Intuitive Adaptive Orientation Control for Enhanced Human–Robot Interaction

Campeau-Lecours, Alexandre; Côté-Allard, Ulysse; Vu, Dinh-Son; Routhier, François; Gosselin, B; Gosselin, Clément

doi:10.1109/tro.2018.2885464

Cited by 27 publications

(16 citation statements)

References 31 publications

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“…As a result, the way in which people interact with machines is constantly evolving towards a better synergy between human intention and machine action. The ease of transcribing intention into commands is highly dependent on the type of interface and its implementations [1]. Within this context, muscle activity offers an attractive and intuitive way to perform gesture recognition as a guidance method [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

et al. 2020

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Surface electromyography (sEMG) provides an intuitive and non-invasive interface from which to control machines. However, preserving the myoelectric control system's performance over multiple days is challenging, due to the transient nature of the signals obtained with this recording technique. In practice, if the system is to remain usable, a time-consuming and periodic recalibration is necessary. In the case where the sEMG interface is employed every few days, the user might need to do this recalibration before every use. Thus, severely limiting the practicality of such a control method. Consequently, this paper proposes tackling the especially challenging task of unsupervised adaptation of sEMG signals, when multiple days have elapsed between each recording, by introducing Self-Calibrating Asynchronous Domain Adversarial Neural Network (SCADANN). SCADANN is compared with two state-of-the-art selfcalibrating algorithms developed specifically for deep learning within the context of EMG-based gesture recognition and three state-of-the-art domain adversarial algorithms. The comparison is made both on an offline and a dynamic dataset (20 participants per dataset), using two different deep network architectures with two different input modalities (temporal-spatial descriptors and spectrograms). Overall, SCADANN is shown to substantially and systematically improves classification performances over no recalibration and obtains the highest average accuracy for all tested cases across all methods.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

et al. 2020

Self Cite

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“…Since the three dimensional position and orientation of the virtual reality controller are tracked, it is possible to completely control the TCP of the robot. This approach also eliminates the need for advanced orientation control methods [20].…”

Section: Robot Programming Interactionmentioning

confidence: 99%

Enabling Humans to Plan Inspection Paths Using a Virtual Reality Interface

Bogaerts¹,

Sels²,

Vanlanduit³

et al. 2019

Preprint

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In this work, we investigate whether humans can manually generate high-quality robot paths for optical inspections. Typically, automated algorithms are used to solve the inspection planning problem. The use of automated algorithms implies that specialized knowledge from users is needed to set up the algorithm. We aim to replace this need for specialized experience, by entrusting a non-expert human user with the planning task. We augment this user with intuitive visualizations and interactions in virtual reality. To investigate if humans can generate high-quality inspection paths, we perform a user study in which users from different experience categories, generate inspection paths with the proposed virtual reality interface. From our study, it can be concluded that users without experience can generate high-quality inspection paths: The median inspection quality of user generated paths ranged between 66-81% of the quality of a state-of-the-art automated algorithm on various inspection planning scenarios. We noticed however, a sizable variation in the performance of users, which is a result of some typical user behaviors. These behaviors are discussed, and possible solutions are provided.

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“…In recent years, the exoskeleton robot has attracted the attention of researchers from all over the world due to its broad application prospects in the fields of power assistance, disability assistance, and rehabilitation [1][2][3][4][5]. Human-machine cooperation technology can effectively improve exoskeleton comfort, which has become a current research focus in the exoskeleton field [4,6].…”

Section: Introductionmentioning

confidence: 99%

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

Liu

Song

et al. 2020

Sensors

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Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient ρ between the estimated angle and actual angle. We also compared three methods (i.e., LSTM using RMS, BPNN (back propagation neural network) using RRTAF, and BPNN using RMS) with LSTM using RRTAF to highlight its good performance. Five healthy subjects participated in the experiment and their eight muscle (i.e., rectus femoris (RF), biceps femoris (BF), semitendinosus (ST), gracilis (GC), semimembranosus (SM), sartorius (SR), medial gastrocnemius (MG), and tibialis anterior (TA)) sEMG signals were taken as algorithm inputs. Moreover, the knee joint angles were used as target values. The experimental results showed that, compared with LSTM using RMS, BPNN using RRTAF, and BPNN using RMS, the average RMSE values of LSTM using RRTAF were respectively reduced by 8.57%, 46.62%, and 68.69%, whereas the average ρ values were respectively increased by 0.31%, 4.15%, and 18.35%. The results demonstrated that LSTM using RRTAF, which contained the time-advanced feature, had better performance for estimating the knee joint motion.

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Intuitive Adaptive Orientation Control for Enhanced Human–Robot Interaction

Cited by 27 publications

References 31 publications

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition

Enabling Humans to Plan Inspection Paths Using a Virtual Reality Interface

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature

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