| 1. |
- Antfolk, Christian, et al.
(författare)
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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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Ingår i: Disability and Rehabilitation: Assistive Technology. - : Informa UK Limited. - 1748-3115 .- 1748-3107. ; 8:3, s. 249-254
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Tidskriftsartikel (refereegranskat)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.
(författare)
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A novel concept for a prosthetic hand with bidirectional non-invasive interface: a feasibility study
- 2009
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Ingår i: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 56:11, s. 2739-2743
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Tidskriftsartikel (refereegranskat)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.
(författare)
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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
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 21:1, s. 112-120
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Sensory feedback in upper limb prosthetics.
- 2013
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Ingår i: Expert Review of Medical Devices. - : Informa UK Limited. - 1745-2422 .- 1743-4440. ; 10:1, s. 45-54
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Tidskriftsartikel (refereegranskat)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.
(författare)
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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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Ingår i: Journal of Medical and Biological Engineering. - : Taiwanese Society of Biomedical Engineering. - 1609-0985. ; 30:6, s. 399-405
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Online Myoelectric Control of a Dexterous Hand Prosthesis by Transradial Amputees
- 2011
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Ingår i: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1534-4320. ; 19:3, s. 260-270
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Decoding of individuated finger movements using surface EMG and input optimization applying a genetic algorithm.
- 2011
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Ingår i: [Host publication title missing]. - 1557-170X. - 9781424441211 ; , s. 1608-1611
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Konferensbidrag (refereegranskat)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.
(författare)
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A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions
- 2020
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Ingår i: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 7:1
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Tidskriftsartikel (refereegranskat)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.
(författare)
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Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM)
- 2017
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Ingår i: 2017 International Conference on Rehabilitation Robotics, ICORR 2017. - 9781538622964 ; , s. 1518-1523
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Konferensbidrag (refereegranskat)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.
(författare)
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Instrumented platform for assessment of isometric hand muscles contractions
- 2019
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Ingår i: Measurement Science and Technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 30:6
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Tidskriftsartikel (refereegranskat)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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