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.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","066022"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Popovic, Dejan"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T09:47:53Z"],["dc.date.available","2018-11-07T09:47:53Z"],["dc.date.issued","2015"],["dc.description.abstract","Objective. Myoelectric activity volitionally generated by the user is often used for controlling hand prostheses in order to replicate the synergistic actions of muscles in healthy humans during grasping. Muscle synergies in healthy humans are based on the integration of visual perception, heuristics and proprioception. Here, we demonstrate how sensor fusion that combines artificial vision and proprioceptive information with the high-level processing characteristics of biological systems can be effectively used in transradial prosthesis control. Approach. We developed a novel context-and user-aware prosthesis (CASP) controller integrating computer vision and inertial sensing with myoelectric activity in order to achieve semi-autonomous and reactive control of a prosthetic hand. The presented method semiautomatically provides simultaneous and proportional control of multiple degrees-of-freedom (DOFs), thus decreasing overall physical effort while retaining full user control. The system was compared against the major commercial state-of-the art myoelectric control system in ten able-bodied and one amputee subject. All subjects used transradial prosthesis with an active wrist to grasp objects typically associated with activities of daily living. Main results. The CASP significantly outperformed the myoelectric interface when controlling all of the prosthesis DOF. However, when tested with less complex prosthetic system (smaller number of DOF), the CASP was slower but resulted with reaching motions that contained less compensatory movements. Another important finding is that the CASP system required minimal user adaptation and training. Significance. The CASP constitutes a substantial improvement for the control of multi-DOF prostheses. The application of the CASP will have a significant impact when translated to real-life scenarious, particularly with respect to improving the usability and acceptance of highly complex systems (e.g., full prosthetic arms) by amputees."],["dc.identifier.doi","10.1088/1741-2560/12/6/066022"],["dc.identifier.isi","000374884100022"],["dc.identifier.pmid","26529274"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35190"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Iop Publishing Ltd"],["dc.relation.issn","1741-2552"],["dc.relation.issn","1741-2560"],["dc.title","Sensor fusion and computer vision for context-aware control of a multi degree-of-freedom prosthesis"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","036007"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Karnal, Hemanth"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.contributor.author","Dosen, Strahinja"],["dc.date.accessioned","2018-11-07T10:23:37Z"],["dc.date.available","2018-11-07T10:23:37Z"],["dc.date.issued","2017"],["dc.description.abstract","Objective. Providing sensory feedback to the user of the prosthesis is an important challenge. The common approach is to use tactile stimulation, which is easy to implement but requires training and has limited information bandwidth. In this study, we propose an alternative approach based on augmented reality. Approach. We have developed the GLIMPSE, a Google Glass application which connects to the prosthesis via a Bluetooth interface and renders the prosthesis states (EMG signals, aperture, force and contact) using augmented reality (seethrough display) and sound (bone conduction transducer). The interface was tested in healthy subjects that used the prosthesis with (FB group) and without (NFB group) feedback during a modified clothespins test that allowed us to vary the difficulty of the task. The outcome measures were the number of unsuccessful trials, the time to accomplish the task, and the subjective ratings of the relevance of the feedback. Main results. There was no difference in performance between FB and NFB groups in the case of a simple task (basic, same-color clothespins test), but the feedback significantly improved the performance in a more complex task (pins of different resistances). Importantly, the GLIMPSE feedback did not increase the time to accomplish the task. Therefore, the supplemental feedback might be useful in the tasks which are more demanding, and thereby less likely to benefit from learning and feedforward control. The subjects integrated the supplemental feedback with the intrinsic sources (vision and muscle proprioception), developing their own idiosyncratic strategies to accomplish the task. Significance. The present study demonstrates a novel self-contained, ready-to-deploy, wearable feedback interface. The interface was successfully tested and was proven to be feasible and functionally beneficial. The GLIMPSE can be used as a practical solution but also as a general and flexible instrument to investigate closed-loop prosthesis control."],["dc.identifier.doi","10.1088/1741-2552/aa620a"],["dc.identifier.isi","000398154200001"],["dc.identifier.pmid","28355147"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/42496"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","PUB_WoS_Import"],["dc.publisher","Iop Publishing Ltd"],["dc.relation.issn","1741-2552"],["dc.relation.issn","1741-2560"],["dc.title","GLIMPSE: Google Glass interface for sensory feedback in myoelectric hand prostheses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","056010"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Journal of Neural Engineering"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Schweisfurth, Meike A."],["dc.contributor.author","Markovic, Marko"],["dc.contributor.author","Dosen, Strahinja"],["dc.contributor.author","Teich, Florian"],["dc.contributor.author","Graimann, Bernhard"],["dc.contributor.author","Farina, Dario"],["dc.date.accessioned","2018-11-07T10:08:08Z"],["dc.date.available","2018-11-07T10:08:08Z"],["dc.date.issued","2016"],["dc.description.abstract","Objective. A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate. Approach. In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively. Main results. Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements. Significance. Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for predictive control, as the subjects used the feedback to adjust the desired force even before the prosthesis contacted the object. In conclusion, the online emgFB was superior to the classic forceFB in realistic conditions that included electrotactile stimulation, limited feedback resolution (8 levels), cognitive processing delay, and time constraints (fast grasping)."],["dc.description.sponsorship","European Commission under the MYOSENS [FP7-PEOPLE-2011-IAPP-286208]"],["dc.identifier.doi","10.1088/1741-2560/13/5/056010"],["dc.identifier.isi","000384023800003"],["dc.identifier.pmid","27547992"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39414"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Iop Publishing Ltd"],["dc.relation.issn","1741-2552"],["dc.relation.issn","1741-2560"],["dc.title","Electrotactile EMG feedback improves the control of prosthesis grasping force"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]