Ignite Creation Date:
2025-12-25 @ 2:36 AM
Ignite Modification Date:
2025-12-30 @ 3:14 PM
Study NCT ID:
NCT04043234
Status:
COMPLETED
Last Update Posted:
2024-04-24
First Post:
2019-07-31
Is Gene Therapy:
True
Has Adverse Events:
True
Brief Title:
RESCU System for Robust Upper Limb Prosthesis Control
Sponsor:
Organization:
Raw JSON
{'hasResults': True, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24', 'submissionTracking': {'firstMcpInfo': {'postDateStruct': {'date': '2024-02-01', 'type': 'ACTUAL'}}}}}, 'resultsSection': {'moreInfoModule': {'pointOfContact': {'email': 'rahul@i-biomed.com', 'phone': '(443) 451-7177', 'title': 'Chief Executive Officer', 'organization': 'Infinite Biomedical Technologies, LLC'}, 'certainAgreement': {'piSponsorEmployee': False, 'restrictiveAgreement': False}}, 'adverseEventsModule': {'timeFrame': '35 days', 'eventGroups': [{'id': 'EG000', 'title': 'Experimental', 'description': 'The Experimental device includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nRESCU: Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.', 'otherNumAtRisk': 4, 'deathsNumAtRisk': 4, 'otherNumAffected': 0, 'seriousNumAtRisk': 4, 'deathsNumAffected': 0, 'seriousNumAffected': 0}, {'id': 'EG001', 'title': 'Control', 'description': 'The Control device includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nPattern Recognition: Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.', 'otherNumAtRisk': 4, 'deathsNumAtRisk': 4, 'otherNumAffected': 0, 'seriousNumAtRisk': 4, 'deathsNumAffected': 0, 'seriousNumAffected': 0}], 'frequencyThreshold': '0'}, 'outcomeMeasuresModule': {'outcomeMeasures': [{'type': 'PRIMARY', 'title': 'Mean Daily Prosthesis Use Duration', 'denoms': [{'units': 'Participants', 'counts': [{'value': '2', 'groupId': 'OG000'}, {'value': '2', 'groupId': 'OG001'}]}], 'groups': [{'id': 'OG000', 'title': 'Experimental', 'description': 'The Experimental device includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nRESCU: Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.'}, {'id': 'OG001', 'title': 'Control', 'description': 'The Control device includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nPattern Recognition: Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.'}], 'classes': [{'categories': [{'measurements': [{'value': '3.81', 'spread': '1.77', 'groupId': 'OG000'}, {'value': '6.83', 'spread': '2.55', 'groupId': 'OG001'}]}]}], 'paramType': 'MEAN', 'timeFrame': 'Baseline, 4 weeks', 'description': 'Prosthesis usage time was monitored as a proxy for user satisfaction, under the assumption that when an individual is more satisfied with their prosthetic solution, they will use it more in their daily lives. For the experimental intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily over the evaluation period (i.e., four weeks). For the control intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily before the evaluation period (i.e., at baseline).', 'unitOfMeasure': 'hours', 'dispersionType': 'Standard Deviation', 'reportingStatus': 'POSTED'}, {'type': 'SECONDARY', 'title': 'Activities Measure for Upper Limb Amputees (AM-ULA)', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'The AM-ULA is a clinician-graded measure of activity performance for adults with upper limb amputation that considers task completion, speed, movement quality, skillfulness of prosthetic use, and independence to quantify how functional an individual is while using their prosthesis. A higher score indicates overall greater prosthesis functionality.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': "Orthotics and Prosthetics User's Survey Upper Extremity Functional Status (OPUS UEFS)", 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The OPUS UEFS is a self-report questionnaire that asks respondents to score how easily they can complete several activities of daily living (e.g., drink from a paper cup, brush hair, etc.). A higher score indicates greater function.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Trinity Amputation and Prosthesis Experience Survey for Upper Limb Amputation (TAPES-ULA)', 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The Psychosocial Adjustment to Amputation measure (originally modified from the TAPES-ULA) is a self-report questionnaire that asks respondents to measure how well they have adapted to life with their amputation and prosthesis. This measure was administered specifically to quantify prosthesis use and return to work. The measure contains two subscales: a 7-item Adjustment to Limitation subscale and a 9-item Work and Independence subscale. For the Adjustment to Limitation subscale, a higher score indicates greater adjustment. For the Work and Independence subscale, a higher score indicates greater feelings of dependency.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'PROMIS Satisfaction Short Form 8a', 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The PROMIS Satisfaction Short Form 8a is a self-report questionnaire designed to query individuals on their satisfaction with their ability to participate in social roles and activities. This measure was chosen due to its ability to capture patient satisfaction with their ability to participate in activities of daily living throughout various roles in life. A higher score indicates higher satisfaction with their ability to participate in work and home life.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Pain Scale', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'Participants were queried about their experience of pain, both in their residual limb and in their phantom limb perception to detect if the choice of control strategy affects pain levels. From 0 to 10, a higher score indicates more feelings of pain.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Socket Comfort Score', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'Socket comfort score was collected to determine if outside factors (i.e., socket fit) are affecting function during the take-home period.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}, {'type': 'SECONDARY', 'title': 'Range of Motion (of Residual Joints)', 'timeFrame': 'Baseline, Post-Fitting', 'description': 'The range of motion of the residual joints is an important factor when judging how well a prosthesis fits a participant, with a higher joint range of motion indicating a better fitting socket. For the individuals in this study (i.e., those with below-elbow amputations), the elbow range-of-motion is expected to be most affected by a prosthesis socket with a poor fit.', 'reportingStatus': 'NOT_POSTED', 'denomUnitsSelected': 'Participants'}]}, 'participantFlowModule': {'groups': [{'id': 'FG000', 'title': 'Single-Case Experimental Design', 'description': 'Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nPattern Recognition: Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.\n\nRESCU: Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.'}], 'periods': [{'title': 'Overall Study', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '4'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '2'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '2'}]}], 'dropWithdraws': [{'type': 'Lost to Follow-up', 'reasons': [{'groupId': 'FG000', 'numSubjects': '1'}]}, {'type': 'Withdrawal by Subject', 'reasons': [{'groupId': 'FG000', 'numSubjects': '1'}]}]}], 'recruitmentDetails': "Participants were recruited and enrolled on the following dates: November 7, 2023, November 10, 2023, and November 15, 2023. All participants were recruited at their usual prosthetist's clinic."}, 'baselineCharacteristicsModule': {'denoms': [{'units': 'Participants', 'counts': [{'value': '4', 'groupId': 'BG000'}]}], 'groups': [{'id': 'BG000', 'title': 'Single-Case Experimental Design', 'description': 'Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).\n\nPattern Recognition: Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.\n\nRESCU: Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.'}], 'measures': [{'title': 'Age, Categorical', 'classes': [{'categories': [{'title': '<=18 years', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Between 18 and 65 years', 'measurements': [{'value': '3', 'groupId': 'BG000'}]}, {'title': '>=65 years', 'measurements': [{'value': '1', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Age, Continuous', 'classes': [{'categories': [{'measurements': [{'value': '55.75', 'spread': '9.34', 'groupId': 'BG000'}]}]}], 'paramType': 'MEAN', 'unitOfMeasure': 'years', 'dispersionType': 'STANDARD_DEVIATION'}, {'title': 'Sex: Female, Male', 'classes': [{'categories': [{'title': 'Female', 'measurements': [{'value': '2', 'groupId': 'BG000'}]}, {'title': 'Male', 'measurements': [{'value': '2', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Ethnicity (NIH/OMB)', 'classes': [{'categories': [{'title': 'Hispanic or Latino', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Not Hispanic or Latino', 'measurements': [{'value': '4', 'groupId': 'BG000'}]}, {'title': 'Unknown or Not Reported', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Race (NIH/OMB)', 'classes': [{'categories': [{'title': 'American Indian or Alaska Native', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Asian', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Native Hawaiian or Other Pacific Islander', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Black or African American', 'measurements': [{'value': '1', 'groupId': 'BG000'}]}, {'title': 'White', 'measurements': [{'value': '3', 'groupId': 'BG000'}]}, {'title': 'More than one race', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}, {'title': 'Unknown or Not Reported', 'measurements': [{'value': '0', 'groupId': 'BG000'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Region of Enrollment', 'classes': [{'title': 'United States', 'categories': [{'measurements': [{'value': '4', 'groupId': 'BG000'}]}]}], 'paramType': 'NUMBER', 'unitOfMeasure': 'participants'}]}}, 'documentSection': {'largeDocumentModule': {'largeDocs': [{'date': '2023-12-29', 'size': 326854, 'label': 'Study Protocol and Statistical Analysis Plan', 'hasIcf': False, 'hasSap': True, 'filename': 'Prot_SAP_000.pdf', 'typeAbbrev': 'Prot_SAP', 'uploadDate': '2023-12-29T16:41', 'hasProtocol': True}, {'date': '2023-10-12', 'size': 335693, 'label': 'Informed Consent Form', 'hasIcf': True, 'hasSap': False, 'filename': 'ICF_001.pdf', 'typeAbbrev': 'ICF', 'uploadDate': '2023-12-29T16:25', 'hasProtocol': False}]}}, 'protocolSection': {'designModule': {'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE', 'maskingDescription': 'The occupational therapist who graded the AM-ULA functional assessment was masked to participant IDs and intervention'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': 'Single-Case Experimental Design (SCED)'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 4}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2023-11-07', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-04', 'completionDateStruct': {'date': '2023-12-19', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-04-23', 'studyFirstSubmitDate': '2019-07-31', 'resultsFirstSubmitDate': '2024-01-08', 'studyFirstSubmitQcDate': '2019-07-31', 'lastUpdatePostDateStruct': {'date': '2024-04-24', 'type': 'ACTUAL'}, 'resultsFirstSubmitQcDate': '2024-04-23', 'studyFirstPostDateStruct': {'date': '2019-08-02', 'type': 'ACTUAL'}, 'resultsFirstPostDateStruct': {'date': '2024-04-24', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-12-19', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Mean Daily Prosthesis Use Duration', 'timeFrame': 'Baseline, 4 weeks', 'description': 'Prosthesis usage time was monitored as a proxy for user satisfaction, under the assumption that when an individual is more satisfied with their prosthetic solution, they will use it more in their daily lives. For the experimental intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily over the evaluation period (i.e., four weeks). For the control intervention, the mean daily prosthesis use duration is reported as the average number of hours the prosthesis was used daily before the evaluation period (i.e., at baseline).'}], 'secondaryOutcomes': [{'measure': 'Activities Measure for Upper Limb Amputees (AM-ULA)', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'The AM-ULA is a clinician-graded measure of activity performance for adults with upper limb amputation that considers task completion, speed, movement quality, skillfulness of prosthetic use, and independence to quantify how functional an individual is while using their prosthesis. A higher score indicates overall greater prosthesis functionality.'}, {'measure': "Orthotics and Prosthetics User's Survey Upper Extremity Functional Status (OPUS UEFS)", 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The OPUS UEFS is a self-report questionnaire that asks respondents to score how easily they can complete several activities of daily living (e.g., drink from a paper cup, brush hair, etc.). A higher score indicates greater function.'}, {'measure': 'Trinity Amputation and Prosthesis Experience Survey for Upper Limb Amputation (TAPES-ULA)', 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The Psychosocial Adjustment to Amputation measure (originally modified from the TAPES-ULA) is a self-report questionnaire that asks respondents to measure how well they have adapted to life with their amputation and prosthesis. This measure was administered specifically to quantify prosthesis use and return to work. The measure contains two subscales: a 7-item Adjustment to Limitation subscale and a 9-item Work and Independence subscale. For the Adjustment to Limitation subscale, a higher score indicates greater adjustment. For the Work and Independence subscale, a higher score indicates greater feelings of dependency.'}, {'measure': 'PROMIS Satisfaction Short Form 8a', 'timeFrame': 'Baseline, Post-Intervention', 'description': 'The PROMIS Satisfaction Short Form 8a is a self-report questionnaire designed to query individuals on their satisfaction with their ability to participate in social roles and activities. This measure was chosen due to its ability to capture patient satisfaction with their ability to participate in activities of daily living throughout various roles in life. A higher score indicates higher satisfaction with their ability to participate in work and home life.'}, {'measure': 'Pain Scale', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'Participants were queried about their experience of pain, both in their residual limb and in their phantom limb perception to detect if the choice of control strategy affects pain levels. From 0 to 10, a higher score indicates more feelings of pain.'}, {'measure': 'Socket Comfort Score', 'timeFrame': 'Baseline, Post-Fitting, Post-Intervention', 'description': 'Socket comfort score was collected to determine if outside factors (i.e., socket fit) are affecting function during the take-home period.'}, {'measure': 'Range of Motion (of Residual Joints)', 'timeFrame': 'Baseline, Post-Fitting', 'description': 'The range of motion of the residual joints is an important factor when judging how well a prosthesis fits a participant, with a higher joint range of motion indicating a better fitting socket. For the individuals in this study (i.e., those with below-elbow amputations), the elbow range-of-motion is expected to be most affected by a prosthesis socket with a poor fit.'}]}, 'oversightModule': {'isUnapprovedDevice': True}, 'conditionsModule': {'keywords': ['upper limb', 'amputation', 'prosthesis', 'myoelectric', 'pattern recognition'], 'conditions': ['Amputation', 'Upper Limb']}, 'descriptionModule': {'briefSummary': 'This study will compare the use of RESCU \\[Experimental\\] Prosthesis with a \\[Standard\\] pattern recognition prosthesis in a clinical setting and in unsupervised daily activity. The protocol will follow a single case experimental design (SCED) to compensate for the limited size of the patient population. Each of the participants will use the Standard and Experimental and systems over a 35-day period. The Standard system will include at least two controllable DoFs (hand, wrist, multi-articulated hand, etc) and a commercially-available pattern recognition controller. The RESCU system will use the same components as the Standard system but will differ with respect to incorporating eight IBT Element Electrodes (as required for pattern recognition control) and the RESCU control software. The hypothesis is that pattern recognition will outperform the commercially-available control strategy for most participants on in-clinic, at-home usage, and subjective measures.', 'detailedDescription': 'The AB sequence for the study protocol is described below, where control type A is the Standard prosthesis and control type B is the Experimental prosthesis.\n\nOn Day 0, the participant will be evaluated with the Standard prosthesis. A series of Measures (as defined in the next paragraph) will then be recorded. The participant will then take the prosthesis home for one week, and daily use data will be recorded. The participant will return to the clinic for Day 7 Measures and download of the daily use data. During this session, the participant will be fit with the second system, undergo occupational therapy in the clinic, and Measures will be recorded. There will be no washout period as the prosthesis is expected to be in daily use. The participants will go home for a four-week period and return on Day 35 for a third set of Measures. At this time, the participant will be asked which prosthesis he/she prefers.\n\nThere are limited functional outcome assessment options for the planned comparison. However, the investigators will test functional measures at the clinic appointments, examine daily use data, and administer several qualitative surveys to assess participant outcomes.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Trans-radial limb difference.\n* Candidate for a 2+ degree-of-freedom (DoF) myoelectric pattern recognition prosthesis as determined by the study prosthetist\n* Active pattern recognition myoelectric prosthesis user\n* Fluent in English\n* Age of 18 years or greater\n\nExclusion Criteria:\n\n* Patients with a residual limb that is unhealed from the amputation surgery\n* Patients with easily damaged or sensitive skin who would not tolerate EMG electrodes\n* Unhealed wounds\n* Significant cognitive deficits as determined upon clinical evaluation\n* Significant neurological deficits as determined upon clinical evaluation\n* Significant physical deficits of the residual limb impacting full participation in the study as determined upon clinical evaluation\n* Uncontrolled pain or phantom pain impacting full participation in the study as determined upon clinical evaluation\n* Serious uncontrolled medical problems as judged by the project therapist'}, 'identificationModule': {'nctId': 'NCT04043234', 'acronym': 'RESCU', 'briefTitle': 'RESCU System for Robust Upper Limb Prosthesis Control', 'organization': {'class': 'INDUSTRY', 'fullName': 'Infinite Biomedical Technologies'}, 'officialTitle': 'User-driven Retrospectively Supervised Classification Updating (RESCU) System for Robust Upper Limb Prosthesis Control', 'orgStudyIdInfo': {'id': 'U44NS108894', 'link': 'https://reporter.nih.gov/quickSearch/U44NS108894', 'type': 'NIH'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Single-Case Experimental Design', 'description': 'Participants act as their own controls. They first use the Control device, which includes the pattern recognition controller, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow). Participants are then transitioned to the Experimental device, which includes the RESCU controller, Apple iPad, 8 electrodes, batteries, socket, frame, and prosthesis terminal device (hand/wrist/elbow).', 'interventionNames': ['Device: RESCU', 'Device: Pattern Recognition']}], 'interventions': [{'name': 'RESCU', 'type': 'DEVICE', 'description': 'Retrospectively Supervised Classification Updating (RESCU) is founded on two innovations that promise significant improvement in performance and outcome. The first is a highly robust machine intelligence algorithm, an Extreme Learning Machine with Adaptive Sparse Representation (EASRC), and the second is a novel adaptive learning algorithm and communication interface we call Nessa. We contend that these two technologies allow the prosthetic device to adapt to its user from the initial fitting through continuing, long-term use in the activities of daily living, shifting the paradigm of training from the current prospective data gathering methods to a more dynamic retrospective application.', 'armGroupLabels': ['Single-Case Experimental Design']}, {'name': 'Pattern Recognition', 'type': 'DEVICE', 'description': 'Pattern recognition prostheses associate the patterns of activity of multiple EMG sites to the action of a prosthesis. Such strategies have historically required prospective calibration of the EMG activation patterns.', 'armGroupLabels': ['Single-Case Experimental Design']}]}, 'contactsLocationsModule': {'locations': [{'zip': '20910', 'city': 'Silver Spring', 'state': 'Maryland', 'country': 'United States', 'facility': 'Medical Center O&P', 'geoPoint': {'lat': 38.99067, 'lon': -77.02609}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Infinite Biomedical Technologies', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Chief Executive Officer', 'investigatorFullName': 'Rahul Kaliki', 'investigatorAffiliation': 'Infinite Biomedical Technologies'}}}}