Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variations even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recorded continuously which is clearly separable for a particular action while recording sEMG signals. To segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset in an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https: github.com GPrathap OpenBCIPython.
A Kinect-based hand movement detection algorithm was developed by @cite_9 , and both EEG and EMG signals were acquired at the same time for the experiment. Kinect was used to detect two different classes of output, the open hand and closed hand. The capabilities of Kinect was under-utilized because the device was used only to detect two events. @cite_23 developed a Human Machine Interface (HMI) using both sEMG and Microsoft Kinect inputs. The architecture is designed to feed the algorithm with either Kinect data or sEMG data from the upper hand to control a human sized service robot. Though the authors have used both sEMG and Kinect, the idea of training the sEMG data with the help of Kinect is not practised. @cite_22 have conducted an experiment on Multichannel sEMG in clinical gait analysis using a portable sEMG device and multiple cameras to analyse the pattern of sEMG along with kinematic and kinetic of the body. The authors were able to extract the body angles and correlate them with the sEMG data.
@cite_9 @cite_22 @cite_23
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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. Human-robot control interfaces have received increased attention during the past decades, since the introduction of robots in everyday life. In this paper, a novel Human-Machine Interface (HMI) is developed, which contains two components. One is based on the surface electromyography (sEMG) signal, which is from the human upper limb and the other is based on the Microsoft Kinect sensor. The proposed interface allows the user to control in real time a mobile humanoid robot arm in 3-D space, using upper limb motion estimation based only on sEMG recordings and the Kinect input. The efficiency of the method is verified using real-time experiments, including random arm motions in the 3-D space with variable hand speed profiles. 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. This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture.