In this paper, we aim at improving human motion prediction during human-robot collaboration in industrial facilities by exploiting contributions from both physical and physiological signals. Improved human-machine collaboration could prove useful in several areas, while it is crucial for interacting robots to understand human movement as soon as possible to avoid accidents and injuries. In this perspective, we propose a novel human-robot interface capable to anticipate the user intention while performing reaching movements on a working bench in order to plan the action of a collaborative robot. The proposed interface can find many applications in the Industry 4.0 framework, where autonomous and collaborative robots will be an essential part of innovative facilities. A motion intention prediction and a motion direction prediction levels have been developed to improve detection speed and accuracy. A Gaussian Mixture Model (GMM) has been trained with IMU and EMG data following an evidence accumulation approach to predict reaching direction. Novel dynamic stopping criteria have been proposed to flexibly adjust the trade-off between early anticipation and accuracy according to the application. The output of the two predictors has been used as external inputs to a Finite State Machine (FSM) to control the behaviour of a physical robot according to user's action or inaction. Results show that our system outperforms previous methods, achieving a real-time classification accuracy of @math after @math from movement onset.
The aforementioned approaches propose to observe and track the body by means of visual systems, for example by exploiting 3D camera networks, or markers attached to the body. This solution has the drawback of being sensitive to camera occlusions, light variations, and motion blur @cite_20 . imu are probably the main alternative to cameras, they are effectively used to learn new behaviors @cite_27 and control robots in industrial setups @cite_22 . In many cases, a multi-modal approach can be used to enrich the information and overcome limitations of uni-modal systems @cite_7 . Many solutions propose the introduction of physiological signals, recorded directly from the human body. emg signals have been rarely considered as a unique tool for motion prediction @cite_9 due to their non-stationarity and sensitivity to muscular fatigue and stress @cite_18 . Nevertheless, emg are quite popular for controlling exoskeletons or prosthesis @cite_10 and they have proved to be a valuable source of information in cooperative tasks when used in conjunction with other measurement units @cite_28 .
@cite_18 @cite_22 @cite_7 @cite_28 @cite_9 @cite_27 @cite_10 @cite_20
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For the control of unmanned aerial vehicles UAVs in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit IMU. First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the unknown metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter EKF formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions. Monitoring and interpreting (sub)cortical reorganization after stroke may be useful for selecting therapies and improving rehabilitation outcome. To develop computational models that predict behavioral motor improvement from changing brain activation pattern, we are currently working on the implementation of a clinically feasible experimental set-up, which enables recording high quality electroencephalography (EEG) signals during inpatient rehabilitation of upper and lower limbs. The major drawback of current experimental paradigms is the cue-guided repetitive design and the lack of functional movements. In this paper, we assess the usability of the Kinect device (Microsoft Inc., Redmond, WA, USA) for tracking self-paced hand opening and closing movements. Three able-bodied volunteers performed self-paced right hand open-close movement sequences while EEG was recorded from sensorimotor areas and electromyography (EMG) from the right arm from extensor carpi radialis and flexor carpi radialis muscles. The results of the study suggest that the Kinect device allows generation of trigger information that is comparable to the information that can be obtained from EMG. The interaction with robotic devices by means of physiological human signals has become of great interest in the last years because of the capability of catching human intention of movement and translate it in a coherent action performed by a robotic platform. Due to the complexity of EMG signals, several studies have been carried out about models built on a single subject (subject-specific). However, the execution of a certain task presents a common underlying behaviour, even if it is performed by different people. This common behaviour leads to some constraints that could be extracted by looking to different interpretations of the task, obtaining a subject-independent model. The few attempts in literature showed the possibility of creating a multiuser interface able to adapt to novel users (subject-independent). Nevertheless, the majority of the studies focused on classification problems, that are only able to determine the type of movement. We improved the state-of-the-art by introducing an online subject-independent framework able to compute the actual trajectory of the robot motion through a regression technique. The framework is based on a Gaussian Mixture Model (GMM) trained through Surface Electromyography (sEMG) signals coming from human subjects. Wavelet Transform has been used to elaborate the sEMG signals in real time. The goodness of the proposed framework has been tested with two different dataset involving various joints for both upper and lower limbs. The achieved results show that our framework could obtain high performances in both accuracy and computational time by reaching significant correlation (≥ 0.8). The whole procedure has been tested on two robots, a simulated hand and a humanoid, by remapping the human motion to the robotic platforms in order to verify the proper execution of the original movement. In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.