Amputee Subjects Research Articles

Mechanical Work and Metabolic Cost of Walking with Knee-Foot Prostheses: A Study with a Prosthesis Simulator

BackgroundAt equivalent speeds, above-knee amputee subjects have a higher metabolic cost than non-amputees. Following amputation, the ankle propulsion is reduced, and using other joints to compensate is mechanically less efficient. ObjectiveThis study investigated the link between mechanical work and metabolic cost in abled-bodied subjects using a prosthesis simulator, and the influence of foot energy restitution by comparing a foot with restitution to one without. MethodSix volunteers fitted with an orthosis immobilising their ankle and knee, enabling the use of a prosthesis, carried out a gait analysis and an analysis of metabolic cost. The total lower limb mechanical work and works at the hip, knee and ankle were computed. ResultsWith an almost twofold increase, metabolic cost and hip work were significantly higher in both configurations with prosthesis than without (p < 0.001 for both variables in both configurations), while total lower limb mechanical work showed no significant difference between configurations. No significant difference was observed between the two prosthetic feet in terms of metabolic cost nor mechanical work performed by the subject. DiscussionTotal lower limb mechanical work alone cannot explain the extra metabolic cost in subjects fitted with a knee-foot prosthesis simulator; internal inefficiencies exist. We also found that metabolic cost and hip work increase and decrease simultaneously, thus studying hip muscles work could be interesting. With no significant difference between the two feet, optimising ankle propulsion seems to be ineffective in improving metabolic cost. These findings should be evaluated in a sample of above-knee amputee subjects.

Transfer Learning for Efficient Intent Prediction in Lower-Limb Prosthetics: A Strategy for Limited Datasets.

This paper presents a transfer learning method to enhance locomotion intent prediction in novel transfemoral amputee subjects, particularly in data-sparse scenarios. Transfer learning is done with three pre-trained models trained on separate datasets: transfemoral amputees, able-bodied individuals, and a mixed dataset of both groups. Each model is subsequently fine-tuned using data from a new transfemoral amputee subject. While subject-dependent models, trained and tested using individual user data, can achieve the least error rate, they require extensive training datasets. In contrast, our transfer learning approach yields comparable error rates while requiring significantly less data. This highlights the benefit of using preexisting, pre-trained features when data is scarce. As anticipated, the performance of transfer learning improves as more data from the subject is made available. We also explore the performance of the intent prediction system under various sensor configurations. We identify that a combination of a thigh inertial measurement unit and load cell offers a practical and efficient choice for sensor setup. These findings underscore the potential of transfer learning as a powerful tool for enhancing intent prediction accuracy for new transfemoral amputee subjects, even under data-limited conditions.

Optimized k-nearest neighbors for classification of prosthetic hand movements using electromyography signal

Electromyography (EMG) signals are essential, as they are used to measure muscular activity in different parts of the human body. The measurement and analysis of EMG signal lead to various applications of muscle disorders such as muscular dystrophy, myopathy, hand movements, etc. In this paper, an improved and effective hand movement classification model is developed for amputee subjects. It includes: (1) EMG feature extraction using Discrete Wavelet Transform (DWT), (2) EMG feature selection using binary Global Best Guided Gaussian Artificial Bee Colony (BGGABC), (3) Hand movements classification using Optimized k-nearest neighbors (OKNN) classifier. The EMG signal is taken from the DB3 of NinaPro dataset comprising 17 different prosthetic hand movements recorded from 11 amputee subjects. Thereafter, DWT is applied to decompose the EMG signal for extracting features. An improved wrapper-based feature selection technique (BGGABC) is used to select the optimal feature subset for effective classification. The two variants of KNN, i.e. Smallest Modified KNN and Largest Modified KNN are taken in which item strength to a class is optimized for efficient classification. The strength of an item to a class depends on distance and weight of an item to a class. Therefore, a multi-objective Non-dominated sorting genetic algorithm-II (NSGA-II) is used for optimizing these two contradictory parameters (distance and weight) simultaneously to have optimized variants, namely: Optimized Smallest KNN (OSKNN) and Optimized Largest KNN (OLKNN). Extensive results show that the proposed method OKNN achieved the highest classification accuracy of 93.07% (OLKNN) and 89.43% (OSKNN) compared with KNN variants and competitors.

Inertial Measuring System to Evaluate Gait Parameters and Dynamic Alignments for Lower-Limb Amputation Subjects.

