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Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis
No full textObjective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context-and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semiautomatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees
Sensory Feedback in Prosthetics: A Standardized Test Bench for Closed-Loop Control
No full textClosing the control loop by providing sensory feedback to the user of a prosthesis is an important challenge, with major impact on the future of prosthetics. Developing and comparing closed-loop systems is a difficult task, since there are many different methods and technologies that can be used to implement each component of the system. Here, we present a test bench developed in Matlab Simulink for configuring and testing the closed-loop human control system in standardized settings. The framework comprises a set of connected generic blocks with normalized inputs and outputs, which can be customized by selecting specific implementations from a library of predefined components. The framework is modular and extensible and it can be used to configure, compare and test different closed-loop system prototypes, thereby guiding the development towards an optimal system configuration. The use of the test bench was demonstrated by investigating two important aspects of closed-loop control: performance of different electrotactile feedback interfaces (spatial versus intensity coding) during a pendulum stabilization task and feedforward methods (joystick versus myocontrol) for force control. The first experiment demonstrated that in the case of trained subjects the intensity coding might be superior to spatial coding. In the second experiment, the control of force was rather poor even with a stable and precise control interface (joystick), demonstrating that inherent characteristics of the prosthesis can be an important limiting factor when considering the overall effectiveness of the closed-loop control. The presented test bench is an important instrument for investigating different aspects of human manual control with sensory feedback
Feasibility test of activity index summary metric in human hand activity recognition
No full textActivity monitoring is a technique for assessing the physical activity that a
person undertakes over some time. Activity Index (AI) is a metric that
summarizes the raw measurements from tri-axial accelerometers, often used
for measuring physical activity. Our research compared the Activity Index
for different activity groups and hand usage [1]. We also tested this metric
as a classification feature, and how different data acquisition and
segmentation parameter configurations influence classification accuracy.
Data acquisition was done with a previously developed system that includes a
smartwatch on each wrist and a smartphone placed in the subject?s pocket;
raw data from smartwatch accelerometers was used for the analysis. We
calculated the Activity Index for labeled data segments and used ANOVA1
statistical test with Bonferroni correction. Significant differences were
found between cases of hand usage (left, right, none, both). In the next
analysis phase, the Activity Index was used as the classification feature
with three supervised machine learning algorithms-Support Vector Machine,
k-Nearest Neighbors, and Random Forest. The best accuracy (measured by F1
score) of classifying hand usage was achieved by using the Random Forest
algorithm, 50 Hz sampling frequency, and a window of 10 s without overlap
for AI calculation, and it was 97%. On the other hand, the classification of
activity groups had a low accuracy, which indicated that a specific activity
group can?t be identified by using only one simple feature.</jats:p
User adaptation in Myoelectric Man-Machine Interfaces
Full text linkState of the art clinical hand prostheses are controlled in a simple and limited way that allows the activation of one function at a time. More advanced laboratory approaches, based on machine learning, offer
a significant increase in functionality, but their clinical impact is limited, mainly due to lack of reliability. In this study, we analyse two conceptually different machine learning approaches, focusing on their
robustness and performance in a closed loop application. A classification (finite number of classes) and a regression (continuous mapping) based projection of EMG into external commands were applied
while artificially introducing non-stationarities in the EMG signals. When tested on ten able-bodied individuals and one transradial amputee,
the two methods were similarly influenced by non-stationarities
when tested offline. However, in online tests, where the user could
adapt his muscle activation patterns to the changed conditions, the
regression-based approach was significantly less influenced by the
changes in signal features than the classification approach.
