Clinical trial NCT02355912: Adaptive Recalibration of Prosthetic Leg Neural Control System
Official title: Adaptive Recalibration of Prosthetic Leg Neural Control System
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Amputation
Keywords: Transfemoral
Lead sponsor: Shirley Ryan AbilityLab
Sponsor class: OTHER
First posted: 2015-02-04
Enrollment: 22
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: BASIC_SCIENCE

Brief summary: The purpose of this study is for transfemoral amputees to walk with an experimental robotic prosthesis. Electric signals will be measured from their muscles and used to help control an artificial leg. The investigators will record from sensors placed on a prosthesis and electric signals measured from muscles in the participants leg to see if the investigators can develop better computer programs to help predict subject actions and prostheses function.

Detailed description: The investigators propose to use a powered knee-ankle prosthesis that is not yet commercially available. The hierarchical control framework the investigators are developing will be equally applicable to any prosthetic leg that needs to be transitioned between ambulation modes, including microprocessor-controlled passive devices.

The overall objective is to develop and evaluate an adaptive framework for controlling lower limb prostheses that compensates for changes in EMG signals. When a participant walks on a lower limb prosthesis, the output of the high-level controller (or ambulation mode predictor) directly influences patterns generated by the participant. After the participant has completed the subsequent stride, a gait pattern estimator (GPE), will provide a label of what the participant actually did. This may differ from the ambulation mode predictor output if there was a misclassification. The label will then be used to update the ambulation mode predictor algorithm such that future steps are predicted with higher accuracies. Finally, the resulting system will be transferred to an embedded system and tested in real-time with transfemoral amputees and compared to a non-adaptive system.

Eligibility criteria: Amputee Subjects

Inclusion Criteria:

* Lower Limb Amputees
* K2/K3/K4 ambulators

Exclusion Criteria:

* Over 250lbs body weight
* Inactive, physically unfit
* cognitive deficits or visual impairment that would impair their ability to give informed consent or to follow simple instructions during the experiments
* Pregnant women
* co-morbidity that interferes with the study (e.g. stroke, pace maker placement, severe ischemia cardiac disease, etc.)

Able-bodied Subjects:

Inclusion Criteria:

* no injury on either lower extremity

Exclusion Criteria:

* inactive, physically unfit
* over 250 lbs body weight
* cognitive deficits or visual impairment that would impair their ability to give informed consent or impair their ability to follow simple instructions during the experiments
* Pregnant women (status determined by self-reporting)
* co-morbidity that interferes with the study (e.g. stroke, pace maker placement, severe ischemia cardiac disease, etc.)

Primary outcomes:
measure: Decreased error rates for pattern recognition system used to predict ambulation modes
timeFrame: Assessed at approximately 2 months and 6 months after enrollment
Secondary outcomes:
Other outcomes:
Locations:
city: Chicago
country: United States
facility: Rehabilitation Institute of Chicago
state: Illinois
Study officials:
affiliation: Shirley Ryan AbilityLab
name: Levi Hargrove, PhD
role: PRINCIPAL_INVESTIGATOR
References: