| 1. |
- Antfolk, Christian, et al.
(author)
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Transfer of tactile input from an artificial hand to the forearm: experiments in amputees and able-bodied volunteers
- 2013
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In: Disability and Rehabilitation: Assistive Technology. - : Informa UK Limited. - 1748-3115 .- 1748-3107. ; 8:3, s. 249-254
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Journal article (peer-reviewed)abstract
- Abstract in UndeterminedPurpose:This study explores the possibilities of transferring peripheral tactile stimulations from an artificial hand to the forearm skin.Method:A tactile display applied to the forearm skin was used to transfer tactile input to the forearm from various locations on a hand displayed on a computer screen. Discernment of location, levels of pressure and a combination of the two in simulated functional grips was tested to quantify the participants' ability to accurately perceive the tactile stimulations presented. Ten participants (5 forearm amputees and 5 able-bodied volunteers) unfamiliar with the equipment participated in the three-stage experiments comprising a learning session with vision, a reinforced learning session without vision and a validation session without vision.Results:The location discernment accuracy was high in both groups (75.2% and 89.6% respectively). The capacity to differentiate between three different levels of pressure was also high (91.7% and 98.1% respectively in the two groups). Recognition of simulated grip was slightly more difficult with the groups scoring 58.7% and 68.0% respectively for accuracy in the validation session.Conclusions:This study demonstrates that it is possible, following a brief training period, to transfer tactile input from an artificial hand to the forearm skin. The level of accuracy was lower for the more complex task, simulated grip recognition, possibly because this represents a more complex task requiring higher order brain functions. These results could form the basis for developing sensory feedback in hand prostheses. [Box: see text].
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| 2. |
- Cipriani, Christian, et al.
(author)
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A novel concept for a prosthetic hand with bidirectional non-invasive interface: a feasibility study
- 2009
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In: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 56:11, s. 2739-2743
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Journal article (peer-reviewed)abstract
- Abstract in Undetermined A conceptually novel prosthesis consisting of a mechatronic hand, an electromyographic classifier, and a tactile display has been developed and evaluated by addressing problems related to controllability in prosthetics: intention extraction, perception, and feeling of ownership. Experiments have been performed, and encouraging results for a young transradial amputee are reported.
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| 3. |
- Antfolk, Christian, et al.
(author)
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Artificial Redirection of Sensation From Prosthetic Fingers to the Phantom Hand Map on Transradial Amputees: Vibrotactile Versus Mechanotactile Sensory Feedback
- 2013
-
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 21:1, s. 112-120
-
Journal article (peer-reviewed)abstract
- This work assesses the ability of transradial amputees to discriminate multi-site tactile stimuli in sensory discrimination tasks. It compares different sensory feedback modalities using an artificial hand prosthesis in: 1) a modality matched paradigm where pressure recorded on the five fingertips of the hand was fed back as pressure stimulation on five target points on the residual limb; and 2) a modality mismatched paradigm where the pressures were transformed into mechanical vibrations and fed back. Eight transradial amputees took part in the study and were divided in two groups based on the integrity of their phantom map; group A had a complete phantom map on the residual limb whereas group B had an incomplete or nonexisting map. The ability in localizing stimuli was compared with that of 10 healthy subjects using the vibration feedback and 11 healthy subjects using the pressure feedback (in a previous study), on their forearms, in similar experiments. Results demonstrate that pressure stimulation surpassed vibrotactile stimulation in multi-site sensory feedback discrimination. Furthermore, we demonstrate that subjects with a detailed phantom map had the best discrimination performance and even surpassed healthy participants for both feedback paradigms whereas group B had the worst performance overall. Finally, we show that placement of feedback devices on a complete phantom map improves multi-site sensory feedback discrimination, independently of the feedback modality.
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| 4. |
- Antfolk, Christian, et al.
(author)
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Sensory feedback in upper limb prosthetics.
- 2013
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In: Expert Review of Medical Devices. - : Informa UK Limited. - 1745-2422 .- 1743-4440. ; 10:1, s. 45-54
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Journal article (peer-reviewed)abstract
- One of the challenges facing prosthetic designers and engineers is to restore the missing sensory function inherit to hand amputation. Several different techniques can be employed to provide amputees with sensory feedback: sensory substitution methods where the recorded stimulus is not only transferred to the amputee, but also translated to a different modality (modality-matched feedback), which transfers the stimulus without translation and direct neural stimulation, which interacts directly with peripheral afferent nerves. This paper presents an overview of the principal works and devices employed to provide upper limb amputees with sensory feedback. The focus is on sensory substitution and modality matched feedback; the principal features, advantages and disadvantages of the different methods are presented.