This observation demonstrates, on the one hand, the importance of
online tests with users in the loop for assessing the performance of myocontrol approaches. On the other hand, it also demonstrates that
regression allows for a better user correction of control commands than
classification
Electrotactile and Vibrotactile Feedback Enable Similar Performance in Psychometric Tests and Closed-loop Control
No full textElectro-and vibro-tactile stimulation are commonly employed for feedback in closed-loop human-machine interfacing. Although these feedback systems have been extensively investigated individually, they are rarely objectively compared. In this study, two state-of-the-art stimulation units (concentric electrode and C2-tactor) similar in shape and size were compared in psychometric and online control tests. The just noticeable difference and number of discriminable levels for intensity and frequency modulation were determined across values of carrier frequency and intensity, respectively. Next, subjects performed a compensatory tracking task, in which the feedback encoded the momentary tracking error. In the psychometric tests, intensity modulation outperformed frequency modulation and electrotactile stimulation enabled significantly higher resolution than vibrotactile stimulation, for the same carrier frequency. However, for the best-case settings (eletro-tactile: 100 Hz; vibro-tactile: 200 Hz), the two stimulation modalities were equivalent in the psychometric tests and in the online control tests, where the two stimulation methods resulted in similar correlation and deviation between the target and the generated trajectory. Time delay was slightly but significantly lower for the vibrotactile modality. Overall, the present assessment shows that despite psychometric differences between the two stimulation methods, they enable similar online control performance when parameters are optimally selected for each modality
EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis
Full text linkBackground Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback). Methods As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task. Results During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees. Conclusions The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system
Improving bimanual interaction with a prosthesis using semi-autonomous control
Full text linkBACKGROUND: The loss of a hand is a traumatic experience that substantially compromises an individual's capability to interact with his environment. The myoelectric prostheses are state-of-the-art (SoA) functional replacements for the lost limbs. Their overall mechanical design and dexterity have improved over the last few decades, but the users have not been able to fully exploit these advances because of the lack of effective and intuitive control. Bimanual tasks are particularly challenging for an amputee since prosthesis control needs to be coordinated with the movement of the sound limb. So far, the bimanual activities have been often neglected by the prosthetic research community.METHODS: We present a novel method to prosthesis control, which uses a semi-autonomous approach in order to simplify bimanual interactions. The approach supplements the commercial SoA two-channel myoelectric control with two additional sensors. Two inertial measurement units were attached to the prosthesis and the sound hand to detect the movement of both limbs. Once a bimanual interaction is detected, the system mimics the coordination strategies of able-bodied subjects to automatically adjust the prosthesis wrist rotation (pronation, supination) and grip type (lateral, palmar) to assist the sound hand during a bimanual task. The system has been evaluated in eight able-bodied subjects performing functional uni- and bi-manual tasks using the novel method and SoA two-channel myocontrol. The outcome measures were time to accomplish the task, semi-autonomous system misclassification rate, subjective rating of intuitiveness, and perceived workload (NASA TLX).RESULTS: The results demonstrated that the novel control interface substantially outperformed the SoA myoelectric control. While using the semi-autonomous control the time to accomplish the task and the perceived workload decreased for 25 and 27%, respectively, while the subjects rated the system as more intuitive then SoA myocontrol.CONCLUSIONS: The novel system uses minimal additional hardware (two inertial sensors) and simple processing and it is therefore convenient for practical implementation. By using the proposed control scheme, the prosthesis assists the user's sound hand in performing bimanual interactions while decreasing cognitive burden.</p
Electrotactile EMG feedback improves the control of prosthesis grasping force
No full textObjective. A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate. Approach. In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively. Main results. Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements. Significance. Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for predictive control, as the subjects used the feedback to adjust the desired force even before the prosthesis contacted the object. In conclusion, the online emgFB was superior to the classic forceFB in realistic conditions that included electrotactile stimulation, limited feedback resolution (8 levels), cognitive processing delay, and time constraints (fast grasping).European Commission under the MYOSENS [FP7-PEOPLE-2011-IAPP-286208
Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping
No full textProsthesis users usually agree that myoelectric prostheses should be equipped with somatosensory feedback. However, the exact role of feedback and potential benefits are still elusive. The current study investigates the nature of human control processes within a specific context of routine grasping. Although the latter includes a fast feedforward control of the grasping force, the assumption was that the feedback would still be useful; it would communicate the outcome of the grasping trial, which the subjects could use to learn an internal model of feedforward control. Nine able-bodied subjects produced repeatedly a desired level of grasping force using different control configurations: feedback versus no-feedback, virtual versus real prosthetic hand, and joystick versus myocontrol. The outcome measures were the median and dispersion of the relative force errors. The results demonstrated that the feedback was successful in limiting the variability of the routine grasping due to uncertainties in the system and/or the command interface. The internal models of feedforward control could be employed by the subjects to control the prosthesis without the loss of performance even after the force feedback was removed. The models were, however, unstable over time, especially with myocontrol. Overall, the study demonstrates that the prosthesis system can be learned by the subjects using feedback. The feedback is also essential to maintain the model, and it could be delivered intermittently. This approach has practical advantages, but the level to which this mechanism can be truly exploited in practice depends directly on the consistency of the prosthesis control interface
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