Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to assist carrying an unknown load. The method is based on user's intentions estimated through a wireless EMG armband allowing movement direction and intensity estimation along 1 Degree of Freedom. This control law aimed to behave like a gravity compensation except that the mass of the load does not need to be known. The proposed approach was tested by 10 participants on a lifting task with a single Degree of Freedom upper-limb exoskeleton. Participants performed it in three different conditions : without assistance, with an exact gravity compensation and with the proposed method based on EMG armband (Myo Armband). The evaluation of the efficiency of the assistance was based on EMG signals captured on seven muscles (objective indicator) and a questionnaire (subjective indicator). Results showed a statically significant reduction of mean activity of the biceps, erector spinae and deltoid by 20% ± 14, 18% ± 12 and 25% ± 16 respectively while comparing the proposed method with no assistance. In addition, similar muscle activities were found both in the proposed method and the traditional gravity compensation. Subjective evaluation showed better precision, efficiency and responsiveness of the proposed method compared to the traditional one.
Implementing an intuitive control law for an upperlimb exoskeleton to perform force augmentation is a challenging issue in the field of human-robot collaboration. The aim of this study is to design an innovative approach to calibrate electromyography (EMG) data in order to detect the intention to lift or put down a charge while wearing an upper-limb exoskeleton. Based on a low-cost EMG sensor bracelet placed around the arm (Myo armband, Thalmics Lab, Ontario), a subject-specific mapping procedure is implemented to discriminate motion intentions during lifting tasks with a 1-DoF upper-limb exoskeleton. The processing is divided into two main parts: (i) direction estimation with an artificial neural network, and (ii) a model-based intensity prediction. The mapping procedure has been tested on 7 healthy participants with a precision of 96.9 ± 3.1% for the classification and a RMS Error of 3.8 ± 0.8N at the end effector. This study opens up the way for fast-deployment applications involving exoskeletons or cobots.
Implementing an intuitive control law for an upperlimb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The goal of this study is to adapt an EMG-based control system to a user based on individual characteristics. To this aim, a method has been designed to tune the parameters of control using objective criteria, improving user's feedback. The user's response time is used as an objective value to adapt the gain of the controller. The proposed approach was tested on 10 participants during a lifting task. Two different conditions have been used to control the exoskeleton: with a generic gain and with a personalized gain. EMG signals was captured on five muscles to evaluate the efficiency of the conditions and the user's adaptation. Results showed a statistically significant reduction of mean muscle activity of the deltoid between the beginning and the end of each situation (28.6%, standard deviation (SD) 13.5% to 17.2%, SD 7.3%, of Relative Maximal Contraction for the generic gain and from 24.9%, SD 8.5%, to 18%, SD 6.8%, of Relative Maximal Contraction for the personalized gain). When focusing on the first assisted movements, the personalized gain induced a mean activity of the deltoid significantly lower (29%, SD 8.0%, of Relative Maximal Contraction and 37.4%, SD 9.5%, of Relative Maximal Contraction, respectively). Subjective evaluation showed that the system with a personalized gain was perceived as more intuitive, and required less concentration when compared to the system with a generic gain.
Implementing an intuitive control law for an upper-limb exoskeleton dedicated to force augmentation is a challenging issue in the field of human-robot collaboration. The goal of this study is to adapt an EMG-based control system to a user based on individual characteristics. To this aim, a method has been designed to tune the parameters of control using objective criteria, improving user's feedback. The user's response time is used as an objective value to adapt the gain of the controller. The proposed approach was tested on 10 participants during a lifting task. Two different conditions have been used to control the exoskeleton: with a generic gain and with a personalized gain. EMG signals was captured on five muscles to evaluate the efficiency of the conditions and the user's adaptation. Results showed a statistically significant reduction of mean muscle activity of the deltoid between the beginning and the end of each situation (28.6 ± 13.5% to 17.2 ± 7.3% of Relative Maximal Contraction for the generic gain and from 24.9 ± 8.5% to 18.0 ± 6.8% of Relative Maximal Contraction for the personalized gain). When focusing on the first assisted movements, the personalized gain induced a mean activity of the deltoid significantly lower (29.0 ± 8.0% of Relative Maximal Contraction and 37.4 ± 9.5% of Relative Maximal Contraction, respectively). Subjective evaluation showed that the system with a personalised gain was perceived as more intuitive, and required less concentration when compared to the system with a generic gain.
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