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| 5. |
- Antfolk, Christian, et al.
(author)
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Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System
- 2010
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In: Journal of Medical and Biological Engineering. - : Taiwanese Society of Biomedical Engineering. - 1609-0985. ; 30:6, s. 399-405
-
Journal article (peer-reviewed)abstract
- All commercially available upper limb prosthesis controllers only allow the hand to be commanded in an open and close fashion without any sensory feedback to the user. Here the evaluation of a multi-degree of freedom hand controlled using a real-time EMG pattern recognition algorithm and incorporating a sensory feedback system is reported. The hand prosthesis, called SmartHand, was controlled in real-time by using 16 myoelectric signals from the residual limb of a 25-year old male transradial amputee in a two day long evaluation session. Initial training of the EMG pattern recognition algorithm was performed with a dataglove fitted to the contralateral hand recording joint angle positions of the fingers and mapping joint angles of the fingers to the EMG data. In the following evaluation sessions, the myoelectric signals were classified using local approximation and lazy learning, producing finger joint angle outputs and consequently controlling the prosthetic hand. Sensory information recorded from force sensors in the artificial hand was relayed to actuators, integrated in the socket of the prosthesis, continuously delivering force sensory feedback stimulations to the stump of the amputee. The participant was able to perform several dextrous movements as well as functional grip tasks after only two hours of training and increased his controllability during the two day session. In the final evaluation session a mean classification accuracy of 86% was achieved.
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| 6. |
- Cipriani, Christian, et al.
(author)
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Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
- 2011
-
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 19:3, s. 260-270
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Journal article (peer-reviewed)abstract
- A real-time pattern recognition algorithm based on k-nearest neighbors and lazy learning was used to classify, voluntary electromyography (EMG) signals and to simultaneously control movements of a dexterous artificial hand. EMG signals were superficially recorded by eight pairs of electrodes from the stumps of five transradial amputees and forearms of five able-bodied participants and used online to control a robot hand. Seven finger movements (not involving the wrist) were investigated in this study. The first objective was to understand whether and to which extent it is possible to control continuously and in real-time, the finger postures of a prosthetic hand, using superficial EMG, and a practical classifier, also taking advantage of the direct visual feedback of the moving hand. The second objective was to calculate statistical differences in the performance between participants and groups, thereby assessing the general applicability of the proposed method. The average accuracy of the classifier was 79% for amputees and 89% for able-bodied participants. Statistical analysis of the data revealed a difference in control accuracy based on the aetiology of amputation, type of prostheses regularly used and also between able-bodied participants and amputees. These results are encouraging for the development of noninvasive EMG interfaces for the control of dexterous prostheses.
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| 7. |
- Kanitz, Gunter, et al.
(author)
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Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.
- 2011
-
In: [Host publication title missing]. - 1557-170X. - 9781424441211 ; , s. 1608-1611
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Conference paper (peer-reviewed)abstract
- In this paper we present surface electromyographic (EMG) data collected from 16 channels on five unimpaired subjects and one transradial amputee performing 12 individual finger movements and a rest class. EMG were processed using a traditional Time Domain feature-set and classifiers: a Linear Discriminant Analysis (LDA) a k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM). Using continuous datasets we show that it is possible to achieve an accuracy up to 80% across subjects. Thereafter possibilities to reduce the numbers of channels physically required, as well as the number of features have been investigated by means of a developed Genetic Algorithm (GA) that included a bonus system to reward eliminated features and channels. The classification was performed firstly on the full datasets and in later runs using the GA. The GA demonstrated high redundancy in the recorded 16 channel data as well as the insignificance of certain features. Although the GA optimization yielded to reduce 8 to 11 channels depending on the subject, such reduction had little to no effect on the classification accuracies.
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| 8. |
- Malesevic, Nebojsa, et al.
(author)
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A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions
- 2020
-
In: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1
-
Journal article (peer-reviewed)abstract
- Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners.
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| 9. |
- Malešević, Nebojša, et al.
(author)
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Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM)
- 2017
-
In: 2017 International Conference on Rehabilitation Robotics, ICORR 2017. - 9781538622964 ; , s. 1518-1523
-
Conference paper (peer-reviewed)abstract
- In this paper we present a novel method for predicting individual fingers movements from surface electromyography (EMG). The method is intended for real-time dexterous control of a multifunctional prosthetic hand device. The EMG data was recorded using 16 single-ended channels positioned on the forearm of healthy participants. Synchronously with the EMG recording, the subjects performed consecutive finger movements based on the visual cues. Our algorithm could be described in following steps: extracting mean average value (MAV) of the EMG to be used as the feature for classification, piece-wise linear modeling of EMG feature dynamics, implementation of hierarchical hidden Markov models (HHMM) to capture transitions between linear models, and implementation of Bayesian inference as the classifier. The performance of our classifier was evaluated against commonly used real-time classifiers. The results show that the current algorithm setup classifies EMG data similarly to the best among tested classifiers but with equal or less computational complexity.
