Target Achievement Control Test Research Articles

A compact solution for vibrotactile proprioceptive feedback of wrist rotation and hand aperture

BackgroundClosing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions.MethodsWe propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback.ResultsConventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants.ConclusionsThe study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Experimental evaluation of the impact of sEMG interfaces in enhancing embodiment of virtual myoelectric prostheses

IntroductionDespite recent technological advances that have led to sophisticated bionic prostheses, attaining embodied solutions still remains a challenge. Recently, the investigation of prosthetic embodiment has become a topic of interest in the research community, which deals with enhancing the perception of artificial limbs as part of users’ own body. Surface electromyography (sEMG) interfaces have emerged as a promising technology for enhancing upper-limb prosthetic control. However, little is known about the impact of these sEMG interfaces on users’ experience regarding embodiment and their interaction with different functional levels.MethodsTo investigate this aspect, a comparison is conducted among sEMG configurations with different number of sensors (4 and 16 channels) and different time delay. We used a regression algorithm to simultaneously control hand closing/opening and forearm pronation/supination in an immersive virtual reality environment. The experimental evaluation includes 24 able-bodied subjects and one prosthesis user. We assess functionality with the Target Achievement Control test, and the sense of embodiment with a metric for the users perception of self-location, together with a standard survey.ResultsAmong the four tested conditions, results proved a higher subjective embodiment when participants used sEMG interfaces employing an increased number of sensors. Regarding functionality, significant improvement over time is observed in the same conditions, independently of the time delay implemented.ConclusionsOur work indicates that a sufficient number of sEMG sensors improves both, functional and subjective embodiment outcomes. This prompts discussion regarding the potential relationship between these two aspects present in bionic integration. Similar embodiment outcomes are observed in the prosthesis user, showing also differences due to the time delay, and demonstrating the influence of sEMG interfaces on the sense of agency.

Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis

Decoding human motor intentions by processing electrophysiological signals is a crucial, yet unsolved, challenge for the development of effective upper limb prostheses. Pattern recognition of continuous myoelectric (EMG) signals represents the state-of-art for multi-DoF prosthesis control. However, this approach relies on the unreliable assumption that repeatable muscular contractions produce repeatable patterns of steady-state EMGs. Here, we propose an approach for decoding wrist and hand movements by processing the signals associated with the onset of contraction (transient EMG). Specifically, we extend the concept of a transient EMG controller for the control of both wrist and hand, and tested it online. We assessed it with one transradial amputee and 15 non-amputees via the Target Achievement Control test. Non-amputees successfully completed 95% of the trials with a median completion time of 17 seconds, showing a significant learning trend ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). The transradial amputee completed about the 80% of the trials with a median completion time of 26 seconds. Although the performance proved comparable with earlier studies, the long completion times suggest that the current controller is not yet clinically viable. However, taken collectively, our outcomes reinforce earlier hypothesis that the transient EMG could represent a viable alternative to steady-state pattern recognition approaches.

Evaluating the Ability of Congenital Upper Extremity Amputees to Control a Multi-Degree of Freedom Myoelectric Prosthesis

To determine whether children and adults with unilateral congenital upper limb amputation can control myoelectric prostheses with multiple degrees of freedom (DOF) using pattern recognition (PR) technology. Seven participants (age 9-62 years) with unilateral congenital transradial amputation were tested on both their residual and sound side limbs to determine proficiency in controlling a virtual prosthesis using electromyographic signals captured by an array of surface electrodes that were processed using PR technology. Proficiency was measured through a virtual environment game called the target achievement control test, in which the testing protocol asked participants to match increasingly complex prosthesis postures with 1,2, and 3 DOF. All the participants successfully created a PR calibration at 1, 2, and 3 DOF with their residual limb during testing, and no differences in calibration accuracy were observed when comparing the residual versus sound upper limbs. No differences were noted in the mean completion rate on the target achievement control test between the residual and sound limbs. Participants with a congenital upper limb amputation achieved PR control calibration of multi-DOF prostheses with proficiency and quality results of PR calibration that were comparable to those of their sound limb. This capability was observed in children as well as in adults. This demonstrates the potential for children and adults with a unilateral congenital transradial amputation to benefit from myoelectric prostheses with PR control. The results from this study highlight the potential for patients in this population to benefit from myoelectric prostheses with PR control. Persons with unilateral congenital upper limb amputations can be considered for provision of this technology andenrollment in future research activities.

A Wearable Ultrasound System for Sensing Muscular Morphological Deformations

Noninvasive monitoring of muscle contraction, which provides information about muscle morphological deformations, has a great potential in medical applications, such as stroke rehabilitation and prosthesis control. This paper presents a wearable multichannel A-mode ultrasound system for the multiperspective muscle contraction detection against its existing bulky ultrasound sensing counterpart. The system consists of a waveform generator and a waveform amplifier for ultrasound excitation, as well as a signal processing module for echo receiving, amplifying, and transmitting. In addition, a miniaturized transducer was optimized for muscle contraction monitoring using 1-3 piezoelectric composite. The system's superiorities on excitation pulse, detection depth, and axial resolution were validated by the evaluation experiments. And in vivo muscle deformation detection and virtual prosthesis control experiments (target achievement control test) demonstrated its ability in rehabilitation applications, with a task completion rate of 100% and path efficiency of 93.30%. These results confirmed the reliability of the proposed ultrasound system and paved the way for its applications in rehabilitation treatment and prosthesis research.