The study aims to construct an inertial measuring system for the application of amputee subjects wearing a prosthesis. A new computation scheme to process inertial data by installing seven wireless inertial sensors on the lower limbs was implemented and validated by comparing it with an optical motion capture system. We applied this system to amputees to verify its performance for gait analysis. The gait parameters are evaluated to objectively assess the amputees' prosthesis-wearing status. The Madgwick algorithm was used in the study to correct the angular velocity deviation using acceleration data and convert it to quaternion. Further, the zero-velocity update method was applied to reconstruct patients' walking trajectories. The combination of computed walking trajectory with pelvic and lower limb joint motion enables sketching the details of motion via a stickman that helps visualize and animate the walk and gait of a test subject. Five participants with above-knee (n = 2) and below-knee (n = 3) amputations were recruited for gait analysis. Kinematic parameters were evaluated during a walking test to assess joint alignment and overall gait characteristics. Our findings support the feasibility of employing simple algorithms to achieve accurate and precise joint angle estimation and gait parameters based on wireless inertial sensor data.

Speed-dependent modulations of asymmetric center of body mass trajectory in the gait of above-knee amputee subjects.

How to achieve stable locomotion while overcoming various instabilities is an ongoing research topic. One essential factor for achieving a stable gait is controlling the center of body mass (CoM). The CoM yields more instability in the mediolateral direction. Examining speed-dependent modulations of the CoM trajectories in the frontal plane can provide insight into control policies for achieving stable locomotion. Although these modulations have been studied while assuming symmetric CoM trajectories, this assumption is generally incorrect. For example, amputee subjects demonstrate asymmetric CoM trajectories. Here, we investigated speed-dependent modulations of asymmetric CoM trajectories in above-knee amputee subjects using Fourier series expansion. Despite the asymmetric CoM trajectories in amputee subjects, the framework of Fourier series expansion clarified that amputee subjects showed the same speed-dependent modulations as non-amputee subjects whose CoM trajectories were symmetric. Specifically, CoM trajectories became narrower in the mediolateral direction and broader in the superoinferior direction as walking speed increased. The speed-dependent modulations of CoM trajectories had a functional role in improving dynamic stability, and faster walking speeds provided greater dynamic stability on both prosthetic and non-prosthetic sides. Although the asymmetry of foot contact duration and CoM trajectory decreased as walking speed increased, step width and the asymmetry of dynamic stability between prosthetic and non-prosthetic sides remained constant across the walking speed, which corresponded to the predictions by our framework. These findings could offer a better strategy for achieving stable walking for amputee subjects.

EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network.

This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN's superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control.

Closed-Loop Force Control by Biorealistic Hand Prosthesis with Visual and Tactile Sensory Feedback.

The ability of a novel biorealistic hand prosthesis for grasp force control reveals improved neural compatibility between the human-prosthetic interaction. The primary purpose here was to validate a virtual training platform for amputee subjects and evaluate the respective roles of visual and tactile information in fundamental force control tasks. We developed a digital twin of tendon-driven prosthetic hand in the MuJoCo environment. Biorealistic controllers emulated a pair of antagonistic muscles controlling the index finger of the virtual hand by surface electromyographic (sEMG) signals from amputees' residual forearm muscles. Grasp force information was transmitted to amputees through evoked tactile sensation (ETS) feedback. Six forearm amputees participated in force tracking and holding tasks under different feedback conditions or using their intact hands. Test results showed that visual feedback played a predominant role than ETS feedback in force tracking and holding tasks. However, in the absence of visual feedback during the force holding task, ETS feedback significantly enhanced motor performance compared to feedforward control alone. Thus, ETS feedback still supplied reliable sensory information to facilitate amputee's ability of stable grasp force control. The effects of tactile and visual feedback on force control were subject-specific when both types of feedback were provided simultaneously. Amputees were able to integrate visual and tactile information to the biorealistic controllers and achieve a good sensorimotor performance in grasp force regulation. The virtual platform may provide a training paradigm for amputees to adapt the biorealistic hand controller and ETS feedback optimally.

Fusion of dual modalities of non-invasive sensory feedback for object profiling with prosthetic hands.

