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Sensory Feedback using Electrical Stimulation of the Tactile Sense
Published in Raymond V. Smith, John H. Leslie, Rehabilitation Engineering, 2018
Andrew Y.J. Szeto, Ronald R. Riso
Evaluations of prostheses with artificial sensory feedback have generally been positive when based on improved performance on a specific task.116 However, these evaluations have often been contaminated by annoying mechanical failures of the arm and other extraneous factors such as inadequate prosthetic training, improper fit, and/or short-term usage of the arm. As a result, the benefits of incorporating feedback into externally powered arm prostheses remain unclear.2,108 In a recent report,119 Scott et al. described their experiences with a feedback system which used electrocutaneous stimulation to indicate prehension force in a prosthetic hand. Six of the ten amputees fitted with the system participated in this clinical evaluation. The number of favorable comments regarding supplemental sensory feedback were counterbalanced by the low rate of long-term usage and system reliability. Congenital amputees who received early fitting of a myoelectric hand appeared able to control their prostheses far better than one would expect based on visual feedback alone. In short, the amputees liked sensory feedback in their prosthetic hand although most of them did not use it nor depend on it for increased controllability. Mann’s 1980 comment2 that “… despite the early and persistent awareness of the importance of sensory feedback … this remains the most refractory of problems …” remains most apropos.
Bidirectional Neural Interfaces
Published in Chang S. Nam, Anton Nijholt, Fabien Lotte, Brain–Computer Interfaces Handbook, 2018
Mikhail A. Lebedev, Alexei Ossadtchi
Figure 37.1 illustrates a bidirectional NI implemented using electrocorticographic (ECoG) recordings. The ECoG grid is placed over both the motor and somatosensory cortical areas. The grid portion overlying the motor area is used to extract motor commands that are sent to a prosthetic hand. The prosthetic hand is equipped with touch and position sensors whose signals are sent to the somatosensory portion of the grid. Electrical stimulation of the somatosensory cortex through the ECoG electrodes generates the sensory feedback needed to improve the control of the prosthetic hand.
Upper and Lower Limb Robotic Prostheses
Published in Pedro Encarnação, Albert M. Cook, Robotic Assistive Technologies, 2017
Patrick M. Pilarski, Jacqueline S. Hebert
Actuators provide the forces needed to move the different parts of the robotic prosthesis (Figures 4.3a, c). They must be robust, generate forces that allow the user to be able to perform daily life tasks, and be efficient in their use of power. Depending on the joint that is being powered by an actuator, the size and type of motor vary. Actuation of individual fingers in a prosthetic hand is readily done by a series of small actuators at the base of a finger or in the palm of the hand (Figures 4.1 and 4.3c). Larger actuators are used in joints such as the knee, elbow, or wrist (Figure 4.3a). For the most part, direct current (DC) motors remain the standard source of actuation, as well reviewed with technical clarity by Weir (2004). Specifically with regard to prosthetic hands, models are now available that allow more than two dozen different grasp patterns through actuators for each finger, with either a manually movable thumb or powered thumb opposition (Belter et al. 2013).
A novel sEMG data augmentation based on WGAN-GP
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2023
Fabrício Coelho, Milena F. Pinto, Aurélio G. Melo, Gabryel S. Ramos, André L. M. Marcato
The recognition of hand movements through sEMG signals is the primary key to most prosthetic hand control. Thus, sEMG signals are used to hand gesture recognition to actuate with upper limps prostheses in several works (Krasoulis et al. 2020; Calado et al. 2019; Shahzad et al. 2019). An important concept to get a successful classification is the availability of a database with a suitable generalization of all movements to be classified. Building a database capable of generating many movements can become a difficult task because the conception of these movements by the subjects can be exhausting due to the many repetitions of the actions. This fatigue contributes to the database’s contamination, generating data that is not experienced in reality (Anicet Zanini and Luna Colombini 2020).
The perspective of rehabilitation health care professionals regarding the clinical utility of a body-environment proximity measurement device
Published in Cogent Medicine, 2019
Céline Faure, Elizabeth L. Inness, Marie-Eve Lamontagne, Geneviève Sirois, Geoffrey Edwards, Bradford J. McFadyen, Karl Zabjek
RHCPs talked about feedback to monitor different distances between body parts or with the environment. The distance between lower limbs was noted to improve the locomotor pattern of patients with a prosthesis or brain injury. As explained by one rehabilitation professional, “…you see a video of the limb with a picture which shows you that the limb isn’t in a good position…it’s visual…I find that says something clinically…”. Another distance of interest was between the upper limbs and an object which could help patients with a prosthesis to manipulate objects. As one rehabilitation professional explained, “my patient always wonders where his prosthetic hand is”. In addition, distances between the body centre or trunk and some environmental reference point to guide bending over and avoid overreaching or improve posture during the transfer from a seat or wheelchair was also noted. The distance between the trunk and an object, or between the trunk and the body to improve posture during manual handling was raised. Finally, the distance between lower limbs and stairs was noted as providing a possible way to reassure elderly people and reduce tripping and falls risks.
Examining the Spatiotemporal Disruption to Gaze When Using a Myoelectric Prosthetic Hand
Published in Journal of Motor Behavior, 2018
J. V. V. Parr, S. J. Vine, N. R. Harrison, G. Wood
Despite these interesting findings, several limitations of the study should be addressed. First, although we have highlighted significant spatial and temporal disruptions to gaze for anatomically intact users of a prosthesis simulator, it is still unclear if these findings are representative of early prosthesis use in upper-limb amputees. Interestingly, Sobuh et al. (2014) found similarities between the gaze behaviors exhibited by intact users of a simulator and amputee subjects—although the task used had relatively few movement phases and no examination of the temporal disruption to gaze was reported. Therefore, future researchers should examine if these findings transfer to clinical populations. Second, the present study was also potentially limited by the fixed rather counterbalanced order of hand conditions. However, such is the difference in control mechanisms when using the prosthetic hand (compared with the anatomic), that any gains from practicing the task with the anatomic hand would have been irrelevant in facilitating prosthetic hand control. Finally, although our gaze shifting measure provided some temporal detail regarding the allocation of gaze during the early part of each task phase, more fine-grained analyses could be explored in future research by quantifying the number of look-ahead and look-back fixations within task phases (Chadwell et al., 2016). Despite this, our relatively simple measure of the temporal allocation of gaze was sensitive enough to be a significant predictor of task performance.