CYBERLEGs: A User-Oriented Robotic Transfemoral Prosthesis with Whole-Body Awareness Control

Ambrozic, Luka; Goršič, Maja; Geeroms, Joost; Flynn, Louis; Lova, Raffaele Molino; Kamnik, Roman; Munih, Marko; Vitiello, Nicola

doi:10.1109/mra.2014.2360278

Cited by 53 publications

(42 citation statements)

References 27 publications

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“…Nevertheless, the sEMG interfaces were prone to fade in a prolonged utilization because of the variations of the skin condition, thereby lowering the accuracy rate in long-term use. With respect to the methods based only on mechanical sensing, our results were higher than those from approaches employing threshold-based finitestate machines [14]. The kinematics inter-subject variability can indeed affect the LMR performance, with thresholding being inherently sensitive to minor variations of the recorded kinematics.…”

Section: B Performance Of the Lmr Processmentioning

confidence: 70%

“…The success rate of decoding methods depends not only on the sensory system constituting the interface, but also on the techniques used for classification. For studies based on mechanical sensing, simple threshold-based finite state machine controllers provided an accuracy rate not higher than 97% [13], [14]. The success rate improved up to 98.8% when processing (e.g., using fuzzy logic-based classifiers) [15] or linear discriminant analysis [16] techniques are applied.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Parri

Yuan

Marconi

et al. 2017

IEEE/ASME Trans. Mechatron.

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Real-time recognition of locomotion-related activities is a fundamental skill that a controller of lower-limb wearable robots should possess. Subject-specific training and reliance on electromyographic interfaces are the main limitations of existing approaches. This study presents a novel methodology for realtime locomotion mode recognition of locomotion-related activities in lower-limb wearable robotics. A hybrid classifier can distinguish among seven locomotion-related activities. First, a timebased approach classifies between static and dynamical states based on gait kinematics data. Second, an event-based fuzzy logic method triggered by foot pressure sensors operates in a subjectindependent fashion on a minimal set of relevant biomechanical features to classify among dynamical modes. The locomotion mode recognition algorithm is implemented on the controller of a portable powered orthosis for hip assistance. An experimental protocol is designed to evaluate the controller performance in an out-of-lab scenario without the need for a subject-specific training. Experiments are conducted on six healthy volunteers performing locomotion-related activities at slow, normal, and fast speeds under the zero-torque and assistive mode of the orthosis. The overall accuracy rate of the controller is 99.4% over more than 10,000 steps, including seamless transitions between different modes. The experimental results show a successful subject-independent performance of the controller for wearable robots assisting locomotion-related activities.

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Section: B Performance Of the Lmr Processmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Parri

Yuan

Marconi

et al. 2017

IEEE/ASME Trans. Mechatron.

Self Cite

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“…Indeed, Subjects #1 and #3 of this experiment participated also in the activities reported in Ambrozic et al (2014) as Subject #1 and Subject #3, respectively. The accuracy rate for Subject #1 in Ambrozic et al (2014) was not higher than 96.2% in all the subphases.…”

Section: Discussionmentioning

confidence: 90%

Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis

et al. 2017

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Restoring locomotion functionality of transfemoral amputees is essential for early rehabilitation treatment and for preserving mobility and independence in daily life. Research in wearable robotics fostered the development of innovative active mechatronic lower-limb prostheses designed with the goal to reduce the cognitive and physical effort of lower-limb amputees in rehabilitation and daily life activities. To ensure benefits to the users, active mechatronic prostheses are expected to be aware of the user intention and properly interact in a closed human-in-the-loop paradigm. In the state of the art various cognitive interfaces have been proposed to online decode the user's intention. Electromyography in combination with mechanical sensing such as inertial or pressure sensors is a widely adopted solution for driving active mechatronic prostheses. In this framework, researchers also explored targeted muscles re-innervation for an objective-oriented surgical amputation promoting wider usability of active prostheses. However, information kept by the neural component of the cognitive interface deteriorates in a prolonged use scenario due to electrodes-related issues, thereby undermining the correct functionality of the active prosthesis. The objective of this work is to present a novel controller for an active transfemoral prosthesis based on whole body awareness relying on a wireless distributed non-invasive sensory apparatus acting as cognitive interface. A finite-state machine controller based on signals monitored from the wearable interface performs subject-independent intention detection of functional tasks such as ground level walking, stair ascent, and sit-to-stand maneuvres and their main sub-phases. Experimental activities carried out with four transfemoral amputees (among them one dysvascular) demonstrated high reliability of the controller capable of providing 100% accuracy rate in treadmill walking even for weak subjects and low walking speeds. The minimum success rate was of 94.8% in performing sit-to-stand tasks. All the participants showed high confidence in using the transfemoral active prosthesis even without training period thanks to intuitiveness of the whole body awareness controller.

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“…Insoles were successfully used in other online applications, such as for providing lower-limb amputees with sensory feedback related to discrete gait events (Crea et al, 2015 , 2017 ), to control an active prosthesis during different locomotion activities (Ambrozic et al, 2014 ) and to control an active lower-limb exoskeleton (Yuan et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

Gastrocnemius Myoelectric Control of a Robotic Hip Exoskeleton Can Reduce the User's Lower-Limb Muscle Activities at Push Off

et al. 2018

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We present a novel assistive control strategy for a robotic hip exoskeleton for assisting hip flexion/extension, based on a proportional Electromyography (EMG) strategy. The novelty of the proposed controller relies on the use of the Gastrocnemius Medialis (GM) EMG signal instead of a hip flexor muscle, to control the hip flexion torque. This strategy has two main advantages: first, avoiding the placement of the EMG electrodes at the human–robot interface can reduce discomfort issues for the user and motion artifacts of the recorded signals; second, using a powerful signal for control, such as the GM, could improve the reliability of the control system. The control strategy has been tested on eight healthy subjects, walking with the robotic hip exoskeleton on the treadmill. We evaluated the controller performance and the effect of the assistance on muscle activities. The tuning of the assistance timing in the controller was subject dependent and varied across subjects. Two muscles could benefit more from the assistive strategy, namely the Rectus Femoris (directly assisted) and the Tibialis Anterior (indirectly assisted). A significant correlation was found between the timing of the delivered assistance (i.e., synchronism with the biological hip torque), and reduction of the hip flexors muscular activity during walking; instead, no significant correlations were found for peak torque and peak power. Results suggest that the timing of the assistance is the most significant parameter influencing the effectiveness of the control strategy. The findings of this work could be important for future studies aimed at developing assistive strategies for walking assistance exoskeletons.

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CYBERLEGs: A User-Oriented Robotic Transfemoral Prosthesis with Whole-Body Awareness Control

Cited by 53 publications

References 27 publications

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots

Whole Body Awareness for Controlling a Robotic Transfemoral Prosthesis

Gastrocnemius Myoelectric Control of a Robotic Hip Exoskeleton Can Reduce the User's Lower-Limb Muscle Activities at Push Off

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