CHICAGO — Major lower limb amputation accounted for up to 76% of amputations sustained by U.S. military personnel from 2001 to 2011. Although prosthetic lower limbs have been available, they traditionally have not restored full function.
Now, according to a study published recently in the Journal of the American Medical Association, leg prostheses that provide power are becoming available but researchers are finding that different ambulation modes — such as level-ground walking, ramp ascent and descent, and stair ascent and descent — require fundamentally different control sequences for operating powered prosthetic limbs.1
Researchers from the University of Chicago and Northwestern University in Evanston, IL, sought to determine the effect of including electromyographic (EMG) data and historical information from prior gait strides in a real-time control system for a powered prosthetic leg capable of level-ground walking, stair ascent and descent, ramp ascent and descent, and natural transitions between these ambulation modes.
To do that, patients might have to slow down, stop, press buttons on an electronic key fob or perform unrelated body movements (e.g., exaggerated hip extension; rocking forward and backward on the prosthesis). Maximizing benefit from the devices and ensuring patient safety require that control systems automatically identify which ambulation mode the patient is using and provide the correct prosthesis response, according to the report.
Participants in the blinded, randomized crossover clinical trial conducted between August 2012 and November 2013 were seven patients with unilateral above-knee or knee-disarticulation amputations. All were capable of ambulation within their homes and communities using a passive prosthesis.
Researchers placed electrodes over nine residual limb muscles and then recorded EMG signals as patients completed 20 circuit trials involving level-ground walking, ramp ascent and descent, and stair ascent and descent. At the same time, data was being recorded from 13 mechanical sensors embedded on the prosthesis.
The study evaluated two real-time pattern recognition algorithms — mechanical sensor data alone or mechanical sensor data in combination with EMG data and historical information from earlier in the gait cycle — based on classification error, defined as the percentage of steps incorrectly predicted by the control system.
Significantly lower classification error occurred when EMG signals and historical information were included in the real-time control system, 7.9% across a mean of 683 steps. With mechanical sensor data only, classification error was 14.1% across a mean of 692 steps.
“In this study of seven patients with lower limb amputations, inclusion of EMG signals and temporal gait information reduced classification error across ambulation modes and during transitions between ambulation modes,” the authors concluded. “These preliminary findings, if confirmed, have the potential to improve the control of powered leg prostheses.”
The next phase of the study will partner with the San Antonio, TX, Military Medical Center to include some Iraq and Afghanistan veterans, study authors said.
1 Hargrove LJ, Young AJ, Simon AM, Fey NP, Lipschutz RD, Finucane SB, Halsne EG, Ingraham KA, Kuiken TA. Intuitive control of a powered prosthetic leg during ambulation: a randomized clinical trial. JAMA. 2015 Jun 9;313(22):2244-52. doi:10.1001/jama.2015.4527. PubMed PMID: 26057285.
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