Understanding Limb Position and External Load Effects on Real-Time Pattern Recognition Control in Amputees.

Limb position is a factor that negatively affects myoelectric pattern recognition classification accuracy. However, prior studies evaluating impact on real-time control for upper-limb amputees have done so without a physical prosthesis on the residual limb. It remains unclear how limb position affects real-time pattern recognition control in amputees when their residual limb is supporting various weights. We used a virtual reality target achievement control test to evaluate the effects of limb position and external load on real-time pattern recognition control in fourteen intact limb subjects and six major upper limb amputee subjects. We also investigated how these effects changed based on different control system training methods. In a static training method, subjects kept their unloaded arm by their side with the elbow bent whereas in the dynamic training method, subjects moved their arm throughout a workspace while supporting a load. When static training was used, limb position significantly affected real-time control in all subjects. However, amputee subjects were still able to adequately complete tasks in all conditions, even in untrained limb positions. Moreover, increasing external loads decreased controller performance, albeit to a lesser extent in amputee subjects. The effects of limb position did not change as load increased, and vice versa. In intact limb subjects, dynamic training significantly reduced the limb position effect but did not completely remove them. In contrast, in amputee subjects, dynamic training eliminated the limb position effect in three out of four outcome measures. However, it did not reduce the effects of load for either subject population. These findings suggest that results obtained from intact limb subjects may not generalize to amputee subjects and that advanced training methods can substantially improve controller robustness to different limb positions regardless of limb loading.

Online Natural Myocontrol of Combined Hand and Wrist Actions Using Tactile Myography and the Biomechanics of Grasping.

Objective: Despite numerous recent advances in the field of rehabilitation robotics, simultaneous, and proportional control of hand and/or wrist prostheses is still unsolved. In this work we concentrate on myocontrol of combined actions, for instance power grasping while rotating the wrist, by only using training data gathered from single actions. This is highly desirable since gathering data for all possible combined actions would be unfeasibly long and demanding for the amputee.Approach: We first investigated physiologically feasible limits for muscle activation during combined actions. Using these limits we involved 12 intact participants and one amputee in a Target Achievement Control test, showing that tactile myography, i.e., high-density force myography, solves the problem of combined actions to a remarkable extent using simple linear regression. Since real-time usage of many sensors can be computationally demanding, we compare this approach with another one using a reduced feature set. These reduced features are obtained using a fast, spatial first-order approximation of the sensor values.Main results: By using the training data of single actions only, i.e., power grasp or wrist movements, subjects achieved an average success rate of 70.0% in the target achievement test using ridge regression. When combining wrist actions, e.g., pronating and flexing the wrist simultaneously, similar results were obtained with an average of 68.1%. If a power grasp is added to the pool of actions, combined actions are much more difficult to achieve (36.1%).Significance: To the best of our knowledge, for the first time, the effectiveness of tactile myography on single and combined actions is evaluated in a target achievement test. The present study includes 3 DoFs control instead of the two generally used in the literature. Additionally, we define a set of physiologically plausible muscle activation limits valid for most experiments of this kind.

Relationship Between Offline and Online Metrics in Myoelectric Pattern Recognition Control Based on Target Achievement Control Test.

Offline classification accuracy (CA) is a widely accepted measure to evaluate the performance in pattern recognition based myoelectric scheme. However, whether offline metrics are able to be transferred to evaluate or predict online performance is still unclear. In this study, the relationship between offline metrics and online metrics are analyzed. In offline scenario, global CA is biased, thus class-wise accuracy standard deviation (std) is proposed as a supplement. Target Achievement Control Test (TAC Test) is adopted in online scenario. Completion rate, completion time and path efficiency are considered as online metrics. Our results demonstrate that online completion rate is strongly correlated with offline global CA and class-wise accuracy std. The correlation between offline and online performance metrics indicates it is reasonable to develop efficient algorithm in offline scenario if both global CA and class-wise accuracy are considered.