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| 10. |
- Malešević, Nebojša, et al.
(author)
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Instrumented platform for assessment of isometric hand muscles contractions
- 2019
-
In: Measurement Science and Technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 30:6
-
Journal article (peer-reviewed)abstract
- Measurement of forces exerted by a human hand while performing common gestures is a highly valuable task for assessment of neurorehabilitation and neurological disorders, but also, for control of movement that could be directly transferred to assistive devices. Even though accurate and selective multi-joint measurement of hand forces is desirable in both clinical and research applications there is no commercially available device able to perform such measurements. Moreover, the custom-made systems used in research commonly impose limitations, such as availability of only single, predefined hand aperture. Furthermore, there is no consensus on design requirements for custom made measurement systems that would enable comparison of results obtained during research or clinical hand function studies. In an attempt to provide a possible solution for a device capable of multi-joint hand forces measurement and disseminate it to the research community, this paper presents the mechanical and electronic design of an instrumented platform for assessment of isometric hand muscles contractions. Some of the key features related to the developed system are: flexibility in placing the hand/fingers, fast and easy hand fitting, adjustability to different lengths, circumferences and postures of the digits, and the possibility to register individual bidirectional forces from the digits and the wrist. The accuracy of isometric force measurements was evaluated in a controlled test with the reference high accuracy force gauge device during which the developed system showed high linearity (R 2 = 0.9999). As the more realistic test, the device was evaluated when force was applied to individual sensors but also during the intramuscular electromyography (iEMG) study. The data gathered during the iEMG measurements was thoroughly assessed to obtain three appropriate metrics; the first estimating crosstalk between individual force sensors; the second evaluating agreement between measured forces and forces estimated through iEMG; and the third providing qualitative evaluation of hand force in respect to activations of individual muscle units. The results of these analyses performed on multiple joint forces show agreement with previously published results, but with the difference that in that case, the measurement was performed with a single degree of freedom device.
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| 11. |
- Malešević, Nebojša, et al.
(author)
-
Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals
- 2018
-
In: Complexity. - : Hindawi Limited. - 1076-2787 .- 1099-0526. ; 2018
-
Journal article (peer-reviewed)abstract
- We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography. The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal. Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed. The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements. Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier. The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application. The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations. The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.
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| 12. |
- Rosén, Birgitta, et al.
(author)
-
Referral of sensation to an advanced humanoid robotic hand prosthesis.
- 2009
-
In: Scandinavian Journal of Plastic and Reconstructive Surgery and Hand Surgery. - : Informa UK Limited. - 1651-2073 .- 0284-4311. ; 43:5, s. 260-266
-
Journal article (peer-reviewed)abstract
- Hand prostheses that are currently available on the market are used by amputees to only a limited extent, partly because of lack of sensory feedback from the artificial hand. We report a pilot study that showed how amputees can experience a robot-like advanced hand prosthesis as part of their own body. We induced a perceptual illusion by which touch applied to the stump of the arm was experienced from the artificial hand. This illusion was elicited by applying synchronous tactile stimulation to the hidden amputation stump and the robotic hand prosthesis in full view. In five people who had had upper limb amputations this stimulation caused referral touch sensation from the stump to the artificial hand, and the prosthesis was experienced more like a real hand. We also showed that this illusion can work when the amputee controls the movements of the artificial hand by recordings of the arm muscle activity with electromyograms. These observations indicate that the previously described "rubber hand illusion" is also valid for an advanced hand prosthesis, even when it has a robotic-like appearance.
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| 13. |
- Cipriani, Christian, et al.
