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.
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. Abstract Background Application of surface electromyography (SEMG) to the clinical evaluation of neuromuscular disorders can provide relevant “diagnostic” contributions in terms of nosological classification, localization of focal impairments, detection of pathophysiological mechanisms, and functional assessment. Methods The present review article elaborates on: (i) the technical aspects of the myoelectric signals acquisition within a protocol of clinical gait analysis (multichannel recording, surface vs. deep probes, electrode placing, encumbrance effects), (ii) the sequence of procedures for the subsequent data processing (filtering, averaging, normalization, repeatability control), and (iii) a set of feasible strategies for the final extraction of clinically useful information. Findings Relevant examples of SEMG application to functional diagnosis are provided. Interpretation Emphasis is given to the key role of SEMG along with kinematic and kinetic analysis, for non-invasive assessment of relevant pathophysiological mechanisms potentially hindering the gait function, such as changes in passive muscle–tendon properties (peripheral non-neural component), paresis, spasticity, and loss of selectivity of motor output in functionally antagonist muscles. 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.