| 1. |
- Antfolk, Christian, et al.
(author)
-
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.
|
|
| 2. |
- Clemente, Francesco, et al.
(author)
-
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.
|
|
| 3. |
- Antfolk, Christian, et al.
(author)
-
Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System
- 2010
-
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.
|
|
| 4. |
- Boni, Irene, et al.
(author)
-
Restoring Natural Forearm Rotation in Transradial Osseointegrated Amputees
- 2018
-
In: IEEE Transactions on Neural Systems and Rehabilitation Engineering. - 1558-0210 .- 1534-4320. ; 26:12, s. 2333-2341
-
Journal article (peer-reviewed)abstract
- Osseointegrated transradial prostheses have the potential to preserve the natural range of wrist rotation, which improves the performance of activities of daily living and reduces compensatory movements that potentially lead to secondary health problems over time. This is possible by enabling the radius and the ulna bone to move with respect to each other, restoring the functionality of the original distal-radioulnar joint. In this paper, we report on psychophysics tests performed on an osseointegrated transradial amputee with the aim to understand the extent of mobility of the implants that is required to preserve the natural forearm rotation. Based on these experiments, we designed and developed an attachment device between the implants and the hand prosthesis that serves as an artificial distal radio-ulnar joint. This device was fitted on an osseointegrated transradial amputee and its functionality assessed by means of the Southampton Hand Assessment Procedure (SHAP) and the Minnesota Manual Dexterity test (MMDT). We found that the axial rotation of the implants is required to preserve forearm rotation, to distribute loads equally over the two implants (60% radius - 40% ulna), and to enable loading of the implants without unpleasant feelings for the patient. Higher function was recorded when our attachment device enabled forearm rotation: SHAP from 61 to 71, MMDT from 258s to 231s. Natural forearm rotation can be successfully restored in transradial amputees by using osseointegration and our novel mechanical attachment to the hand prosthesis.
|
|
| 5. |
- Cipriani, Christian, et al.
(author)
-
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
-
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.
|
|
| 6. |
- Malešević, Nebojša, et al.
(author)
-
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.
|
|
| 7. |
- Malešević, Nebojša, et al.
(author)
-
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.
|
|
| 8. |
- 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.
|
|
| 9. |
- Mastinu, Enzo, 1987, et al.
(author)
-
Neural feedback strategies to improve grasping coordination in neuromusculoskeletal prostheses
- 2020
-
In: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 10:1
-
Journal article (peer-reviewed)abstract
- Conventional prosthetic arms suffer from poor controllability and lack of sensory feedback. Owing to the absence of tactile sensory information, prosthetic users must rely on incidental visual and auditory cues. In this study, we investigated the effect of providing tactile perception on motor coordination during routine grasping and grasping under uncertainty. Three transhumeral amputees were implanted with an osseointegrated percutaneous implant system for direct skeletal attachment and bidirectional communication with implanted neuromuscular electrodes. This neuromusculoskeletal prosthesis is a novel concept of artificial limb replacement that allows to extract control signals from electrodes implanted on viable muscle tissue, and to stimulate severed afferent nerve fibers to provide somatosensory feedback. Subjects received tactile feedback using three biologically inspired stimulation paradigms while performing a pick and lift test. The grasped object was instrumented to record grasping and lifting forces and its weight was either constant or unexpectedly changed in between trials. The results were also compared to the no-feedback control condition. Our findings confirm, in line with the neuroscientific literature, that somatosensory feedback is necessary for motor coordination during grasping. Our results also indicate that feedback is more relevant under uncertainty, and its effectiveness can be influenced by the selected neuromodulation paradigm and arguably also the prior experience of the prosthesis user.
|
|
