Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control.
In this study, human robot collaboration (HRC) via force myography (FMG) bio-signal was investigated. Interactive hand force was estimated during moving a wooden rod in 3D with a Kuka robot. A baseline FMG-based deep convolutional neural network (FMG-DCNN) model could moderately estimate applied forces during the HRC task. Model performance can be improved with additional training data; however, collection of it was impractical and time-consuming. Available long-term multiple source data (32 feature spaces) during human robot interaction (HRI) with a linear robot collected over a long time period might be useful. Therefore, we explored a cross-domain generalization (CDG) technique that allowed pretraining a model to transfer knowledge between two unrelated source (2D-HRI) and target data (3D-HRC) for the first time. An FMG-based transfer learning with CDG (TL-CDG) model trained with these multiple source domains was examined in estimating applied forces from 16-channel FMG data during interactions with the Kuka robot. Two target scenarios were evaluated: case i) collaborative task of moving the wooden rod in 3D, and case ii) grasping interactions in 1D. In both cases, few calibration data finetuned the TL-CDG model and improved recognizing out-of-domain target data (case i: R 2 ≈60-63%, and case ii: R 2 ≈79-87%) compared to the baseline FMG-DCNN model. Hence, cross-domain generalization could be useful in platform-independent FMG-based HRI applications.
Estimating ankle joint power can be used to identify gait abnormities, which is usually achieved by employing a complicated biomechanical model using heavy equipment settings. This paper demonstrates deep learning approaches to estimate ankle joint power from two Inertial Measurement Unit (IMU) sensors attached at foot and shank. The purpose of this study was to investigate deep learning models in estimating ankle joint power in practical scenarios, in terms of variance in walking speeds, reduced number of extracted features and inter-subject model adaption. IMU data was collected from nine healthy participants during five walking trials at different speeds on a force-plate-instrumented treadmill while an optical motion tracker was used as ground truth. Three state-of-the-art deep neural architectures, namely Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and, fusion of CNN and LSTM (CNN-LSTM), were developed, trained, and evaluated in predicting ankle joint power by extracting few simple, meaningful features. The proposed architectures were found efficient and promising with higher estimation accuracies (correlation coefficient, R > 0.92 and adjusted R-squared value > 83%) and lower errors (mean squared error < 0.06, and mean absolute error < 0.13) in interparticipant evaluations. Performance evaluations among the three deep regressors showed that LSTM performed comparatively better. Lower standard deviations in mean squared error (0.029) and adjusted R-squared value (5.5%) proved the proposed model's efficiency for all participants.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.