(author)
-
Humans can integrate feedback of discrete events in their sensorimotor control of a robotic hand
- 2014
-
In: Experimental Brain Research. - : Springer Science and Business Media LLC. - 0014-4819 .- 1432-1106. ; 232:11, s. 3421-3429
-
Journal article (peer-reviewed)abstract
- Providing functionally effective sensory feedback to users of prosthetics is a largely unsolved challenge. Traditional solutions require high band-widths for providing feedback for the control of manipulation and yet have been largely unsuccessful. In this study, we have explored a strategy that relies on temporally discrete sensory feedback that is technically simple to provide. According to the Discrete Event-driven Sensory feedback Control (DESC) policy, motor tasks in humans are organized in phases delimited by means of sensory encoded discrete mechanical events. To explore the applicability of DESC for control, we designed a paradigm in which healthy humans operated an artificial robot hand to lift and replace an instrumented object, a task that can readily be learned and mastered under visual control. Assuming that the central nervous system of humans naturally organizes motor tasks based on a strategy akin to DESC, we delivered short-lasting vibrotactile feedback related to events that are known to forcefully affect progression of the grasp-lift-and-hold task. After training, we determined whether the artificial feedback had been integrated with the sensorimotor control by introducing short delays and we indeed observed that the participants significantly delayed subsequent phases of the task. This study thus gives support to the DESC policy hypothesis. Moreover, it demonstrates that humans can integrate temporally discrete sensory feedback while controlling an artificial hand and invites further studies in which inexpensive, noninvasive technology could be used in clever ways to provide physiologically appropriate sensory feedback in upper limb prosthetics with much lower band-width requirements than with traditional solutions.
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| 14. |
- Clemente, Francesco, et al.
(author)
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Non-Invasive, Temporally Discrete Feedback of Object Contact and Release Improves Grasp Control of Closed-Loop Myoelectric Transradial Prostheses
- 2016
-
In: IEEE transactions on neural systems and rehabilitation engineering. - 1534-4320 .- 1558-0210. ; 24:12, s. 1314-1322
-
Journal article (peer-reviewed)abstract
- Human grasping and manipulation control critically depends on tactile feedback. Without this feedback, the ability for fine control of a prosthesis is limited in upper limb amputees. Although various approaches have been investigated in the past, at present there is no commercially available device able to restore tactile feedback in upper limb amputees. Based on the Discrete Event-driven Sensory feedback Control (DESC) policy we present a device able to deliver short-lasting vibrotactile feedback to transradial amputees using commercially available myoelectric hands. The device (DESC-glove) comprises sensorized thimbles to be placed on the prosthesis digits, a battery-powered electronic board, and vibrating units embedded in an arm-cuff being transiently activated when the prosthesis makes and breaks contact with objects. The consequences of using the DESC-glove were evaluated in a longitudinal study. Five transradial amputees were equipped with the device for onemonth at home. Through a simple test proposed here for the first time-the virtual eggs test-we demonstrate the effectiveness of the device for prosthetic control in daily life conditions. In the future the device could be easily exploited as an add-on to complement myoelectric prostheses or even embedded in prosthetic sockets to enhance their control by upper limb amputees.
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| 15. |
- Clemente, Francesco, et al.
(author)
-
Touch and Hearing Mediate Osseoperception
- 2017
-
In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 7
-
Journal article (peer-reviewed)abstract
- Osseoperception is the sensation arising from the mechanical stimulation of a bone-anchored prosthesis. Here we show that not only touch, but also hearing is involved in this phenomenon. Using mechanical vibrations ranging from 0.1 to 6 kHz, we performed four psychophysical measures (perception threshold, sensation discrimination, frequency discrimination and reaction time) on 12 upper and lower limb amputees and found that subjects: consistently reported perceiving a sound when the stimulus was delivered at frequencies equal to or above 400 Hz; were able to discriminate frequency differences between stimuli delivered at high stimulation frequencies (similar to 1500 Hz); improved their reaction time for bimodal stimuli (i.e. when both vibration and sound were perceived). Our results demonstrate that osseoperception is a multisensory perception, which can explain the improved environment perception of bone-anchored prosthesis users. This phenomenon might be exploited in novel prosthetic devices to enhance their control, thus ultimately improving the amputees' quality of life.
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| 16. |
- D'Accolti, Daniele, et al.
(author)
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Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis
- 2023
-
In: IEEE Robotics and Automation Letters. - 2377-3766. ; 8:2, s. 1045-1052
-
Journal article (peer-reviewed)abstract
- 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 (p < 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.
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| 17. |
- Earley, Eric, 1989, et al.
(author)
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Cutting Edge Bionics in Highly Impaired Individuals: A Case of Challenges and Opportunities
- 2024
-
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 32, s. 1013-1022
-
Journal article (peer-reviewed)abstract
- Highly impaired individuals stand to benefit greatly from cutting-edge bionic technology, however concurrent functional deficits may complicate the adaptation of such technology. Here, we present a case in which a visually impaired individual with bilateral burn injury amputation was provided with a novel transradial neuromusculoskeletal prosthesis comprising skeletal attachment via osseointegration and implanted electrodes in nerves and muscles for control and sensory feedback. Difficulties maintaining implant hygiene and donning and doffing the prosthesis arose due to his contralateral amputation, ipsilateral eye loss, and contralateral impaired vision necessitating continuous adaptations to the electromechanical interface. Despite these setbacks, the participant still demonstrated improvements in functional outcomes and the ability to control the prosthesis in various limb positions using the implanted electrodes. Our results demonstrate the importance of a multidisciplinary, iterative, and patient-centered approach to making cutting-edge technology accessible to patients with high levels of impairment.
