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Levi J Hargrove - One of the best experts on this subject based on the ideXlab platform.
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Online adaptive neural control of a robotic lower limb prosthesis.
Journal of neural engineering, 2018Co-Authors: John A. Spanias, Ann M Simon, Suzanne B. Finucane, Eric J. Perreault, Levi J HargroveAbstract:Objective The purpose of this study was to develop and evaluate an adaptive Intent Recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's Intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive Intent Recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation. Main results Our proposed algorithm accurately and consistently identified the user's Intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate. Significance This study demonstrates that our adaptive Intent Recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's Intent with low error rates.
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Across-Day Lower Limb Pattern Recognition Performance of a Powered Knee-Ankle Prosthesis
2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 2018Co-Authors: Ann M Simon, Emily A Seyforth, Levi J HargroveAbstract:Powered lower limb prostheses have the capabilities to assist individuals with a lower limb amputation during ambulation. While these devices can generate power at the knee and/or ankle to assist with incline walking and stair climbing, it is difficult to control the transition between these ambulation modes in a seamless and natural way. Pattern Recognition has been suggested as an alternative to using a key fob to switch between modes and recent results have shown reliable performance (less than 5% error rate) across five ambulation modes. In this study we investigated performance of a similar system across multiple sessions of use, a necessary step prior to clinical use. Two individuals with a transfemoral amputation used a powered knee-ankle for five ambulation activities including level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent over four sessions spaced out over at least two months. An Intent Recognition system was trained using embedded prosthesis mechanical sensors with varying amounts of data collected across the sessions to determine the effect of multi-session use and increased variation in the activities trained. Overall system error rate decreased from 1.45% [0.3%] when the system was trained with Session 1 data only and tested with Session 4 data to 0.60% [0.02%] when the system was trained with Sessions 1-3 data and tested with Session 4 data. These results demonstrate that a reliable Intent Recognition system can be created with multiple sessions of use, bringing lower limb Intent Recognition systems for powered prostheses one step closer to clinical viability.
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delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee ankle prosthesis
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Ann M Simon, Aaron Young, John A. Spanias, Suzanne B. Finucane, Kimberly A Ingraham, Elizabeth G Halsne, Levi J HargroveAbstract:Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study’s goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis’ mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable Intent Recognition system for a powered knee-ankle prosthesis.
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user Intent prediction with a scaled conjugate gradient trained artificial neural network for lower limb amputees using a powered prosthesis
International Conference of the IEEE Engineering in Medicine and Biology Society, 2016Co-Authors: Richard B Woodward, John A. Spanias, Levi J HargroveAbstract:Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming. By using subject independent datasets, whereby a unique subject is tested against a pooled dataset of other subjects, we believe subject training time can be reduced while still achieving an accurate classification. We present here an Intent Recognition system using an artificial neural network (ANN) with a scaled conjugate gradient learning algorithm to classify gait Intention with user-dependent and independent datasets for six unilateral lower limb amputees. We compare these results against a linear discriminant analysis (LDA) classifier. The ANN was found to have significantly lower classification error (P<0.05) than LDA with all user-dependent step-types, as well as transitional steps for user-independent datasets. Both types of classifiers are capable of making fast decisions; 1.29 and 2.83 ms for the LDA and ANN respectively. These results suggest that ANNs can provide suitable and accurate offline classification in prosthesis gait prediction.
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anticipatory kinematics and muscle activity preceding transitions from level ground walking to stair ascent and descent
Journal of Biomechanics, 2016Co-Authors: Joshua Peng, Todd A Kuiken, Nicholas P. Fey, Levi J HargroveAbstract:The majority of fall-related accidents are during stair ambulation-occurring commonly at the top and bottom stairs of each flight, locations in which individuals are transitioning to stairs. Little is known about how individuals adjust their biomechanics in anticipation of walking-stair transitions. We identified the anticipatory stride mechanics of nine able-bodied individuals as they approached transitions from level ground walking to stair ascent and descent. Unlike prior investigations of stair ambulation, we analyzed two consecutive "anticipation" strides preceding the transitions strides to stairs, and tested a comprehensive set of kinematic and electromyographic (EMG) data from both the leading and trailing legs. Subjects completed ten trials of baseline overground walking and ten trials of walking to stair ascent and descent. Deviations relative to baseline were assessed. Significant changes in mechanics and EMG occurred in the earliest anticipation strides analyzed for both ascent and descent transitions. For stair descent, these changes were consistent with observed reductions in walking speed, which occurred in all anticipation strides tested. For stair ascent, subjects maintained their speed until the swing phase of the latest anticipation stride, and changes were found that would normally be observed for decreasing speed. Given the timing and nature of the observed changes, this study has implications for enhancing Intent Recognition systems and evaluating fall-prone or disabled individuals, by testing their abilities to sense upcoming transitions and decelerate during locomotion. Language: en
Ann M Simon - One of the best experts on this subject based on the ideXlab platform.
