RESUMEN
Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.
RESUMEN
Laser scanning optical beam induced current (OBIC) microscopy has become a powerful and nondestructive alternative to other complicated methods like electron beam induced current (EBIC) microscopy, for high resolution defect analysis of electronic devices. OBIC is based on the generation of electron-hole pairs in the sample due to the raster scanning of a focused laser beam with energy equal or greater than the band gap energy and synchronized detection of resultant current profile with respect to the beam positions. OBIC is particularly suitable to localize defect sites caused by metal-semiconductor interdiffusion or electrostatic discharge (ESD). OBIC signals, thus, are capable of revealing the parameters/factors directly related to the reliability and efficiency of the electronic device under test (DUT). In this review, the basic principles of OBIC microscopy strategies and their notable applications in semiconductor device characterization are elucidated. An overview on the developments of OBIC microscopy is also presented. Specifically, the recent progresses on the following three OBIC measurement strategies have been reviewed, which include continuous laser based single photon OBIC, pulsed laser based single photon OBIC, and multiphoton OBIC microscopy for three-dimensional mapping of photocurrent response of electronic devices at high spatiotemporal resolution. Challenges and future prospects of OBIC in characterizing complex electronic devices are also discussed. HIGHLIGHTS: Characterization of electronic device quality is of paramount importance. Optical beam induced current (OBIC) microscopy offers spatially resolved mapping of local electronic properties. This review presents the principle and notable applications of OBIC microscopy.
RESUMEN
With the advancement in machine learning and signal processing techniques, electromyogram (EMG) signals have increasingly gained importance in man-machine interaction. Multifingered hand prostheses using surface EMG for control has appeared in the market. However, EMG based control is still rudimentary, being limited to a few hand postures based on higher number of EMG channels. Moreover, control is non-intuitive, in the sense that the user is required to learn to associate muscle remnants actions to unrelated posture of the prosthesis. Herein lies the promise of a low channel EMG based grasp classification architecture for development of an embedded intelligent prosthetic controller. This paper reports classification of six grasp types used during 70% of daily living activities based on two channel forearm EMG. A feature vector through principal component analysis of discrete wavelet transform coefficients based features of the EMG signal is derived. Classification is through radial basis function kernel based support vector machine following preprocessing and maximum voluntary contraction normalization of EMG signals. 10-fold cross validation is done. We have achieved an average recognition rate of 97.5%.