| 1. |
- Oddo, Calogero Maria, 1983, et al.
(författare)
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A mechatronic platform for human touch studies
- 2011
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Ingår i: Mechatronics. - : Elsevier BV. - 0957-4158. ; 21:3, s. 604-613
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Tidskriftsartikel (refereegranskat)abstract
- The development of a mechatronic tactile stimulation platform for touch studies is presented. The platform was developed for stimulation of the fingertip using textured surfaces, providing repeatable tangential sliding motion of stimuli with controlled indentation force. Particular requirements were addressed to make the platform suitable for neurophysiological studies in humans with particular reference to electrophysiological measurements, but allowing a variety of other studies too, such as psychophysical, tri-bological and artificial touch ones. The design of the mechatronic tactile stimulator is detailed, as well as the performance in tracking reference trajectories. Using microneurography, we recorded from human tactile afferents and validated the platform compatibility with the exacting demands of electrophysiological methods, comprising the absence of spurious vibrations and the lack of relevant electromagnetic interference.
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| 2. |
- Oddo, Calogero Maria, 1983, et al.
(författare)
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Roughness Encoding in Human and Biomimetic Artificial Touch: Spatiotemporal Frequency Modulation and Structural Anisotropy of Fingerprints
- 2011
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 11:6, s. 5596-5615
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Tidskriftsartikel (refereegranskat)abstract
- The influence of fingerprints and their curvature in tactile sensing performance is investigated by comparative analysis of different design parameters in a biomimetic artificial fingertip, having straight or curved fingerprints. The strength in the encoding of the principal spatial period of ridged tactile stimuli (gratings) is evaluated by indenting and sliding the surfaces at controlled normal contact force and tangential sliding velocity, as a function of fingertip rotation along the indentation axis. Curved fingerprints guaranteed higher directional isotropy than straight fingerprints in the encoding of the principal frequency resulting from the ratio between the sliding velocity and the spatial periodicity of the grating. In parallel, human microneurography experiments were performed and a selection of results is included in this work in order to support the significance of the biorobotic study with the artificial tactile system.
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| 3. |
- 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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| 4. |
- 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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| 5. |
- 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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| 6. |
- Laschi, Cecilia, et al.
(författare)
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A Bio-inspired Neural Sensory-Motor Coordination Scheme for Robot Reaching and Preshaping
- 2006
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Ingår i: Proceedings of the First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics : BioRob 2006. - Piscataway, NJ : IEEE. - 1424400406 ; , s. 531-536
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a sensory-motor coordination scheme for a robot hand-arm-head system that provides the robot with the capability to reach for and to grasp an object, while pre-shaping the fingers to the required grasp configuration. A model for sensory-motor coordination derived from studies in humans inspired the development of the scheme. A special feature of this model is the prediction of the tactile image perceived after grasping. The proposed scheme is based on a neuro-fuzzy modnle that, after a learning phase, starting from visual data, calculates the position and orientation of the hand for grasping, selects the best-suited hand configuration, and predicts the tactile feedback after grasping. The implementation of the scheme on a humanoid robot ailowed experimental validation of its effectiveness in robotics and provided perspectives on applications of sensory predictions in robot motor control.
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