Control within a virtual environment is correlated to functional outcomes when using a physical prosthesis

BackgroundAdvances such as targeted muscle reinnervation and pattern recognition control may provide improved control of upper limb myoelectric prostheses, but evaluating user function remains challenging. Virtual environments are cost-effective and immersive tools that are increasingly used to provide practice and evaluate prosthesis control, but the relationship between virtual and physical outcomes—i.e., whether practice in a virtual environment translates to improved physical performance—is not understood.MethodsNine people with transhumeral amputations who previously had targeted muscle reinnervation surgery were fitted with a myoelectric prosthesis comprising a commercially available elbow, wrist, terminal device, and pattern recognition control system. Virtual and physical outcome measures were obtained before and after a 6-week home trial of the prosthesis.ResultsAfter the home trial, subjects showed statistically significant improvements (p < 0.05) in offline classification error, the virtual Target Achievement Control test, and the physical Southampton Hand Assessment Procedure and Box and Blocks Test. A trend toward improvement was also observed in the physical Clothespin Relocation task and Jebsen-Taylor test; however, these changes were not statistically significant. The median completion time in the virtual test correlated strongly and significantly with the Southampton Hand Assessment Procedure (p = 0.05, R = − 0.86), Box and Blocks Test (p = 0.007, R = − 0.82), Jebsen-Taylor Test (p = 0.003, R = 0.87), and the Assessment of Capacity for Myoelectric Control (p = 0.005,R = − 0.85). The classification error performance only had a significant correlation with the Clothespin Relocation Test (p = 0.018, R = .76).ConclusionsIn-home practice with a pattern recognition-controlled prosthesis improves functional control, as measured by both virtual and physical outcome measures. However, virtual measures need to be validated and standardized to ensure reliability in a clinical or research setting.Trial registrationThis is a registered clinical trial: NCT03097978.

Upper Limb Prosthesis Control for High-Level Amputees via Myoelectric Recognition of Leg Gestures.

Recognition of motion intent via surface electromyography (EMG) has become increasingly practical for prosthesis control, but lacking residual muscle sites remains a major obstacle to its use by high-level amputees. Currently, there are few approaches to upper limb prosthesis control for individuals with amputations proximal to the elbow, all of which suffer from one or more of three primary problems: invasiveness, the need for intensive training, and lacking functionality. Using surface EMG sensors placed on the lower leg and a natural mapping between degrees of freedom of the leg and the arm, we tested a noninvasive control approach by which high-level amputees could control prosthetic elbow, wrist, and hand movements with minimal training. In this paper, we used able-bodied subjects to facilitate a direct comparison between control using intact arm and leg muscles. First, we found that foot gestures could be classified offline using time domain features and linear discriminant analysis with accuracy comparable to an equivalent system for recognizing arm movements. Second, we used the target achievement control test to evaluate real-time control performance in three and four degrees of freedom. After approximately 20 min of training, subjects tended to perform the task as well with the leg as with intact arm muscles, and performance overall was comparable to other control methods.

Evaluation of Computer-Based Target Achievement Tests for Myoelectric Control.

Real-time evaluation of novel prosthetic control schemes is critical for translational research on artificial limbs. Recently, two computer-based, real-time evaluation tools, the target achievement control (TAC) test and the Fitts’ law test (FLT), have been proposed to assess real-time controllability. Whereas TAC tests provides an anthropomorphic visual representation of the limb at the cost of confusing visual feedback, FLT clarifies the current and target locations by simplified non-anthropomorphic representations. Here, we investigated these two approaches and quantified differences in common performance metrics that can result from the chosen method of visual feedback. Ten able-bodied and one amputee subject performed target achievement tasks corresponding to the FLT and TAC test with equivalent indices of difficulty. Able-bodied subjects exhibited significantly (p <0.05) better completion rate, path efficiency, and overshoot when performing the FLT, although no significant difference was seen in throughput performance. The amputee subject showed significantly better performance in overshoot at the FLT, but showed no significant difference in completion rate, path efficiency, and throughput. Results from the FLT showed a strong linear relationship between the movement time and the index of difficulty (R2 = 0.96), whereas TAC test results showed no apparent linear relationship (R2 = 0.19). These results suggest that in relatively similar conditions, the confusing location of virtual limb representation used in the TAC test contributed to poorer performance. Establishing an understanding of the biases of various evaluation protocols is critical to the translation of research into clinical practice.

Target Achievement Control Test: Evaluating real-time myoelectric pattern-recognition control of multifunctional upper-limb prostheses

Despite high classification accuracies (~95%) of myoelectric control systems based on pattern recognition, how well offline measures translate to real-time closed-loop control is unclear. Recently, a real-time virtual test analyzed how well subjects completed arm motions using a multiple-degree of freedom (DOF) classifier. Although this test provided real-time performance metrics, the required task was oversimplified: motion speeds were normalized and unintended movements were ignored. We included these considerations in a new, more challenging virtual test called the Target Achievement Control Test (TAC Test). Five subjects with transradial amputation attempted to move a virtual arm into a target posture using myoelectric pattern recognition, performing the test with various classifier (1- vs 3-DOF) and task complexities (one vs three required motions per posture). We found no significant difference in classification accuracy between the 1- and 3-DOF classifiers (97.2% +/- 2.0% and 94.1% +/- 3.1%, respectively; p = 0.14). Subjects completed 31% fewer trials in significantly more time using the 3-DOF classifier and took 3.6 +/- 0.8 times longer to reach a three-motion posture compared with a one-motion posture. These results highlight the need for closed-loop performance measures and demonstrate that the TAC Test is a useful and more challenging tool to test real-time pattern-recognition performance.