Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

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 variation 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 recording 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 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/OpenBCIPytho

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 361)

This bibliography lists 141 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Mar. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Optical non-destructive evaluation of articular cartilage integrity: A review

This paper reviews the current status of the application of optical non-destructive methods, particularly infrared (IR) and near infrared (NIR), in the evaluation of the physiological integrity of articular cartilage. It is concluded that a significant amount of work is still required in order to achieve specificity and clinical applicability of these methods in the assessment and treatment of dysfunctional articular joints

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Aerospace Medicine and Biology. A continuing bibliography with indexes

This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Recent trends, technical concepts and components of computer-assisted orthopedic surgery systems: A comprehensive review

Computer-assisted orthopedic surgery (CAOS) systems have become one of the most important and challenging types of system in clinical orthopedics, as they enable precise treatment of musculoskeletal diseases, employing modern clinical navigation systems and surgical tools. This paper brings a comprehensive review of recent trends and possibilities of CAOS systems. There are three types of the surgical planning systems, including: systems based on the volumetric images (computer tomography (CT), magnetic resonance imaging (MRI) or ultrasound images), further systems utilize either 2D or 3D fluoroscopic images, and the last one utilizes the kinetic information about the joints and morphological information about the target bones. This complex review is focused on three fundamental aspects of CAOS systems: their essential components, types of CAOS systems, and mechanical tools used in CAOS systems. In this review, we also outline the possibilities for using ultrasound computer-assisted orthopedic surgery (UCAOS) systems as an alternative to conventionally used CAOS systems.Web of Science1923art. no. 519

Data-Driven Grasp Synthesis - A Survey

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic

Asimov's Coming Back

Ever since the word ‘ROBOT’ first appeared in a science\ud fiction in 1921, scientists and engineers have been trying\ud different ways to create it. Present technologies in\ud mechanical and electrical engineering makes it possible\ud to have robots in such places as industrial manufacturing\ud and assembling lines. Although they are\ud essentially robotic arms or similarly driven by electrical\ud power and signal control, they could be treated the\ud primitive pioneers in application. Researches in the\ud laboratories go much further. Interdisciplines are\ud directing the evolution of more advanced robots. Among these are artificial\ud intelligence, computational neuroscience, mathematics and robotics. These disciplines\ud come closer as more complex problems emerge.\ud From a robot’s point of view, three basic abilities are needed. They are thinking\ud and memory, sensory perceptions, control and behaving. These are capabilities we\ud human beings have to adapt ourselves to the environment. Although\ud researches on robots, especially on intelligent thinking, progress slowly, a revolution\ud for biological inspired robotics is spreading out in the laboratories all over the world