Either non-invasive somatotopic or substitute sensory feedback is capable of conveying a single modality of sensory information from prosthetic hands to amputees. However, the neurocognitive ability of amputees to integrate multi-modality sensory information for functional discrimination is unclear. The purpose of this study was to assess the fusion of non-invasive somatotopic tactile and substitute aperture feedbacks for profile perception of multiple physical features during grasping objects. Two left transradial amputees with somatotopic evoked tactile sensation (ETS) of five fingers participated in the study. The tactile information of prosthetic hand was provided to amputees by the ETS feedback elicited on the stump projected finger map. Hand aperture information was conveyed to amputees with substitute electrotactile stimulation on the forearm or upper arm. Two types of sensory feedback were integrated to a commercial prosthetic hand. The efficacy of somatotopic ETS feedback on object length identification task was assessed with or without substitute aperture stimulation. The object size identification task was utilized to assess how ETS stimulation at the stump may affect aperture perception with stimulation on the ipsilateral upper arm or forearm. Finally, the task of identifying combined length and size was conducted to evaluate the ability of amputees to integrate the dual modalities of sensory feedback for perceiving profile features. The study revealed that amputee subjects can effectively integrate the ETS feedback with electrotactile substitutive feedback for object profile discrimination. Specifically, ETS was robust to provide object length information with electrotactile stimulation at either the forearm or upper arm. However, electrotactile stimulation at the upper arm for aperture perception was less susceptible to the interference of ETS stimulation than at the forearm. Amputee subjects are able to combine somatotopic ETS and aperture feedbacks for identifying multi-dimensional features in object profiling. The two sensory streams of information can be fused effectively without mutual interference for functional discrimination.

Exploring the EMG transient: the muscular activation sequences used as novel time-domain features for hand gestures classification.

Muscular activation sequences have been shown to be suitable time-domain features for classification of motion gestures. However, their clinical application in myoelectric prosthesis control was never investigated so far. The aim of the paper is to evaluate the robustness of these features extracted from the EMG signal in transient state, on the forearm, for classifying common hand tasks. The signal associated to four hand gestures and the rest condition were acquired from ten healthy people and two persons with trans-radial amputation. A feature extraction algorithm allowed for encoding the EMG signals into muscular activation sequences, which were used to train four commonly used classifiers, namely Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Non-linear Logistic Regression (NLR) and Artificial Neural Network (ANN). The offline performances were assessed with the entire sample of recruited people. The online performances were assessed with the amputee subjects. Moreover, a comparison of the proposed method with approaches based on the signal envelope in the transient state and in the steady state was conducted. The highest performance were obtained with the NLR classifier. Using the sequences, the offline classification accuracy was higher than 93% for healthy and amputee subjects and always higher than the approach with the signal envelope in transient state. As regards the comparison with the steady state, the performances obtained with the proposed method are slightly lower (<4%), but the classification occurred at least 200 ms earlier. In the online application, the motion completion rate reached up to 85% of the total classification attempts, with a motion selection time that never exceeded 218 ms. Muscular activation sequences are suitable alternatives to the time-domain features commonly used in classification problems belonging to the sole EMG transient state and could be potentially exploited in control strategies of myoelectric prosthesis hands.

Functionally Adaptive Myosite Selection Using High-Density sEMG for Upper Limb Myoelectric Prostheses.

Our study defines a novel electrode placement method called Functionally Adaptive Myosite Selection (FAMS), as a tool for rapid and effective electrode placement during prosthesis fitting. We demonstrate a method for determining electrode placement that is adaptable towards individual patient anatomy and desired functional outcomes, agnostic to the type of classification model used, and provides insight into expected classifier performance without training multiple models. FAMS relies on a separability metric to rapidly predict classifier performance during prosthesis fitting. The results show a predictable relationship between the FAMS metric and classifier accuracy (3.45%SE), allowing estimation of control performance with any given set of electrodes. Electrode configurations selected using the FAMS metric show improved control performance ( ) for target electrode counts compared to established methods when using an ANN classifier, and equivalent performance ( R2 ≥ .96) to previous top-performing methods on an LDA classifier, with faster convergence ( ). We used the FAMS method to determine electrode placement for two amputee subjects by using the heuristic to search through possible sets, and checking for saturation in performance vs electrode count. The resulting configurations that averaged 95.8% of the highest possible classification performance using a mean 25 number of electrodes (19.5% of the available sites). FAMS can be used to rapidly approximate the tradeoffs between increased electrode count and classifier performance, a useful tool during prosthesis fitting.

Powered Knee and Ankle Prosthesis Control for Adaptive Ambulation at Variable Speeds, Inclines, and Uneven Terrains.

Ambulation in everyday life requires walking at variable speeds, variable inclines, and variable terrains. Powered prostheses aim to provide this adaptability through control of the actuated joints. Some powered prosthesis controllers can adapt to discrete changes in speed and incline but require manual tuning to determine the control parameters, leading to poor clinical viability. Other data-driven controllers can continuously adapt to changes in speed and incline but do so by imposing the same non-amputee gait patterns for all amputee subjects, which does not consider subjective preferences and differing clinical needs of users. Here, we present a controller for powered knee and ankle prostheses that can continuously adapt to different walking speeds, inclines, and uneven terrains without enforcing a specific prosthesis position, impedance, or torque. A virtual biarticular muscle connection determines the knee flexion torque, which changes with both speed and slope. Adaptation to inclines and uneven terrains is based solely on the global shank orientation. Continuously variable damping allows for speed adaptation. Minimum-jerk programming defines the prosthesis swing trajectory at variable cadences. Experiments with one individual with an above-knee amputation suggest that the proposed controller can effectively adapt to different walking speeds, inclines, and rough terrains.

Musculoskeletal model of osseointegrated transfemoral amputees in OpenSim.

This study presents a generic OpenSim musculoskeletal model of people with an osseointegrated unilateral transfemoral amputation wearing a generic prosthesis. The model, which consists of seventy-six musculotendon units and two ideal actuators at the knee and ankle joints of the prosthesis, is tested by designing an optimal control strategy that guarantees the tracking of experimental amputee data during level-ground walking while finding the actuators' torques and minimizing the muscle forces. The model can be made subject-specific and, as such, is able to reproduce the kinematics and dynamics of both healthy and amputee subjects. The model provides a tool to analyze the biomechanics of level-ground walking and to understand the contribution of the muscles and of the prosthesis' actuators. The proposed OpenSim musculoskeletal model is released as support material to this study.

Nonlinear Mapping From EMG to Prosthesis Closing Velocity Improves Force Control With EMG Biofeedback.

When using EMG biofeedback to control the grasping force of a myoelectric prosthesis, subjects need to activate their muscles and maintain the myoelectric signal within an appropriate interval. However, their performance decreases for higher forces, because the myoelectric signal is more variable for stronger contractions. Therefore, the present study proposes to implement EMG biofeedback using nonlinear mapping, in which EMG intervals of increasing size are mapped to equal-sized intervals of the prosthesis velocity. To validate this approach, 20 non-disabled subjects performed force-matching tasks using Michelangelo prosthesis with and without EMG biofeedback with linear and nonlinear mapping. Additionally, four transradial amputees performed a functional task in the same feedback and mapping conditions. The success rate in producing desired force was significantly higher with feedback (65.4±15.9%) compared to no feedback (46.2±14.9%) as well as when using nonlinear (62.4±16.8%) versus linear mapping (49.2±17.2%). Overall, in non-disabled subjects, the highest success rate was obtained when EMG biofeedback was combined with nonlinear mapping (72%), and the opposite for linear mapping with no feedback (39.6%). The same trend was registered also in four amputee subjects. Therefore, EMG biofeedback improved prosthesis force control, especially when combined with nonlinear mapping, which showed to be an effective approach to counteract increasing variability of myoelectric signal for stronger contractions.

E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb

Surface electromyography is a bioelectrical indicator that emerges during muscle contraction and has been widely used in a variety of clinical applications. Several prosthetic control applications can benefit from the analysis based on the classification of surface electromyography (sEMG) signals. However, for the real-time application of upper limb prosthesis, the EMG-based systems need robust performance and rapid response behavior. In this study, we propose an efficient concatenated convolutional neural network (E2CNN) for classification of sEMG extracted from the upper limb. We have tested and validated the performance of the proposed E2CNN on two datasets: a longitudinal dataset comprising ten nondisabled (healthy) subjects and six transradial amputee subjects and spanning the data collected for a period of seven days; and the publicly available NinaPro DB1 dataset. The raw sEMG signals are converted into Log-Mel spectrograms (LMSs). This model combines the input layers with the output of each convolutional block using concatenation layers. The proposed E2CNN when applied to LMS-based images provides a good response time with high-performance accuracy of 98.31% ± 0.5% and 97.97% ± 1.41% for both nondisabled and amputee subjects. When applied to NinaPro DB1, the proposed E2CNN has attained a mean accuracy of 91.27%, an increase by 24.67% with respect to the baseline CNN model. The results show that the achieved results are comparable to those obtained using stacked sparse autoencoders (SSAEs) and other CNN models; however, E2CNN is associated with reduced training and prediction times, making it a potential candidate for real-time classification of sEMG based on LM spectrogram images.

