Dosen, Strahinja

[["dc.bibliographiccitation.artnumber","55"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Somer, Kelef"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:41:46Z"],["dc.date.available","2019-07-09T11:41:46Z"],["dc.date.issued","2015"],["dc.description.abstract","Background 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."],["dc.identifier.doi","10.1186/s12984-015-0047-z"],["dc.identifier.pmid","26088323"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12335"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58507"],["dc.language.iso","en"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/286208/EU//MYOSENS"],["dc.relation.euproject","MYOSENS"],["dc.rights.access","openAccess"],["dc.rights.holder","Dosen et al."],["dc.title","EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.volume","15"],["dc.contributor.affiliation","Mouchoux, Jérémy; 1Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Göttingen, Georg-August University, Göttingen, Germany"],["dc.contributor.affiliation","Bravo-Cabrera, Miguel A.; 1Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Göttingen, Georg-August University, Göttingen, Germany"],["dc.contributor.affiliation","Dosen, Strahinja; 2Faculty of Medicine, Department of Health Science and Technology Center for Sensory-Motor Interaction, Aalborg University, Aalborg, Denmark"],["dc.contributor.affiliation","Schilling, Arndt F.; 1Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Göttingen, Georg-August University, Göttingen, Germany"],["dc.contributor.affiliation","Markovic, Marko; 1Applied Rehabilitation Technology Lab, Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Göttingen, Georg-August University, Göttingen, Germany"],["dc.contributor.author","Mouchoux, Jérémy"],["dc.contributor.author","Bravo-Cabrera, Miguel A."],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Schilling, Arndt F."],["dc.contributor.author","Markovic, Marko"],["dc.date.accessioned","2022-02-01T10:31:40Z"],["dc.date.available","2022-02-01T10:31:40Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:20:13Z"],["dc.description.abstract","Semi-autonomous (SA) control of upper-limb prostheses can improve the performance and decrease the cognitive burden of a user. In this approach, a prosthesis is equipped with additional sensors (e.g., computer vision) that provide contextual information and enable the system to accomplish some tasks automatically. Autonomous control is fused with a volitional input of a user to compute the commands that are sent to the prosthesis. Although several promising prototypes demonstrating the potential of this approach have been presented, methods to integrate the two control streams (i.e., autonomous and volitional) have not been systematically investigated. In the present study, we implemented three shared control modalities (i.e., sequential, simultaneous , and continuous ) and compared their performance, as well as the cognitive and physical burdens imposed on the user. In the sequential approach, the volitional input disabled the autonomous control. In the simultaneous approach, the volitional input to a specific degree of freedom (DoF) activated autonomous control of other DoFs, whereas in the continuous approach, autonomous control was always active except for the DoFs controlled by the user. The experiment was conducted in ten able-bodied subjects, and these subjects used an SA prosthesis to perform reach-and-grasp tasks while reacting to audio cues (dual tasking). The results demonstrated that, compared to the manual baseline (volitional control only), all three SA modalities accomplished the task in a shorter time and resulted in less volitional control input. The simultaneous SA modality performed worse than the sequential and continuous SA approaches. When systematic errors were introduced in the autonomous controller to generate a mismatch between the goals of the user and controller, the performance of SA modalities substantially decreased, even below the manual baseline. The sequential SA scheme was the least impacted one in terms of errors. The present study demonstrates that a specific approach for integrating volitional and autonomous control is indeed an important factor that significantly affects the performance and physical and cognitive load, and therefore these should be considered when designing SA prostheses."],["dc.description.abstract","Semi-autonomous (SA) control of upper-limb prostheses can improve the performance and decrease the cognitive burden of a user. In this approach, a prosthesis is equipped with additional sensors (e.g., computer vision) that provide contextual information and enable the system to accomplish some tasks automatically. Autonomous control is fused with a volitional input of a user to compute the commands that are sent to the prosthesis. Although several promising prototypes demonstrating the potential of this approach have been presented, methods to integrate the two control streams (i.e., autonomous and volitional) have not been systematically investigated. In the present study, we implemented three shared control modalities (i.e., sequential, simultaneous , and continuous ) and compared their performance, as well as the cognitive and physical burdens imposed on the user. In the sequential approach, the volitional input disabled the autonomous control. In the simultaneous approach, the volitional