Extraction and Analysis of Respiratory Motion Using Wearable Inertial Sensor System during Trunk Motion.

Respiratory activity is an essential vital sign of life that can indicate changes in typical breathing patterns and irregular body functions such as asthma and panic attacks. Many times, there is a need to monitor breathing activity while performing day-to-day functions such as standing, bending, trunk stretching or during yoga exercises. A single IMU (inertial measurement unit) can be used in measuring respiratory motion; however, breathing motion data may be influenced by a body trunk movement that occurs while recording respiratory activity. This research employs a pair of wireless, wearable IMU sensors custom-made by the Department of Electrical Engineering at San Diego State University. After appropriate sensor placement for data collection, this research applies principles of robotics, using the Denavit-Hartenberg convention, to extract relative angular motion between the two sensors. One of the obtained relative joint angles in the "Sagittal" plane predominantly yields respiratory activity. An improvised version of the proposed method and wearable, wireless sensors can be suitable to extract respiratory information while performing sports or exercises, as they do not restrict body motion or the choice of location to gather data.

Active brainwave pattern generation for brain-to-machine communication.

Over the years of research, Electroencephalogram (EEG) signal study has grown to give promising outcomes. A lot of research has been done on implementing brain-computer-interfaces, and the brain-computer interface (BCI) algorithm as well as the study of the effects of different stimuli on brain signals. This paper intends to make progress toward that goal by developing a responsive real-time EEG-based brain-to-machine communication system by generating distinct EEG signals at will and identification of the explicit pattern that they reflect for the presented self-induced internal visual and auditory stimuli. The brain-to-machine communication system delivers the real-time capture, analysis, and visualization of the brain signal patterns that can be used for smart medical applications such as rehabilitation robotic control, smart wheelchair, etc.

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface.

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

Design of mechanical components for vibration reduction in an atomic force microscope.

Vibration is a key factor to be considered when designing the mechanical components of a high precision and high speed atomic force microscope (AFM). It is required to design the mechanical components so that they have resonant frequencies higher than the external and internal vibration frequencies. In this work, the mechanical vibration in a conventional AFM system is analyzed by considering its mechanical components, and a vibration reduction is then achieved by reconfiguring the mechanical components. To analyze the mechanical vibration, a schematic of the lumped model of the AFM system is derived and the vibrational influences of the AFM components are experimentally examined. Based on this vibration analysis, a reconfigured AFM system is proposed and its effects are compared to a conventional system through a series of simulations and experiments.

Control of voice coil motor nanoscanners for an atomic force microscopy system using a loop shaping technique.

The voice coil motor nanoscanner has the advantages of large working range, easy control, and low cost compared to the conventional lead zirconate titanate driven nanoscanner. However, it has a small damping problem which causes mechanical vibration. The mechanical vibration reduces the accuracy as well as servobandwidth, which deteoriates the atomic force microscopy (AFM) image of the samples. In order to solve the vibration problem, the loop shaping technique [for vertical (z)] and input prefilter [for lateral (xy)] are applied. Experimental results of the proposed techniques are presented for vertical (z) and lateral (xy) scanner. Finally, the AFM images are provided to investigate its effect.