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| 18. |
- Kanitz, Gunter, et al.
(author)
-
Classification of Transient Myoelectric Signals for the Control of Multi-Grasp Hand Prostheses
- 2018
-
In: IEEE transactions on neural systems and rehabilitation engineering. - : IEEE. - 1534-4320 .- 1558-0210. ; 26:9, s. 1756-1764
-
Journal article (peer-reviewed)abstract
- Understanding the neurophysiological signals underlying voluntary motor control and decoding them for controlling limb prostheses is one of the major challenges in applied neuroscience and rehabilitation engineering. While pattern recognition of continuous myoelectric (EMG) signals is arguably the most investigated approach for hand prosthesis control, its underlying assumption is poorly supported, i.e., that repeated muscular contractions produce consistent patterns of steady-state EMGs. In fact, it still remains to be shown that pattern recognition-based controllers allow natural control over multiple grasps in hand prosthesis outside well-controlled laboratory settings. Here, we propose an approach that relies on decoding the intended grasp from forearm EMG recordings associated with the onset of muscle contraction as opposed to the steady-state signals. Eight unimpaired individuals and two hand amputees performed four grasping movements with a variety of arm postures while EMG recordings subsequently processed to mimic signals picked up by conventional myoelectric sensors were obtained from their forearms and residual limbs, respectively. Off-line data analyses demonstrated the feasibility of the approach also with respect to the limb position effect. The sampling frequency and length of the classified EMG window that off-line resulted in optimal performance were applied to a controller of a research prosthesis worn by one hand amputee and proved functional in real-time when operated under realistic working conditions.
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| 19. |
- Malešević, Nebojša, et al.
(author)
-
A database of high-density surface electromyogram signals comprising 65 isometric hand gestures
- 2021
-
In: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 8:1
-
Journal article (peer-reviewed)abstract
- Control of contemporary, multi-joint prosthetic hands is commonly realized by using electromyographic signals from the muscles remaining after amputation at the forearm level. Although this principle is trying to imitate the natural control structure where muscles control the joints of the hand, in practice, myoelectric control provides only basic hand functions to an amputee using a dexterous prosthesis. This study aims to provide an annotated database of high-density surface electromyographic signals to aid the efforts of designing robust and versatile electromyographic control interfaces for prosthetic hands. The electromyographic signals were recorded using 128 channels within two electrode grids positioned on the forearms of 20 able-bodied volunteers. The participants performed 65 different hand gestures in an isometric manner. The hand movements were strictly timed using an automated recording protocol which also synchronously recorded the electromyographic signals and hand joint forces. To assess the quality of the recorded signals several quantitative assessments were performed, such as frequency content analysis, channel crosstalk, and the detection of poor skin-electrode contacts.
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| 20. |
- Malesevic, Nebojsa, et al.
(author)
-
Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography
- 2022
-
In: Sensors. - : MDPI AG. - 1424-8220. ; 22:13
-
Journal article (peer-reviewed)abstract
- Although seemingly effortless, the control of the human hand is backed by an elaborate neuro-muscular mechanism. The end result is typically a smooth action with the precise positioning of the joints of the hand and an exerted force that can be modulated to enable precise interaction with the surroundings. Unfortunately, even the most sophisticated technology cannot replace such a comprehensive role but can offer only basic hand functionalities. This issue arises from the drawbacks of the prosthetic hand control strategies that commonly rely on surface EMG signals that contain a high level of noise, thus limiting accurate and robust multi-joint movement estimation. The use of intramuscular EMG results in higher quality signals which, in turn, lead to an improvement in prosthetic control performance. Here, we present the evaluation of fourteen common/well-known algorithms (mean absolute value, variance, slope sign change, zero crossing, Willison amplitude, waveform length, signal envelope, total signal energy, Teager energy in the time domain, Teager energy in the frequency domain, modified Teager energy, mean of signal frequencies, median of signal frequencies, and firing rate) for the direct and proportional control of a prosthetic hand. The method involves the estimation of the forces generated in the hand by using different algorithms applied to iEMG signals from our recently published database, and comparing them to the measured forces (ground truth). The results presented in this paper are intended to be used as a baseline performance metric for more advanced algorithms that will be made and tested using the same database.
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