In this paper, the authors present an evaluation of a new biometric based on electrocardiogram (ECG) waveforms. ECG data were collected from 50 subjects during three data-recording sessions on different days using a simple user interface, where subjects held two electrodes on the pads of their thumbs using their thumb and index fingers. Data from session 1 were used to establish an enrolled database, and data from the remaining two sessions were used as test cases. Classification was performed using three different quantitative measures: percent residual difference, correlation coefficient, and a novel distance measure based on wavelet transform. The wavelet distance measure has a classification accuracy of 89%, outperforming the other methods by nearly 10%. This ECG person-identification modality would be a useful supplement for conventional biometrics, such as fingerprint and palm recognition systems.

Since the da Vinci surgical system was approved by the Food and Drug Administration (FDA) in 2000, the development and deployment of various robot-assisted minimally invasive surgery (MIS) systems have been largely expedited and boomed. With the rapid advancement of robotic techniques in recent decades, robot-assisted systems have been widely used in various surgeries including orthopedics. These robot-related techniques are transforming the conventional ways to conduct surgical procedures. Robot-assisted orthopedic surgeries have become more and more popular due to their potential benefits of increased accuracy and precision in surgical outcomes, enhanced reproducibility, reduced technical variability, decreased pain, and faster recovery time. In this paper, robotic systems and navigation techniques in typical orthopedic surgeries are reviewed, especially for arthroplasty. From the perspective of robotics and engineering, the systems and techniques are divided into two main categories, i.e., robotic systems (RSs), and computer-aided navigation systems (CANSs). The former is further divided into autonomous RS, hands-on RS, and teleoperated RS. For the latter, three key elements in CANS are introduced, including 3D modeling, registration, and navigation. Lastly, the potential advantages and disadvantages of the RS and CANS are summarized and discussed. Future perspectives on robotics in orthopedics, as well as the challenges, are presented.

In this paper, we demonstrate that the electrocardiogram (ECG) can be used as a biometric. While previous studies have shown the potential of an ECG biometric, this research demonstrates it under conditions that include intra-individual variations and a simple user interface, consisting of electrodes held on the pads of the subject's thumbs. ECG person identification was accomplished through quantitative comparisons of an unknown signal to enrolled signals. The quantitative comparisons were: the correlation coefficient and a wavelet distance measure. It was found that the combination of these two methods provided improved performance, relative to either individual method. ECG person identification accuracy was 90.8%. While this accuracy is relatively low compared to conventional biometrics, such as fingerprints, the ECG can be used as supplementary information for a multi-modal biometric system. A multi-modal system that includes the ECG would have increased accuracy and robustness, without necessarily requiring any change to the perceived user interface. At minimum, the ECG would be useful in providing liveness detection