input to a specific degree of freedom (DoF) activated autonomous control of other DoFs, whereas in the continuous approach, autonomous control was always active except for the DoFs controlled by the user. The experiment was conducted in ten able-bodied subjects, and these subjects used an SA prosthesis to perform reach-and-grasp tasks while reacting to audio cues (dual tasking). The results demonstrated that, compared to the manual baseline (volitional control only), all three SA modalities accomplished the task in a shorter time and resulted in less volitional control input. The simultaneous SA modality performed worse than the sequential and continuous SA approaches. When systematic errors were introduced in the autonomous controller to generate a mismatch between the goals of the user and controller, the performance of SA modalities substantially decreased, even below the manual baseline. The sequential SA scheme was the least impacted one in terms of errors. The present study demonstrates that a specific approach for integrating volitional and autonomous control is indeed an important factor that significantly affects the performance and physical and cognitive load, and therefore these should be considered when designing SA prostheses."],["dc.identifier.doi","10.3389/fnbot.2021.768619"],["dc.identifier.eissn","1662-5218"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/98918"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-517"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5218"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Impact of Shared Control Modalities on Performance and Usability of Semi-autonomous Prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","2547"],["dc.bibliographiccitation.issue","8"],["dc.bibliographiccitation.journal","Experimental Brain Research"],["dc.bibliographiccitation.lastpage","2559"],["dc.bibliographiccitation.volume","235"],["dc.contributor.author","De Nunzio, Alessandro Marco"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Lemling, Sabrina"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Schweisfurth, Meike Annika"],["dc.contributor.author","Ge, Nan"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Falla, Deborah"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2019-07-09T11:44:27Z"],["dc.date.available","2019-07-09T11:44:27Z"],["dc.date.issued","2017"],["dc.description.abstract","Grasping is a complex task routinely performed in an anticipatory (feedforward) manner, where sensory feedback is responsible for learning and updating the internal model of grasp dynamics. This study aims at evaluating whether providing a proportional tactile force feedback during the myoelectric control of a prosthesis facilitates learning a stable internal model of the prosthesis force control. Ten able-bodied subjects controlled a sensorized myoelectric prosthesis performing four blocks of consecutive grasps at three levels of target force (30, 50, and 70%), repeatedly closing the fully opened hand. In the first and third block, the subjects received tactile and visual feedback, respectively, while during the second and fourth block, the feedback was removed. The subjects also performed an additional block with no feedback 1 day after the training (Retest). The median and interquartile range of the generated forces was computed to assess the accuracy and precision of force control. The results demonstrated that the feedback was indeed an effective instrument for the training of prosthesis control. After the training, the subjects were still able to accurately generate the desired force for the low and medium target (30 and 50% of maximum force available in a prosthesis), despite the feedback being removed within the session and during the retest (low target force). However, the training was substantially less successful for high forces (70% of prosthesis maximum force), where subjects exhibited a substantial loss of accuracy as soon as the feedback was removed. The precision of control decreased with higher forces and it was consistent across conditions, determined by an intrinsic variability of repeated myoelectric grasping. This study demonstrated that the subject could rely on the tactile feedback to adjust the motor command to the prosthesis across trials. The subjects adjusted the mean level of muscle activation (accuracy), whereas the precision could not be modulated as it depends on the intrinsic myoelectric variability. They were also able to maintain the feedforward command even after the feedback was removed, demonstrating thereby a stable learning, but the retention depended on the level of the target force. This is an important insight into the role of feedback as an instrument for learning of anticipatory prosthesis force control."],["dc.identifier.doi","10.1007/s00221-017-4991-7"],["dc.identifier.pmid","28550423"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14765"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59015"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","120357"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.contributor.author","Jorgovanovic, Nikola"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Djozic, Damir J."],["dc.contributor.author","Krajoski, Goran"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:46:47Z"],["dc.date.available","2018-11-07T09:46:47Z"],["dc.date.issued","2014"],["dc.description.abstract","Closing