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Online adaptive neural control of a robotic lower limb prosthesis.
Journal of neural engineering, 2018Co-Authors: John A. Spanias, Ann M Simon, Suzanne B. Finucane, Eric J. Perreault, Levi J HargroveAbstract:Objective The purpose of this study was to develop and evaluate an adaptive Intent Recognition algorithm that continuously learns to incorporate a lower limb amputee's neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach We present a powered lower limb prosthesis that was configured to acquire the user's neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user's Intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive Intent Recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user's model of neural data during ambulation. Main results Our proposed algorithm accurately and consistently identified the user's Intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm-with a 6.66% [3.16%] relative decrease in error rate. Significance This study demonstrates that our adaptive Intent Recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user's Intent with low error rates.
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Across-Day Lower Limb Pattern Recognition Performance of a Powered Knee-Ankle Prosthesis
2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 2018Co-Authors: Ann M Simon, Emily A Seyforth, Levi J HargroveAbstract:Powered lower limb prostheses have the capabilities to assist individuals with a lower limb amputation during ambulation. While these devices can generate power at the knee and/or ankle to assist with incline walking and stair climbing, it is difficult to control the transition between these ambulation modes in a seamless and natural way. Pattern Recognition has been suggested as an alternative to using a key fob to switch between modes and recent results have shown reliable performance (less than 5% error rate) across five ambulation modes. In this study we investigated performance of a similar system across multiple sessions of use, a necessary step prior to clinical use. Two individuals with a transfemoral amputation used a powered knee-ankle for five ambulation activities including level-ground walking, ramp ascent, ramp descent, stair ascent, and stair descent over four sessions spaced out over at least two months. An Intent Recognition system was trained using embedded prosthesis mechanical sensors with varying amounts of data collected across the sessions to determine the effect of multi-session use and increased variation in the activities trained. Overall system error rate decreased from 1.45% [0.3%] when the system was trained with Session 1 data only and tested with Session 4 data to 0.60% [0.02%] when the system was trained with Sessions 1-3 data and tested with Session 4 data. These results demonstrate that a reliable Intent Recognition system can be created with multiple sessions of use, bringing lower limb Intent Recognition systems for powered prostheses one step closer to clinical viability.
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delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee ankle prosthesis
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Ann M Simon, Aaron Young, John A. Spanias, Suzanne B. Finucane, Kimberly A Ingraham, Elizabeth G Halsne, Levi J HargroveAbstract:Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study’s goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis’ mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable Intent Recognition system for a powered knee-ankle prosthesis.
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Intent Recognition in a powered lower limb prosthesis using time history information
Annals of Biomedical Engineering, 2014Co-Authors: Ann M Simon, Levi J Hargrove, Aaron Young, Nicholas P. FeyAbstract:New computerized and powered lower limb prostheses are being developed that enable amputees to perform multiple locomotion modes. However, current lower limb prosthesis controllers are not capable of transitioning these devices automatically and seamlessly between locomotion modes such as level-ground walking, stairs and slopes. The focus of this study was to evaluate different Intent Recognition interfaces, which if configured properly, may be capable of providing more natural transitions between locomotion modes. Intent Recognition can be accomplished using a multitude of different signals from mechanical sensors on the prosthesis. Since these signals are non-stationary over any given stride, and gait is cyclical, time history information may improve locomotion mode Recognition. The authors propose a dynamic Bayesian network classification strategy to incorporate prior sensor information over the gait cycle with current sensor information. Six transfemoral amputees performed locomotion circuits comprising level-ground walking and ascending/descending stairs and ramps using a powered knee and ankle prosthesis. Using time history reduced steady-state misclassifications by over half (p < 0.01), when compared to strategies that did not use time history, without reducing Intent Recognition performance during transitions. These results suggest that including time history information across the gait cycle can enhance locomotion mode Intent Recognition performance.
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A Training Method for Locomotion Mode Prediction Using Powered Lower Limb Prostheses
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014Co-Authors: Aaron J Young, Ann M Simon, Levi J HargroveAbstract:Recently developed lower-limb prostheses are capable of actuating the knee and ankle joints, allowing amputees to perform advanced locomotion modes such as step-over-step stair ascent and walking on sloped surfaces. However, transitions between these locomotion modes and walking are neither automatic nor seamless. This study describes methods for construction and training of a high-level Intent Recognition system for a lower-limb prosthesis that provides natural transitions between walking, stair ascent, stair descent, ramp ascent, and ramp descent. Using mechanical sensors onboard a powered prosthesis, we collected steady-state and transition data from six transfemoral amputees while the five locomotion modes were performed. An Intent Recognition system built using only mechanical sensor data was 84.5% accurate using only steady-state training data. Including training data collected while amputees performed seamless transitions between locomotion modes improved the overall accuracy rate to 93.9%. Training using a single analysis window at heel contact and toe off provided higher Recognition accuracy than training with multiple analysis windows. This study demonstrates the capability of an Intent Recognition system to provide automatic, natural, and seamless transitions between five locomotion modes for transfemoral amputees using powered lower limb prostheses.
