Browsing by Author "Lock, Blair A."

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    A Novel Research And Clinical Approach To Using Gel Liners For Collection Of Surface Myoelectric Signals For Prosthetic Control
    (2011) Lipschutz, Robert D.; Lock, Blair A.
    For more than two decades, individuals with lower limb amputations have been successfully fitted with gel liners constructed from a variety of materials. Prosthetists have also reported moderate success with gel liners fit to individuals with upper limb amputations who use externally powered prostheses. At the Center for Bionic Medicine, we have explored a novel approach to collecting myoelectric signals from individuals with lower limb or upper limb amputations—using electrodes embedded in gel liners. Initial designs have proven more comfortable and easier to don than traditional suction sockets and have allowed us to eliminate the need for separate connection of pre-amplifiers. We believe this technology will be of benefit to individuals with upper or lower limb amputations and eliminate some of the clinical challenges and reported drawbacks of current myoelectric fittings. The next step is to combine the new liner technology with advanced electronics to control actuated drive units in both upper limb and lower limb prostheses. In this contribution we describe the evolution of this liner technology from initial experiences through current status to future directions.
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    ADAPTIVE PATTERN RECOGNITION TO ENSURE CLINICAL VIABILITY OVER TIME
    (2008) Sensinger, Jonathan W.; Lock, Blair A.; Kuiken, Todd A.
    Pattern Recognition is a useful tool for deciphering movement intent from myoelectric signals. In order to be clinically viable over time, recognition paradigms must be capable of adapting with the user. Most existing paradigms are static, although two forms of adaptation have received limited attention: Supervised adaptation achieves high accuracy, since the intended class is known, but at the cost of repeated cumbersome training sessions. Unsupervised adaptation attempts to achieve high accuracy without explicitly being told the intended class, thus achieving adaptation that is invisible to the user at the cost of reduced accuracy. This paper reports a novel adaptive experiment on eight subjects that allowed a post-hoc comparison of four supervised and three unsupervised adaptation paradigms. All supervised adaptation paradigms reduced error over time by at least 23%. Most unsupervised adaptation paradigms failed to achieve statistically significant reductions in error due to the uncertainty of the correct class. One method that selected high-confidence samples showed the most practical potential, although other methods warrant future investigation outside of a laboratory setting. The ability to provide supervised adaptation should be incorporated into any clinically viable pattern recognition controller, and unsupervised adaptation should receive renewed interest in order to provide invisible adaptation.
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    CLINICALLY PRACTICAL APPLICATIONS OF PATTERN RECOGNITION FOR MYOELECTRIC PROSTHESES
    (2008) Lock, Blair A.; Schultz, Aimee E.; Kuiken, Todd A.
    The promise of pattern recognition for improved control of upper-extremity powered prostheses has existed for a long time. During the years of offline research and algorithm development, very little experience has been gained with real-time use in clinical and chronic settings. Our group, having the benefit of working with subjects who have undergone targeted muscle reinnervation (TMR) surgery, is at the forefront of real-world application of pattern recognition for upper extremity amputees. Based on our experiences, we highlight a progression of myoelectric control schemes from conventional control to enhanced pattern recognition control, stressing the application of simple pattern recognition schemes to replace more conventional control. These clinically practical pattern recognition systems incorporate a realistic number of electrodes and the ability to control available prosthetic components. Our experience suggests how the impending, and initial deployment of pattern recognition-controlled prostheses for daily use can be more approachable than what is depicted in high-dimension studies common in the literature today.
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    Prosthesis-Guided Training For Practical Use Of Pattern Recognition Control Of Prostheses
    (2011) Lock, Blair A.; Simon, Ann M.; Stubblefield, Kathy; Hargrove, Levi J.
    The potential for pattern recognition to improve powered prosthesis control has been discussed for many years. One remaining barrier to at-home use of these techniques is that practical methods of user prompting during system training are lacking. Most research and development of pattern recognition systems for prosthesis control has relied on on-screen cues to prompt the prosthesis wearer during signal collection; therefore most systems require connection to a computer or external device. We have developed a method called Prosthesis-Guided Training (PGT) to address this issue. In PGT, the prosthesis itself moves through a pre-programmed sequence of motions to prompt the wearer to elicit the appropriate muscle contractions. PGT requires no extra hardware and allows wearers to retrain, refresh, or recalibrate the controller in many locations and situations. Training via PGT is self-initiated and requires only about 1 minute of the wearer’s time. Furthermore, PGT provides a practical mechanism for overcoming malfunctioning or changing inputs, addresses differences in routine donning, and results in acquisition of myoelectric signals representative of those elicited during functional use. Qualitative and quantitative data acquired to investigate the efficacy of PGT suggest that it is an intuitive, effective, and clinically viable method of training pattern recognition–controlled prostheses.
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    Prosthesis-Guided Training Increases Functional Wear Time And Improves Tolerance To Malfunctioning Inputs Of Pattern Recognition–Controlled Prostheses
    (2011) Simon, Ann M.; Lock, Blair A.; Stubblefield, Kathy A.; Hargrove, Levi J.
    A remaining barrier to the clinical accessibility of pattern recognition systems is the lack of practical methods to acquire the myoelectric signals required to train the system. Many current methods involve screen-guided training (SGT), where wearers connected to an external computer perform muscle contractions synchronized with a sequence of visual cues. The system complexity prevents easy retraining when signal conditions change. We have developed a method called prosthesis-guided training (PGT), where the prosthesis itself provides the cues by moving through a sequence of preprogrammed motions; screen prompting and external connections are eliminated. Five prosthesis wearers performed a repetitive clothespin placement task using pattern recognition control. Wearers demonstrated similar baseline functionality between systems trained with PGT (10 ± 4 clothespins) and SGT (12 ± 7 clothespins) (p = 0.56). To investigate the efficacy of PGT retraining, real-world issues (e.g. broken wires, external noise) were simulated to accelerate control degradation. Sessions ended when wearers indicated loss of functional control. On average, wearers maintained function through two malfunctioning inputs, placing 48 ± 17 clothespins in 31.6 ± 16.2 minutes when allowed to retrain using PGT. These results suggest that PGT acquires adequate training data and may enable longer-lasting functional use, potentially increasing prosthesis wear time and reducing device rejection.