the control system to adjust grasp force. Based on a finite element model, a good linear relationship (R2 = 0.99) between the global tangential force at the time when relative slip occurs on the monitoring point (GTFRS) and the µs was obtained. An optical experiment was conducted and verified the accuracy of the simulation result. Finally, a slip sensitive soft fingertip was fabricated and tested, results show µs estimation in samples of seven different roughness is well realized with average error of 6.68 %.

Design of human lower limb motion data acquisition system based on multi-sensor

In order to protect the health of older people, a wearable lower extremity motion data acquisition system was designed. With STM32F103C8T6 as the core, the system uses an inertial attitude sensor to collect lower limb motion attitude information. In view of the large difference in plantar pressure data of different fall types, the distributed flexible film plantar pressure sensor is used to collect plantar pressure information as an auxiliary discrimination means, which can accurately distinguish the fall behavior from other behaviors, and carry out data transmission through the wireless module. The device is small in size, light in weight, low in power consumption, and wearable, which is easy to apply in the field of health monitoring, and can also be used as a reference for the implementation of corresponding protection strategies for active lower limb prosthetics.

Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.

As an interactive method gaining popularity, brain-computer interfaces (BCIs) aim to facilitate communication between the brain and external devices. Among the various research topics in BCIs, the classification of motor imagery using electroencephalography (EEG) signals has the potential to greatly improve the quality of life for people with disabilities. This technology assists them in controlling computers or other devices like prosthetic limbs, wheelchairs, and drones. However, the current performance of EEG signal decoding is not sufficient for real-world applications based on Motor Imagery EEG (MI-EEG). To address this issue, this study proposes an attention-based bidirectional feature pyramid temporal convolutional network model for the classification task of MI-EEG. The model incorporates a multi-head self-attention mechanism to weigh significant features in the MI-EEG signals. It also utilizes a temporal convolution network (TCN) to separate high-level temporal features. The signals are enhanced using the sliding-window technique, and channel and time-domain information of the MI-EEG signals is extracted through convolution. Additionally, a bidirectional feature pyramid structure is employed to implement attention mechanisms across different scales and multiple frequency bands of the MI-EEG signals. The performance of our model is evaluated on the BCI Competition IV-2a dataset and the BCI Competition IV-2b dataset, and the results showed that our model outperformed the state-of-the-art baseline model, with an accuracy of 87.5 and 86.3% for the subject-dependent, respectively. In conclusion, the BFATCNet model offers a novel approach for EEG-based motor imagery classification in BCIs, effectively capturing relevant features through attention mechanisms and temporal convolutional networks. Its superior performance on the BCI Competition IV-2a and IV-2b datasets highlights its potential for real-world applications. However, its performance on other datasets may vary, necessitating further research on data augmentation techniques and integration with multiple modalities to enhance interpretability and generalization. Additionally, reducing computational complexity for real-time applications is an important area for future work.

Classification of EMG signals with CNN features and voting ensemble classifier

Electromyography (EMG) signals are primarily used to control prosthetic hands. Classifying hand gestures efficiently with EMG signals presents numerous challenges. In addition to overcoming these challenges, a successful combination of feature extraction and classification approaches will improve classification accuracy. In the current work, convolutional neural network (CNN) features are used to reduce the redundancy problems associated with time and frequency domain features to improve classification accuracy. The features from the EMG signal are extracted using a CNN and are fed to the ‘k’ nearest neighbor (KNN) classifier with a different number of neighbors ( 1 N N , 3 N N , 5 N N , and 7 N N ) . It results in an ensemble of classifiers that are combined using a hard voting-based classifier. Based on the benchmark Ninapro DB4 database and CapgMyo database, the proposed framework obtained 91.3 % classification accuracy on CapgMyo and 89.5 % on Ninapro DB4.

Liquid Metal Composites-Enabled Real-Time Hand Gesture Recognizer with Superior Recognition Speed and Accuracy.