Aaron Young - One of the best experts on this subject based on the ideXlab platform.
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delaying ambulation mode transition decisions improves accuracy of a flexible control system for powered knee ankle prosthesis
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017Co-Authors: Ann M Simon, Aaron Young, John A. Spanias, Suzanne B. Finucane, Kimberly A Ingraham, Elizabeth G Halsne, Levi J HargroveAbstract:Powered lower limb prostheses can assist users in a variety of ambulation modes by providing knee and/or ankle joint power. This study’s goal was to develop a flexible control system to allow users to perform a variety of tasks in a natural, accurate, and reliable way. Six transfemoral amputees used a powered knee-ankle prosthesis to ascend/descend a ramp, climb a 3- and 4-step staircase, perform walking and standing transitions to and from the staircase, and ambulate at various speeds. A mode-specific classification architecture was developed to allow seamless transitions at four discrete gait events. Prosthesis mode transitions (i.e., the prosthesis’ mechanical response) were delayed by 90 ms. Overall, users were not affected by this small delay. Offline classification results demonstrate significantly reduced error rates with the delayed system compared to the non-delayed system (p < 0.001). The average error rate for all heel contact decisions was 1.65% [0.99%] for the non-delayed system and 0.43% [0.23%] for the delayed system. The average error rate for all toe off decisions was 0.47% [0.16%] for the non-delayed system and 0.13% [0.05%] for the delayed system. The results are encouraging and provide another step towards a clinically viable Intent Recognition system for a powered knee-ankle prosthesis.
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analysis of using emg and mechanical sensors to enhance Intent Recognition in powered lower limb prostheses
Journal of Neural Engineering, 2014Co-Authors: Aaron Young, Levi J Hargrove, Todd A KuikenAbstract:Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode Intent Recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern Recognition‐based Intent Recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting Intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce Intent Recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance Intent Recognition performance; this strategy should be considered for future real-time experiments.
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Intent Recognition in a powered lower limb prosthesis using time history information
Annals of Biomedical Engineering, 2014Co-Authors: Ann M Simon, Levi J Hargrove, Aaron Young, Nicholas P. FeyAbstract:New computerized and powered lower limb prostheses are being developed that enable amputees to perform multiple locomotion modes. However, current lower limb prosthesis controllers are not capable of transitioning these devices automatically and seamlessly between locomotion modes such as level-ground walking, stairs and slopes. The focus of this study was to evaluate different Intent Recognition interfaces, which if configured properly, may be capable of providing more natural transitions between locomotion modes. Intent Recognition can be accomplished using a multitude of different signals from mechanical sensors on the prosthesis. Since these signals are non-stationary over any given stride, and gait is cyclical, time history information may improve locomotion mode Recognition. The authors propose a dynamic Bayesian network classification strategy to incorporate prior sensor information over the gait cycle with current sensor information. Six transfemoral amputees performed locomotion circuits comprising level-ground walking and ascending/descending stairs and ramps using a powered knee and ankle prosthesis. Using time history reduced steady-state misclassifications by over half (p < 0.01), when compared to strategies that did not use time history, without reducing Intent Recognition performance during transitions. These results suggest that including time history information across the gait cycle can enhance locomotion mode Intent Recognition performance.
Michael Goldfarb - One of the best experts on this subject based on the ideXlab platform.
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Multiclass real-time Intent Recognition of a powered lower limb prosthesis
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Huseyin Atakan Varol, Frank Sup, Michael GoldfarbAbstract:This paper describes a control architecture and Intent Recognition approach for the real-time supervisory control of a powered lower limb prosthesis. The approach infers user Intent to stand, sit, or walk, by recognizing patterns in prosthesis sensor data in real time, without the need for instrumentation of the sound-side leg. Specifically, the Intent recognizer utilizes time-based features extracted from frames of prosthesis signals, which are subsequently reduced to a lower dimensionality (for computational efficiency). These data are initially used to train Intent models, which classify the patterns as standing, sitting, or walking. The trained models are subsequently used to infer the user's Intent in real time. In addition to describing the generalized control approach, this paper describes the implementation of this approach on a single unilateral transfemoral amputee subject and demonstrates via experiments the effectiveness of the approach. In the real-time supervisory control experiments, the Intent recognizer identified all 90 activity-mode transitions, switching the underlying middle-level controllers without any perceivable delay by the user. The Intent recognizer also identified six activity-mode transitions, which were not intended by the user. Due to the Intentional overlapping functionality of the middle-level controllers, the incorrect classifications neither caused problems in functionality, nor were perceived by the user.