the control loop by providing somatosensory feedback to the user of a prosthesis is a well-known, long standing challenge in the field of prosthetics. Various approaches have been investigated for feedback restoration, ranging from direct neural stimulation to noninvasive sensory substitution methods. Although there are many studies presenting closed-loop systems, only a few of them objectively evaluated the closed-loop performance, mostly using vibrotactile stimulation. Importantly, the conclusions about the utility of the feedback were partly contradictory. The goal of the current study was to systematically investigate the capability of human subjects to control grasping force in closed loop using electrotactile feedback. We have developed a realistic experimental setup for virtual grasping, which operated in real time, included a set of real life objects, as well as a graphical and dynamical model of the prosthesis. We have used the setup to test 10 healthy, able bodied subjects to investigate the role of training, feedback and feedforward control, robustness of the closed loop, and the ability of the human subjects to generalize the control to previously \"unseen\" objects. Overall, the outcomes of this study are very optimistic with regard to the benefits of feedback and reveal various, practically relevant, aspects of closed-loop control."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2014"],["dc.identifier.doi","10.1155/2014/120357"],["dc.identifier.isi","000330402200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9756"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34964"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Hindawi Publishing Corporation"],["dc.relation.issn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Virtual Grasping: Closed-Loop Force Control Using Electrotactile Feedback"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Wilke, Meike Annika"],["dc.contributor.author","Niethammer, Christian"],["dc.contributor.author","Meyer, Britta"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Dosen, Strahinja"],["dc.date.accessioned","2020-12-10T18:39:01Z"],["dc.date.available","2020-12-10T18:39:01Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1186/s12984-019-0622-9"],["dc.identifier.eissn","1743-0003"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17235"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77512"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Frontiers in Neurorobotics"],["dc.bibliographiccitation.lastpage","17"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Castellini, Claudio"],["dc.contributor.author","Artemiadis, Panagiotis"],["dc.contributor.author","Wininger, Michael"],["dc.contributor.author","Ajoudani, Arash"],["dc.contributor.author","Alimusaj, Merkur"],["dc.contributor.author","Bicchi, Antonio"],["dc.contributor.author","Caputo, Barbara"],["dc.contributor.author","Craelius, William"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Englehart, Kevin B."],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Gijsberts, Arjan"],["dc.contributor.author","Godfrey, Sasha B."],["dc.contributor.author","Hargrove, Levi"],["dc.contributor.author","Ison, Mark"],["dc.contributor.author","Kuiken, Todd"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Pilarski, Patrick M."],["dc.contributor.author","Rupp, Ruediger"],["dc.contributor.author","Scheme, Erik"],["dc.date.accessioned","2018-11-07T09:36:33Z"],["dc.date.available","2018-11-07T09:36:33Z"],["dc.date.issued","2014"],["dc.description.abstract","One of the hottest topics in rehabilitation robotics is that of proper control of prosthetic devices. Despite decades of research, the state of the art is dramatically behind the expectations. To shed light on this issue, in June, 2013 the first international workshop on Present and future of non-invasive peripheral nervous system (PNS)-Machine Interfaces (MI; PMI) was convened, hosted by the International Conference on Rehabilitation Robotics. The keyword PMI has been selected to denote human-machine interfaces targeted at the limb-deficient, mainly upper-limb amputees, dealing with signals gathered from the PNS in a non-invasive way, that is, from the surface of the residuum. The workshop was intended to provide an overview of the state of the art and future perspectives of such interfaces; this paper represents is a collection of opinions expressed by each and every researcher/group involved in it."],["dc.identifier.doi","10.3389/fnbot.2014.00022"],["dc.identifier.isi","000348815300001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12193"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32642"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5218"],["dc.relation.issn","1662-5218"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Proceedings of the first workshop on Peripheral Machine Interfaces: going beyond traditional surface electromyography"],["dc.type","review"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","114"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Gonzalez-Vargas, Jose"],["dc.contributor.author","Sartori, Massimo"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Torricelli, Diego"],["dc.contributor.author","Pons, Jose L."],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:51:34Z"],["dc.date.available","2018-11-07T09:51:34Z"],["dc.date.issued","2015"],["dc.description.abstract","Humans can