Prosthetic hands play a vital role in restoring forearm functionality for patients who have suffered hand loss or deformity. The hand gesture intention recognition system serves as a critical component within the prosthetic hand system. However, accurately and swiftly identifying hand gesture intentions remains a challenge in existing approaches. Here, a real-time motion intention recognition system utilizing liquid metal composite sensor bracelets is proposed. The sensor bracelet detects pressure signals generated by forearm muscle movements to recognize hand gesture intent. Leveraging the remarkable pressure sensitivity of liquid metal composites and the efficient classifier based on the optimized recognition algorithm, this system achieves an average offline and real-time recognition accuracy of 98.2% and 92.04%, respectively, with an average recognition speed of 0.364 s. Thus, this wearable system shows advantages in superior recognition speed and accuracy. Furthermore, this system finds applications in master-slave control of prosthetic hands in unmanned scenarios, such as electrically powered operations, space exploration, and telemedicine. The proposed system promises significant advances in next-generation intent-controlled prosthetic hands and robots.

On Automated Object Grasping for Intelligent Prosthetic Hands Using Machine Learning.

Prosthetic technology has witnessed remarkable advancements, yet challenges persist in achieving autonomous grasping control while ensuring the user's experience is not compromised. Current electronic prosthetics often require extensive training for users to gain fine motor control over the prosthetic fingers, hindering their usability and acceptance. To address this challenge and improve the autonomy of prosthetics, this paper proposes an automated method that leverages computer vision-based techniques and machine learning algorithms. In this study, three reinforcement learning algorithms, namely Soft Actor-Critic (SAC), Deep Q-Network (DQN), and Proximal Policy Optimization (PPO), are employed to train agents for automated grasping tasks. The results indicate that the SAC algorithm achieves the highest success rate of 99% among the three algorithms at just under 200,000 timesteps. This research also shows that an object's physical characteristics can affect the agent's ability to learn an optimal policy. Moreover, the findings highlight the potential of the SAC algorithm in developing intelligent prosthetic hands with automatic object-gripping capabilities.

A Semi-Autonomous Hierarchical Control Framework for Prosthetic Hands Inspired by Dual Streams of Human.

The routine use of prosthetic hands significantly enhances amputees' daily lives, yet it often introduces cognitive load and reduces reaction speed. To address this issue, we introduce a wearable semi-autonomous hierarchical control framework tailored for amputees. Drawing inspiration from the visual processing stream in humans, a fully autonomous bionic controller is integrated into the prosthetic hand control system to offload cognitive burden, complemented by a Human-in-the-Loop (HIL) control method. In the ventral-stream phase, the controller integrates multi-modal information from the user's hand-eye coordination and biological instincts to analyze the user's movement intention and manipulate primitive switches in the variable domain of view. Transitioning to the dorsal-stream phase, precise force control is attained through the HIL control strategy, combining feedback from the prosthetic hand's sensors and the user's electromyographic (EMG) signals. The effectiveness of the proposed interface is demonstrated by the experimental results. Our approach presents a more effective method of interaction between a robotic control system and the human.

Hardware and Software Design and Implementation of Surface-EMG-Based Gesture Recognition and Control System

The continuous advancement of electronic technology has led to the gradual integration of automated intelligent devices into various aspects of human life. Motion gesture-based human–computer interaction systems offer abundant information, user-friendly functionalities, and visual cues. Surface electromyography (sEMG) signals enable the decoding of muscle movements, facilitating the realization of corresponding control functions. Considering the inherent instability and minuscule nature of sEMG signals, this thesis proposes the integration of a dynamic time regularization algorithm to enhance gesture recognition detection accuracy and real-time system performance. The application of the dynamic time warping algorithm allows the fusion of three sEMG signals, enabling for the calculation of similarity between the sample and the model. This process facilitates gesture recognition and ensures effective communication between individuals and the 3D printed prosthesis. Utilizing this algorithm, the best feature model was generated by amalgamating six types of gesture classification model. A total of 600 training and evaluation experiments were performed, with each movement recognized 100 times. The experimental tests demonstrate that the accuracy of gesture recognition and prosthetic limb control using the temporal dynamic regularization algorithm achieves an impressive 93.75%, surpassing the performance of the traditional threshold control switch.

Robust myoelectric pattern recognition methods for reducing users' calibration burden: challenges and future.

Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots. Current MIs not only enable multi-degree-of-freedom control of prosthetic limbs but also demonstrate substantial potential in consumer electronics. However, the non-stationary random characteristics of myoelectric signals poses challenges, leading to performance degradation in practical scenarios such as electrode shifting and switching new users. Conventional MIs often necessitate meticulous calibration, imposing a significant burden on users. To address user frustration during the calibration process, researchers have focused on identifying MPR methods that alleviate this burden. This article categorizes common scenarios that incur calibration burdens as based on data distribution shift and based on dynamic data categories. Then further investigated and summarized the popular robust MPR algorithms used to reduce the user's calibration burden. We categorize these algorithms as based on data manipulate, feature manipulation and, model structure. And describes the scenarios to which each method is applicable and the conditions required for calibration. Finally, this review is concluded with the advantages of robust MPR and the remaining challenges and future opportunities.