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real time gait mode Intent Recognition of a powered knee and ankle prosthesis for standing and walking
IEEE International Conference on Biomedical Robotics and Biomechatronics, 2008Co-Authors: Huseyin Atakan Varol, Michael GoldfarbAbstract:This paper describes a real-time gait mode Intent Recognition approach for the supervisory control of a powered transfemoral prosthesis. The proposed approach infers user Intent by recognizing patterns in the prosthesis sensorpsilas signals in real-time, eliminating the need for sound-side instrumentation and allowing fast mode switching. Simple time based features extracted from frames of prosthesis signals are reduced to lower dimensions. Gaussian Mixture Models are trained using an experimental database for gait mode classification. A voting scheme is applied as a post-processing step to increase the robustness of decision making. The effectiveness of the proposed method is shown via gait experiments on a treadmill with a healthy subject using an able bodied adapter.
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real time Intent Recognition for a powered knee and ankle transfemoral prosthesis
IEEE International Conference on Rehabilitation Robotics, 2007Co-Authors: Huseyin Atakan Varol, Michael GoldfarbAbstract:This paper describes a real-time gait Intent Recognition approach for use in controlling a fully powered transfemoral prosthesis. Rather than utilize an "echo control" as proposed by others, which requires instrumentation of the sound-side leg, the proposed approach infers user Intent based on the characteristic shape of the force and moment vector of interaction between the user and prosthesis. The real-time Intent Recognition approach utilizes a K-nearest neighbor algorithm with majority voting and threshold biasing schemes to increase its robustness. The ability of the approach to recognize in real time a person's Intent to stand or walk at one of three different speeds is demonstrated on measured biomechanics data.
Huseyin Atakan Varol - One of the best experts on this subject based on the ideXlab platform.
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Multiclass real-time Intent Recognition of a powered lower limb prosthesis
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Huseyin Atakan Varol, Frank Sup, Michael GoldfarbAbstract:This paper describes a control architecture and Intent Recognition approach for the real-time supervisory control of a powered lower limb prosthesis. The approach infers user Intent to stand, sit, or walk, by recognizing patterns in prosthesis sensor data in real time, without the need for instrumentation of the sound-side leg. Specifically, the Intent recognizer utilizes time-based features extracted from frames of prosthesis signals, which are subsequently reduced to a lower dimensionality (for computational efficiency). These data are initially used to train Intent models, which classify the patterns as standing, sitting, or walking. The trained models are subsequently used to infer the user's Intent in real time. In addition to describing the generalized control approach, this paper describes the implementation of this approach on a single unilateral transfemoral amputee subject and demonstrates via experiments the effectiveness of the approach. In the real-time supervisory control experiments, the Intent recognizer identified all 90 activity-mode transitions, switching the underlying middle-level controllers without any perceivable delay by the user. The Intent recognizer also identified six activity-mode transitions, which were not intended by the user. Due to the Intentional overlapping functionality of the middle-level controllers, the incorrect classifications neither caused problems in functionality, nor were perceived by the user.
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real time gait mode Intent Recognition of a powered knee and ankle prosthesis for standing and walking
IEEE International Conference on Biomedical Robotics and Biomechatronics, 2008Co-Authors: Huseyin Atakan Varol, Michael GoldfarbAbstract:This paper describes a real-time gait mode Intent Recognition approach for the supervisory control of a powered transfemoral prosthesis. The proposed approach infers user Intent by recognizing patterns in the prosthesis sensorpsilas signals in real-time, eliminating the need for sound-side instrumentation and allowing fast mode switching. Simple time based features extracted from frames of prosthesis signals are reduced to lower dimensions. Gaussian Mixture Models are trained using an experimental database for gait mode classification. A voting scheme is applied as a post-processing step to increase the robustness of decision making. The effectiveness of the proposed method is shown via gait experiments on a treadmill with a healthy subject using an able bodied adapter.
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real time Intent Recognition for a powered knee and ankle transfemoral prosthesis
IEEE International Conference on Rehabilitation Robotics, 2007Co-Authors: Huseyin Atakan Varol, Michael GoldfarbAbstract:This paper describes a real-time gait Intent Recognition approach for use in controlling a fully powered transfemoral prosthesis. Rather than utilize an "echo control" as proposed by others, which requires instrumentation of the sound-side leg, the proposed approach infers user Intent based on the characteristic shape of the force and moment vector of interaction between the user and prosthesis. The real-time Intent Recognition approach utilizes a K-nearest neighbor algorithm with majority voting and threshold biasing schemes to increase its robustness. The ability of the approach to recognize in real time a person's Intent to stand or walk at one of three different speeds is demonstrated on measured biomechanics data.