Early decoding of walking tasks with minimal set of EMG channels

Objective. Powered lower-limb prostheses relying on decoding motor intentions from non-invasive sensors, like electromyographic (EMG) signals, can significantly improve the quality of life of amputee subjects. However, the optimal combination of high decoding performance and minimal set-up burden is yet to be determined. Here we propose an efficient decoding approach obtaining high decoding performance by observing only a fraction of the gait duration with a limited number of recording sites. Approach. Thirteen transfemoral amputee subjects performed five motor tasks while recording EMG signals from four muscles and inertial signals from the prosthesis. A support-vector-machine-based algorithm decoded the gait modality selected by the patient from a finite set. We investigated the trade-off between the robustness of the classifier’s accuracy and the minimization of (i) the duration of the observation window, (ii) the number of EMG recording sites, (iii) the computational load of the procedure, measured the complexity of the algorithm. Main results. When including pre-foot-strike data in the decoding, the combination of three EMG recording sites and the inertial signals led to correct rates above 94% at the 20% of the gait cycle, showing the best trade-off between invasiveness of the setup and accuracy of the classifier. The complexity of the algorithm proved to be significantly higher when applying a polynomial kernel compared to a linear one, while the correct rate of the classifier generally showed no differences between the two approaches. The proposed algorithm led to high performance with a minimal EMG set-up and using only a fraction of the gait duration. Significance. These results pave the way for efficient control of powered lower-limb prostheses with minimal set-up burden and a rapid classification output.

Harnessing Machine Learning and Physiological Knowledge for a Novel EMG-Based Neural-Machine Interface.

In this study, we aimed to develop a novel electromyography (EMG)-based neural machine interface (NMI), called the Neural Network-Musculoskeletal hybrid Model (N2M2), to decode continuous joint angles. Our approach combines the concepts of machine learning and musculoskeletal modeling. We compared our novel design with a musculoskeletal model (MM) and 2 continuous EMG decoders based on artificial neural networks (ANNs): multilayer perceptrons (MLPs) and nonlinear autoregressive neural networks with exogenous inputs (NARX networks). EMG and joint kinematics data were collected from 10 non-disabled and 1 transradial amputee subject. The offline performance tested across 3 different conditions (i.e., varied arm postures, shifted electrode locations, and noise-contaminated EMG signals) and online performance for a virtual postural matching task was quantified. Finally, we implemented the N2M2 to operate a prosthetic hand and tested functional task performance. The N2M2 made more accurate predictions than the MLP in all postures and electrode locations (p < 0.003). For estimated MCP joint angles, the N2M2 was less sensitive to noisy EMG signals than the MM or NARX network with respect to error (p < 0.032) as well as the NARX network with respect to correlation (p = 0.007). Additionally, the N2M2 had better online task performance than the NARX network (p ≤ 0.030). Overall, we have found that combining the concepts of machine learning and musculoskeletal modeling has resulted in a more robust joint kinematics decoder than either concept individually. The outcome of this study may result in a novel, highly reliable controller for powered prosthetic hands.

Wrist autonomy based on upper-limb synergy: a pilot study.

Incorporating an electrically powered wrist can largely improve the dexterity of a prosthetic hand when grasping various objects; however, it also intensifies the difficulty of the hand's operation due to the introduction of extra degrees of freedom (DOFs). The mechanism of multi-joint synergy in human body movements provides a new sight to solve this problem. In this paper, focusing on four typical manipulation activities of daily life (ADLs), 10 upper-limb joint angles were collected and analyzed first to verify the existence of synergy. Then, a linear regression model was established to predict the wrist rotation angle from the shoulder and elbow joints, which can be directly used as a control reference for achieving wrist autonomy. For both healthy and amputee subjects, experimental platforms were established and control tests were conducted, wherein the task completion time and compensatory movement during the four ADLs were evaluated. The results show that our synergy-based wrist autonomy method can effectively improve the completion efficiency of multiple ADLs without increasing the control complexity. Also, it can significantly reduce the compensatory movements of multiple joints compared to traditional prostheses using an idle wrist.