efficiently walk across a large variety of terrains and locomotion conditions with little or no mental effort. It has been hypothesized that the nervous system simplifies neuromuscular control by using muscle synergies, thus organizing multi-muscle activity into a small number of coordinative co-activation modules. In the present study we investigated how muscle modularity is structured across a large repertoire of locomotion conditions including five different speeds and five different ground elevations. For this we have used the non-negative matrix factorization technique in order to explain EMG experimental data with a low-dimensional set of four motor components. In this context each motor components is composed of a non-negative factor and the associated muscle weightings. Furthermore, we have investigated if the proposed descriptive analysis of muscle modularity could be translated into a predictive model that could: (1) Estimate how motor components modulate across locomotion speeds and ground elevations. This implies not only estimating the non-negative factors temporal characteristics, but also the associated muscle weighting variations. (2) Estimate how the resulting muscle excitations modulate across novel locomotion conditions and subjects. The results showed three major distinctive features of muscle modularity: (1) the number of motor components was preserved across all locomotion conditions, (2) the non negative factors were consistent in shape and timing across all locomotion conditions, and (3) the muscle weightings were modulated as distinctive functions of locomotion speed and ground elevation. Results also showed that the developed predictive model was able to reproduce well the muscle modularity of un-modeled data, i.e., novel subjects and conditions. Muscle weightings were reconstructed with a cross-correlation factor greater than 70% and a root mean square error less than 0.10. Furthermore, the generated muscle excitations matched well the experimental excitation with a cross correlation factor greater than 85% and a root mean square error less than 0.09. The ability of synthetizing the neuromuscular mechanisms underlying human locomotion across a variety of locomotion conditions will enable solutions in the field of neurorehabilitation technologies and control of bipedal artificial systems. Open access of the model implementation is provided for further analysis at https://simtk.org/home/p-mep/."],["dc.description.sponsorship","Open-Access Publikationsfonds 2015"],["dc.identifier.doi","10.3389/fncom.2015.00114"],["dc.identifier.isi","000361631700001"],["dc.identifier.pmid","26441624"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12160"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35943"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Media Sa"],["dc.relation.issn","1662-5188"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","A predictive model of muscle excitations based on muscle modularity for a large repertoire of human locomotion conditions"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","35"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Paredes, Liliana P."],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Rattay, Frank"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:58:35Z"],["dc.date.available","2018-11-07T09:58:35Z"],["dc.date.issued","2015"],["dc.description.abstract","Background: Electrocutaneous stimulation can restore the missing sensory information to prosthetic users. In electrotactile feedback, the information about the prosthesis state is transmitted in the form of pulse trains. The stimulation frequency is an important parameter since it influences the data transmission rate over the feedback channel as well as the form of the elicited tactile sensations. Methods: We evaluated the influence of the stimulation frequency on the subject's ability to utilize the feedback information during electrotactile closed-loop control. Ten healthy subjects performed a real-time compensatory tracking (standard test bench) of sinusoids and pseudorandom signals using either visual feedback (benchmark) or electrocutaneous feedback in seven conditions characterized by different combinations of the stimulation frequency (F-STIM) and tracking error sampling rate (F-TE). The tracking error was transmitted using two concentric electrodes placed on the forearm. The quality of tracking was assessed using the Squared Pearson Correlation Coefficient (SPCC), the Normalized Root Mean Square Tracking Error (NRMSTE) and the time delay between the reference and generated trajectories (TDIO). Results: The results demonstrated that F-STIM was more important for the control performance than F-TE. The quality of tracking deteriorated with a decrease in the stimulation frequency, SPCC and NRMSTE (mean) were 87.5% and 9.4% in the condition 100/100 (F-TE/F-STIM), respectively, and deteriorated to 61.1% and 15.3% in 5/5, respectively, while the TDIO increased from 359.8 ms in 100/100 to 1009 ms in 5/5. However, the performance recovered when the tracking error sampled at a low rate was delivered using a high stimulation frequency (SPCC = 83.6%, NRMSTE = 10.3%, TDIO = 415.6 ms, in 5/100). Conclusions: The likely reason for the performance decrease and recovery was that the stimulation frequency critically influenced the tactile perception quality and thereby the effective rate of