A method to determine the optimal features for control of a powered lower-limb prostheses

Farrell, Matthew Todd; Herr, Hugh M.

doi:10.1109/iembs.2011.6091493

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…However, determining the user intent of when to transition between these modes is difficult. A promising approach is to use pattern recognition algorithms [6][7][8]. While other options are available, such as a button press, a system that does not burden the user and transitions seamlessly, automatically and naturally between locomotion modes based on the amputee's intent would be ideal.…”

Section: Introductionmentioning

confidence: 99%

Classifying the intent of novel users during human locomotion using powered lower limb prostheses

Young

Simon

Fey

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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Intent recognition systems using pattern recognition technology to control powered lower-limb prostheses are promising for seamlessly changing between locomotion modes -such as transitioning from level walking to stair ascent. These transitions can be accomplished by training an algorithm to recognize the patterns of mechanical and/or myoelectric signals an amputee generates during and between different locomotion modes. While low error rates can be achieved with this method, it typically requires a substantial amount of training data to be gathered. To alleviate this burden, this study investigated training a user-independent classifier from a pool of lower limb amputees performing level walking, ramps and stairs on a powered prosthesis and tested generalization of the classifier to a novel subject. The effect of using the amputee's EMG signals in combination with the mechanical sensors on the leg was also evaluated for this userindependent classifier. Generalization was poor to a novel subject --48% overall recognition rate with EMG and 62% without (mechanical sensors only). However, an important system improvement could be made by including a few level walking trials of the novel subject (only a few minutes of data collection) in the training data, the overall recognition rate improved to 86% with EMG and 83% without.

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Section: Introductionmentioning

confidence: 99%

Classifying the intent of novel users during human locomotion using powered lower limb prostheses

Young

Simon

Fey

et al. 2013

2013 6th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…24 In a study by Farrell and Herr the number of lower limb optical markers was reduced to three: lateral ankle, shank and lateral knee. 72 Local coordinate systems for identifying joint segments for congruent tracking of joint motion have been identified by the International Society of Biomechanics. 51,73 Motion capture systems primarily determine joint angles.…”

Section: Experimental Sensor Equipment For Evaluating Prosthesis Effimentioning

confidence: 99%

“…Farrell and Herr conclude that intrinsic control for the prostheses are superior to EMG signal regarding terrain identification. 72 Farrell and Herr simulated an inertial measurement unit consisting of a threedimensional accelerometer and three-dimensional gyroscope with an optical motion system. 72 The simulation implicates the future objective of integrating a robust accelerometer and gyroscope package into a powered prosthesis.…”

Section: Experimental Sensor Equipment For Evaluating Prosthesis Effimentioning

confidence: 99%

“…72 Farrell and Herr simulated an inertial measurement unit consisting of a threedimensional accelerometer and three-dimensional gyroscope with an optical motion system. 72 The simulation implicates the future objective of integrating a robust accelerometer and gyroscope package into a powered prosthesis. The Ossur Proprio Foot modulates ankle angle as a function of the prosthesis accelerometer signal.…”

Section: Experimental Sensor Equipment For Evaluating Prosthesis Effimentioning

confidence: 99%

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Advances Regarding Powered Prosthesis for Transtibial Amputation

LeMoyne

2015

J. Mech. Med. Biol.

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The necessity for developing advanced prostheses are apparent in light of projections that the forecast for the number of people enduring amputation will double by the year 2050. The transtibial powered prosthesis that enables positive mechanical work about the ankle during the powered plantar flexion aspect of stance phase constitutes a paradigm shift in available transtibial prostheses. The objective of the review is to advocate the state of the art regarding the transtibial powered prosthesis. The historic origins of the prosthesis and motivations for amputation are clarified. The phases of gait and the compensatory mechanisms and asymmetries inherent with passive transtibial prostheses are described. The three general classes of transtibial prosthesis (passive, energy storage and return and powered prostheses) are defined. Subsystems that are integral to the powered prosthesis are explained, such as the series elastic actuator and control architecture. Gait analysis systems and their role for the test and evaluation of energy storage and return and powered prostheses are demonstrated. Future advanced concepts; such as the integration of titin into novel muscle models that account for force enhancement and force depression including their implications for cutting edge bio-inspired actuators are elucidated. The review accounts for the evolution of the prosthetic device with regards to the scope of transtibial amputation and assesses the current state-of-the-art.

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“…Foot-ankle prostheses are typically used for either walking (Herr and Grabowski, 2012) or running (McGowan et al, 2012), but not both. Adaptation to changing conditions or variation in terrain remains a significant issue (Farrell and Herr, 2011;Sinitski et al, 2012;Tkach and Hargrove, 2013;Kannape and Herr, 2014). Advances in prosthesis development have been driven largely by technology (e.g., light-weight materials, long-life batteries, programmable electronics, and wireless communication), rather than by advances in understanding of the biological principles underlying human movement.…”

Section: Introductionmentioning

confidence: 99%

Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level Walking and Stair Ascent

et al. 2018

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Powered ankle-foot prostheses assist users through plantarflexion during stance and dorsiflexion during swing. Provision of motor power permits faster preferred walking speeds than passive devices, but use of active motor power raises the issue of control. While several commercially available algorithms provide torque control for many intended activities and variations of terrain, control approaches typically exhibit no inherent adaptation. In contrast, muscles adapt instantaneously to changes in load without sensory feedback due to the intrinsic property that their stiffness changes with length and velocity. We previously developed a "winding filament" hypothesis (WFH) for muscle contraction that accounts for intrinsic muscle properties by incorporating the giant titin protein. The goals of this study were to develop a WFH-based control algorithm for a powered prosthesis and to test its robustness during level walking and stair ascent in a case study of two subjects with 4-5 years of experience using a powered prosthesis. In the WFH algorithm, ankle moments produced by virtual muscles are calculated based on muscle length and activation. Net ankle moment determines the current applied to the motor. Using this algorithm implemented in a BiOM T2 prosthesis, we tested subjects during level walking and stair ascent. During level walking at variable speeds, the WFH algorithm produced plantarflexion angles (range = −8 to −19 •) and ankle moments (range = 1 to 1.5 Nm/kg) similar to those produced by the BiOM T2 stock controller and to people with no amputation. During stair ascent, the WFH algorithm produced plantarflexion angles (range −15 to −19 •) that were similar to persons with no amputation and were ∼5 times larger on average at 80 steps/min than those produced by the stock controller. This case study provides proof-of-concept that, by emulating muscle properties, the WFH algorithm provides robust, adaptive control of level walking at variable speed and stair ascent with minimal sensing and no change in parameters.

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A method to determine the optimal features for control of a powered lower-limb prostheses

Cited by 25 publications

References 13 publications

Classifying the intent of novel users during human locomotion using powered lower limb prostheses

Classifying the intent of novel users during human locomotion using powered lower limb prostheses

Advances Regarding Powered Prosthesis for Transtibial Amputation

Case Study: A Bio-Inspired Control Algorithm for a Robotic Foot-Ankle Prosthesis Provides Adaptive Control of Level Walking and Stair Ascent

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