Intuitive Control of a Powered Prosthetic Leg During Ambulation

Abstract

IMPORTANCE Some patients with lower leg amputations may be candidates for motorized prosthetic limbs. Optimal control of such devices requires accurate classification of the patient's ambulation mode (eg, on level ground or ascending stairs) and natural transitions between different ambulation modes.OBJECTIVE 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. DESIGN, SETTING, AND PARTICIPANTS Blinded, randomized crossover clinical trial conducted between August 2012 and November 2013 in a research laboratory at the Rehabilitation Institute of Chicago. Participants were 7 patients with unilateral above-knee (n = 6) or knee-disarticulation (n = 1) amputations. All patients were capable of ambulation within their home and community using a passive prosthesis (ie, one that does not provide external power).INTERVENTIONS Electrodes were placed over 9 residual limb muscles and EMG signals were recorded as patients ambulated and completed 20 circuit trials involving level-ground walking, ramp ascent and descent, and stair ascent and descent. Data were acquired simultaneously from 13 mechanical sensors embedded on the prosthesis. Two real-time pattern recognition algorithms, using either (1) mechanical sensor data alone or (2) mechanical sensor data in combination with EMG data and historical information from earlier in the gait cycle, were evaluated. The order in which patients used each configuration was randomized (1:1 blocked randomization) and double-blinded so patients and experimenters did not know which control configuration was being used. MAIN OUTCOMES AND MEASURESThe main outcome of the study was classification error for each real-time control system. Classification error is defined as the percentage of steps incorrectly predicted by the control system. RESULTSIncluding EMG signals and historical information in the real-time control system resulted in significantly lower classification error (mean, 7.9% [95% CI, 6.1%-9.7%]) across a mean of 683 steps (range, 640-756 steps) compared with using mechanical sensor data only (mean, 14.1% [95% CI, 9.3%-18.9%]) across a mean of 692 steps (range, 631-775 steps), with a mean difference between groups of 6.2% (95% CI, 2.7%-9.7%] (P = .01). CONCLUSIONS AND RELEVANCEIn this study of 7 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. These preliminary findings, if confirmed, have the potential to improve the control of powered leg prostheses.

Targeted Muscle Reinnervation for the Upper and Lower Extremity

“…EMG signals from residual thigh muscles contain useful control information that can be combined with mechanical sensor data to create an accurate and responsive control system that enables intuitive, seamless transitions between ambulation modes [33]. Interestingly, EMG alone provides accurate control of a powered prosthesis during non-weight-bearing activities [34].…”

Detection of Gait Modes Using an Artificial Neural Network during Walking with a Powered Ankle-Foot Orthosis

“…Several studies have explored gait mode recognition [1–20]. Most were for prosthetic devices [2–11, 17–20].…”

“…Most were for prosthetic devices [2–11, 17–20]. Some used manual switching schemes to deal with changing gait modes during walking.…”

“…Young et al designed a dynamic Bayesian network for processing both mechanical (velocity and position) and EMG sensors for intent recognition [19, 20]. Miller et al performed walking mode classification by using linear discriminant analysis (LDA) and support vector machine (SVM) on EMG signals for transtibial amputees [21].…”

“…In summary, existing gait mode recognition schemes have shortcomings of not being autonomous [4, 5], delay in detection of a new mode until the next step [16, 18], difficulty of implementation in real-time [6–10, 17], dependency on trained stair heights [16], or need for a large number of sensors [12–15, 19, 20]. There are currently no reliable gait mode recognition algorithms available which can detect all of the modes without long delays and without the use of a large number of sensors.…”