information transfer through the feedback channel. The outcome of this study can facilitate the selection of optimal system parameters for somatosensory feedback in upper limb prostheses. The results imply that the feedback variables (e.g., grasping force) should be transmitted at relatively high frequencies of stimulation (> 25 Hz), but that they can be sampled at much lower rates (e.g., 5 Hz)."],["dc.identifier.doi","10.1186/s12984-015-0022-8"],["dc.identifier.isi","000353197700001"],["dc.identifier.pmid","25889752"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12508"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37393"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","The impact of the stimulation frequency on closed-loop control with electrotactile feedback"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","73"],["dc.bibliographiccitation.journal","Journal of NeuroEngineering and Rehabilitation"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Celadon, Nicolo"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Binder, Iris"],["dc.contributor.author","Ariano, Paolo"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:10:19Z"],["dc.date.available","2018-11-07T10:10:19Z"],["dc.date.issued","2016"],["dc.description.abstract","Background: The importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. Surface electromyography can be used to create a user-driven control of a rehabilitation robot, in which the subject needs to engage actively, by using spared voluntary activation to trigger the assistance of the robot. Methods: The study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (HD-sEMG) with minimal interference between movements of other fingers. Regression was evaluated in online and offline control tests with nine healthy subjects (per test) using a linear discriminant analysis classifier (LDA), a common spatial patterns proportional estimator (CSP-PE), and a thresholding (THR) algorithm. In all tests, the subjects performed an isometric force tracking task guided by a moving visual marker indicating the contraction type (flexion/extension), desired activation level and the finger that should be moved. The outcome measures were mean square error (nMSE) between the reference and generated trajectories normalized to the peak-to-peak value of the reference, the classification accuracy (CA), the mean amplitude of the false activations (MAFA) and, in the offline tests only, the Pearson correlation coefficient (PCORR). Results: The offline tests demonstrated that, for the reduced number of electrodes (<= 24), the CSP-PE outperformed the LDA with higher precision of proportional estimation and less crosstalk between the movement classes (e.g., 8 electrodes, median MAFA similar to 0.6 vs. 1.1 %, median nMSE similar to 4.3 vs. 5.5 %). The LDA and the CSP-PE performed similarly in the online tests (median nMSE < 3.6 %, median MAFA < 0.7 %), but the CSP-PE provided a more stable performance across the tested conditions (less improvement between different sessions). Furthermore, THR, exploiting topographical information about the single finger activity from HD-sEMG, provided in many cases a regression accuracy similar to that of the pattern recognition techniques, but the performance was not consistent across subjects and fingers. Conclusions: The CSP-PE is a method of choice for selective individual finger control with the limited number of electrodes (< 24), whereas for the higher resolution of the recording, either method (CPS-PA or LDA) can be used with a similar performance. Despite the abundance of detection points, the simple THR showed to be significantly worse compared to both pattern recognition/regression methods. Nevertheless, THR is a simple method to apply (no training), and it could still give satisfactory performance in some subjects and/or simpler scenarios (e.g., control of selected fingers). These conclusions are important for guiding future developments towards the clinical application of the methods for individual finger control in rehabilitation robotics."],["dc.identifier.doi","10.1186/s12984-016-0172-3"],["dc.identifier.isi","000381337700001"],["dc.identifier.pmid","27488270"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13858"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39829"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-0003"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Proportional estimation of finger movements from high-density surface electromyography"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","707208"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.contributor.author","Cikajlo, Imre"],["dc.contributor.author","Watanabe, Takashi"],["dc.contributor.author","Dosen, Strahinja"],["dc.date.accessioned","2018-11-07T09:45:57Z"],["dc.date.available","2018-11-07T09:45:57Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.1155/2014/707208"],["dc.identifier.isi","000339812300001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/11918"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34756"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Hindawi Publishing Corporation"],["dc.relation.issn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Computational and Control Methods in Rehabilitation Medicine"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.subtype","letter_note"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]