Upper Limb Amputees
Conditions
Keywords
Upper limb amputation, HD-sEMG, Pattern recognition, Sensory feedback, TENS
Brief summary
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.
Interventions
OTHERElectromyography recording with HD-sEMG
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.
OTHERTranscutaneous Electrical Nerve Stimulation (TENS)
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.
Sponsors
Istituto Nazionale Assicurazione contro gli Infortuni sul Lavoro
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
BASIC_SCIENCE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
* Upper limb amputation; * Stable clinical condition; * Skin integrity of the stump; * Age between 18 and 65 years; * High level of motivation to participate in the study and acceptance of the purpose of the study; * Signed informed consent document.
Exclusion criteria
* Clinical instability; * Dehiscence of the amputation wound; * Failure to complete the informed consent; * State of pregnancy; * Implanted devices that can interfere with TENS stimulation (e.g. pacemakers);
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Improve gestures decoding by means of HD-sEMG decoding algorithms | through study completion, an average of 2 year through study completion, an average of 2 year | 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. |
| Elicit somatic sensations in amputees | through study completion, an average of 2 year through study completion, an average of 2 year | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Increase the number of hand grasps to be classfiied | through study completion, an average of 2 year through study completion, an average of 2 year | 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. |
| Development of encoding strategies | through study completion, an average of 2 year through study completion, an average of 2 year | 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. |
Countries
Italy
Contacts
Primary ContactEmanuele Gruppioni, PhD
Outcome results
None listed