Upper limb amputation still causes severe disability today; prostheses currently on the market are able to restore partially to the amputee the lost functionality. In addition to the motor capacity of the limb, prosthetic systems should also aim to restore to the sensory information from the surrounding environment during contact with objects. Therefore, it is important to develop bidirectional prostheses. It is thus apparent that the development of new techniques for decoding the efferent channel, such as high-density surface electromyography, and for encoding of the afferent channel afferent, to return multimodal somatosensory sensations of mechanoception, nociception, and thermoception using TENS, isimportant to improve the patient's use of the prosthesis.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
BASIC_SCIENCE
Masking
NONE
Enrollment
30
Measurement of muscle electrical signal with HD-sEMG sensors, training of a pattern recognition classifier for hand gesture recognition, verification and comparison with state of the art.
Application of TENS by means of non-invasive superifical electrodes on the stump skin of the participants to restore multimodal somatotopical sensations of mechanoception, nociception and thermoception.
Centro Protesi Inail
Budrio, BO, Italy
RECRUITINGImprove gestures decoding by means of HD-sEMG decoding algorithms
The performance of HD-sEMG classifiers with variable number of classes will be evaluated in the offline phase in terms of accuracy of classification and F1-Score.
Time frame: through study completion, an average of 2 year through study completion, an average of 2 year
Elicit somatic sensations in amputees
The performance of the stimulation strategy will be evaluated in terms of stimulus discrimination accuracy, a parameter that identifies the number of times the subject correctly reports the type of sensation elicited by the experimenter compared to the total number of stimulations performed.
Time frame: through study completion, an average of 2 year through study completion, an average of 2 year
Increase the number of hand grasps to be classfiied
To explore the possibility of increasing the number of gestures that can be classified by the developed classification system, compared to the number of gestures that can be reproduced by prosthetic control solutions traditional and to evaluate the intuitiveness in using classifiers to control a polyarticulated prosthesis.
Time frame: through study completion, an average of 2 year through study completion, an average of 2 year
Development of encoding strategies
Develop new algorithms for decoding motor intention from the myoelectric signal and new encoding algorithms for the sensory restitution by non-invasive stimulation and to evaluate the intuitiveness of the developed strategies.
Time frame: through study completion, an average of 2 year through study completion, an average of 2 year
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