A BIONIC HAND VS. A REPLANTED HAND.

Evaluation of the hand function affected when replacing a malfunctioning hand by a bionic hand. Case report. One individual that wished for a better quality of life after unsatisfying hand function following a replantation. A quantitative and qualitative evaluation of body functions as well as activity performance and participation before and after a planned amputation and prosthetic fitting is presented. Improvements were seen in the patient-reported outcome measures (PROMs) that were used regarding activity (Disability of the Arm, Shoulder and Hand [DASH] and Canadian Occupational Performance Measure [COPM]), pain (Neuropathic Pain Symptom Inventory [NPSI], Brief Pain Inventory [BPI], Visual Analogue Scale [VAS]), cold intolerance (CISS) and health related quality of life (SF-36), as well as in the standardised grip function test, Southampton Hand Assessment Procedure (SHAP). No referred sensations were seen but the discriminative touch on the forearm was improved. In the qualitative interview, a relief of pain, a lack of cold intolerance, improved appearance, better grip function and overall emotional wellbeing were expressed. The planned amputation and subsequent fitting and usage of a hand prosthesis were satisfying for the individual with positive effects on activity and participation. When the hand function after a hand replantation does not reach satisfactory levels, a planned amputation and a prosthetic hand can be the right solution.

Remapping Wetness Perception in Upper Limb Amputees

Recent research has made remarkable strides in restoring sensory feedback for prosthetic users, including tactile, proprioceptive, and thermal feedback. Herein, a sensory modality that has been largely neglected is explored: the ability to perceive wetness. Providing moisture‐related information to prosthesis users can increase their overall sensory palette toward a more natural sensory experience. A rapid decrease in skin temperature is found to trigger the illusion of contact with something wet. Two body parts were tested, the upper arm and the lateral abdomen, in a group of non amputated participants, and it was found that a wetness sensation can be elicited and maintained for at least 10 s in 86% and 93% of participants, respectively. It is then demonstrated how to mediate the wetness sensation in real‐time using a thermal wearable device that mimics the thermal properties of the skin. Finally, two upper limb amputee individuals used their prosthetic arm, sensorized with the device, to discriminate between three levels of moisture; their detection accuracy was similar to one they had with their intact hands. The current study is a stepping stone for future prostheses aimed at restoring the richness of sensory experience in upper limb amputees.

Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks

Predicting triplanar and bidirectional movements for a transtibial prosthesis for rehabilitation using intelligent neural networks

PERBEDAAN KECEPATAN BERJALAN PADA PENGGUNA TRANSTIBIAL PROSTHESIS TIPE SACH FOOT DAN SINGLE AXIS FOOT

Background: Below-knee amputations are the most common amputations in amputation patients. One of the mobility devices that can be used by amputation patients is a prosthesis. The main function of the prosthesis is to help its users be able to walk again and carry out activities normally . The appropriate type of prosthesis is a key factor in rehabilitation. The choice of foot prosthesis type is one of the factors that affect the gait parameter where in the gait parameter there is a walking speed factor. In most cases, the limb wearing the prosthesis does not compensate the amputated limb well, this can affect the speed of walking Method and Subject: This type of research uses analytical observational methods, with a cross-sectional research design. The subjects of this study were users of transtibial prosthesis type SACH foot and single-axis foot. The sample from this study was 16 respondents with purposive sampling techniques. The measuring instruments used in this study were meters and 10 MWT (Meter Walk Test). Results: Based on statistical tests using Mann Whitney, the results were found that there was a difference in walking speed in transtibial prosthesis users of SACH foot and single-axis foot types. Where the p-value (sig) < 0.05 is 0.001 with an effect size (large effect difference) of 0.83 which means it has a high or strong difference effect. Conclusion: there is a difference in walking speed in users of SACH foot and single-axis foot type prosthesis transtibials.

Methodology to Investigate Effect of Prosthetic Interface Design on Residual Limb Soft Tissue Deformation.