Wrist speed feedback improves elbow compensation and reaching accuracy for myoelectric transradial prosthesis users in hybrid virtual reaching task

BackgroundMyoelectric prostheses are a popular choice for restoring motor capability following the loss of a limb, but they do not provide direct feedback to the user about the movements of the device—in other words, kinesthesia. The outcomes of studies providing artificial sensory feedback are often influenced by the availability of incidental feedback. When subjects are blindfolded and disconnected from the prosthesis, artificial sensory feedback consistently improves control; however, when subjects wear a prosthesis and can see the task, benefits often deteriorate or become inconsistent. We theorize that providing artificial sensory feedback about prosthesis speed, which cannot be precisely estimated via vision, will improve the learning and control of a myoelectric prosthesis.MethodsIn this study, we test a joint-speed feedback system with six transradial amputee subjects to evaluate how it affects myoelectric control and adaptation behavior during a virtual reaching task.ResultsOur results showed that joint-speed feedback lowered reaching errors and compensatory movements during steady-state reaches. However, the same feedback provided no improvement when control was perturbed.ConclusionsThese outcomes suggest that the benefit of joint speed feedback may be dependent on the complexity of the myoelectric control and the context of the task.

Fingertip Proximity-Based Grasping Pattern Prediction of Transradial Myoelectric Prosthesis.

For transradial amputees, especially those with insufficient residual muscle activity, it is challenging to quickly obtain an appropriate grasping pattern for a multigrasp prosthesis. To address this problem, this study proposed a fingertip proximity sensor and a grasping pattern prediction method base on it. Rather than exclusively utilizing the EMG of the subject for the grasping pattern recognition, the proposed method used fingertip proximity sensing to predict the appropriate grasping pattern automatically. We established a five-fingertip proximity training dataset for five common classes of grasping patterns (spherical grip, cylindrical grip, tripod pinch, lateral pinch, and hook). A neural network-based classifier was proposed and got a high accuracy (96%) within the training dataset. We assessed the combined EMG/proximity-based method (PS-EMG) on six able-bodied subjects and one transradial amputee subject while performing the "reach-and-pick up" tasks for novel objects. The assessments compared the performance of this method with the typical pure EMG methods. Results indicated that able-bodied subjects could reach the object and initiate prosthesis grasping with the desired grasping pattern on average within 1.93 s and complete the tasks 7.30% faster on average with the PS-EMG method, relative to the pattern recognition-based EMG method. And the amputee subject was, on average, 25.58% faster in completing tasks with the proposed PS-EMG method relative to the switch-based EMG method. The results showed that the proposed method allowed the user to obtain the desired grasping pattern quickly and reduced the requirement for EMG sources.

Study of Tactile Sensation Somatotopy and Homology Between Projected Fingers in Residual Limb and Natural Fingers in Intact Limb.

Based on the comparisons of the somatosensory event-related potentials (ERPs), the object of this study is to investigate the underlying cognition mechanism of somatotopy and the homology of tactile sensation between the projected fingers in the residual limb and the natural fingers in the intact limb. One amputee subject and three able-bodied subjects were recruited. The forearm amputee had a clear projected finger mapping (PFM) that could evoke the tactile sensation of the entire five missing fingers. Transcutaneous electrical nerve stimulation (TENS) was used to evoke the sensation pattern of touch. Stimulation locations were divided into three groups: the locations of Group PA (projected-finger of amputee-subject) were located on the entire five projected fingers for the amputee subject, the locations of Group NA (natural-finger of amputee-subject) were located on the entire five natural fingers for the amputee subject, and the locations of Group NH (natural-finger of healthy-subject) were located on the bilateral natural index fingers for the able-bodied subjects. The somatosensory ERPs evoked by the stimulations were recorded. We measured the latency and amplitude of the ERP components and made statistical analyses for them. Since the ERP components of the early-stage are similar for both the stimulation in the projected fingers and the natural fingers, it can infer that the delivery pathway of the projected finger was similar to that of the natural finger. The second finding of the study is that, as the processing of sensory sensation in the cortex of the three groups is similar, it can also infer that the somatosensory evoked by the external stimuli are also similar. The present findings suggest that the somatotopy and the homology of tactile sensation between the projected fingers in the residual limb and the natural fingers in the intact limb have evident uniformity. We infer that the median nerve and the ulnar nerve of the peripheral nerve may divaricate new pathways, and these pathways would have been linked to the PFM.