Residual limb discomfort and injury is a common experience for people living with lower limb amputation. Frequently, inadequate load distribution between the prosthetic device and the residual limb is the root cause of this issue. To advance our understanding of prosthetic interface fit, tools are needed to evaluate the mechanical interaction at the prosthetic interface, allowing interface designs to be evaluated and optimised. Present a methodology report designed to facilitate comprehension of the mechanical interaction between the prosthetic interface and the residual limb. As a pilot study, this methodology is used to compare a hands-on and hands-off interface for a single transtibial prosthesis user using secondary Magnetic Resonance Imaging (MRI) data. MRI data of the residual limb while wearing a prosthetic interface is segmented into a hard tissue and a skin surface model. These models are exported as stereolithography (STL) files. Two methods are used to analyse the interface designs. Firstly, CloudCompare software is used to compute the nearest vertex on the skin surface for every vertex on the compiled internal bony surface for both interface types. Secondly, CloudCompare software is used to compare registered skin surfaces of the residual limb while wearing the hands-on and hands-off interfaces. The maximum and minimum nearest distances between the internal bony surface and skin surface were similar between interface types. However, the distribution of nearest distances was different. When comparing the skin surface while wearing both interfaces, where the fit is more compressive can be visualized. For the dataset used in this study, the classic features of a hands-on Patella Tendon Bearing interface and hands-off pressure cast interface could be identified. The methodology presented in this report may give researchers a further tool to better understand how interface designs affect the soft tissues of the residual limb.

A Study on the Fabrication of Pressure Measurement Sensors and Intention Verification in a Personalized Socket of Intelligent Above-Knee Prostheses: A Guideline for Fabricating Flexible Sensors Using Velostat Film

Intelligent transfemoral prostheses, which have recently been studied, are equipped with a microcontroller, providing appropriate motion functions for their walking environments. Thus, studies have been conducted to estimate user intentions in locomotion movements by applying biomechanical sensors inside the socket. Among them, a pressure sensor is used to determine the intentions of locomotion movements through changes in the internal pressure of the prosthetic socket. However, existing studies have a problem in that the reproducibility of pressure change data is degraded due to the non-detection and saturation of the pressure measurement value. Accordingly, this study proposes a fabrication method for a wide and flexible pressure sensor that can solve this problem and a method for the identification of user intentions in locomotion movements using it. The proposed system was fabricated with Velostat film, which has a smaller noise impact and can be fabricated in various sizes and shapes. The fabricated sensor was attached to four points inside the socket, confirming the possibility of detecting the intention of six movements according to the multi-critical detection method. The proposed pressure-sensor-based intention detection system can be applied individually by prosthetic users through simple tasks. Moreover, it will be universally applicable for commercialization.

Method and organization for expert evaluating of rehabilitation services for lower limb prosthetics: problems and solutions

Relevance. Prosthetics of the lower limb after amputation restores the patient's ability to move and self-care. Preparation and prosthetics in the early stages after amputation, in the absence of contraindications, improves the physical, psycho-emotional state of patients, reduces the risk of complications due to forced exercise and exacerbation of concomitant somatic pathology, as well as increases exercise tolerance, restores mobility, functioning after amputation.The aim of the work is to improve the quality of life of patients with mobility disabilities.Materials and methods. The information base of the study was the normative legal acts of the Russian Federation, standards, clinical recommendations defining the existing system of providing rehabilitation services for prosthetics of the lower limb.Conclusion. Unsatisfactory prosthetics of the limb after amputation negatively affects the quality of life of the patient. For this reason, the assessment of the rehabilitation service of prosthetics of the lower limb, including using the tools of the International Classification of Functioning, Disability and Health, is both relevant and necessary.

Accessible Prosthetic Arms: Victoria Hand Project and The Impact of 3D Printing.

Victoria Hand Project (VHP) is a Canadian charity with a mission to provide 3D printed prosthetic arms to people in-need across the world, by partnering with prosthetic care providers. This article explores the journey of VHP, sharing insights, lessons learned, ongoing directions, and the impact of 3D printing on prosthetic care for people with upper-limb amputation. Benefits such as affordability and customization are explored, as well as the challenges encountered, including quality control and the steep learning curve associated with working in the digital 3D space. Through this article, the potential of 3D printing to continue to transform the field of assistive technology and prosthetic and orthotic applications is underscored, especially when used for collaborative, humanitarian initiatives.