Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- Active hand device (1)
- Audio Signal Processing (1)
- Backbone exoskeleton (1)
- Biomedical engineering (1)
- Blind Source Separation (1)
-
- Cartilage (1)
- Computer vision (1)
- Convolutional Source Separation (1)
- Decision tree (1)
- IMU (1)
- Independent Component Analysis (1)
- Information Theory (1)
- Intent recognition (1)
- Intraoperative monitoring (1)
- Ionic Polymer Metallic Composite (1)
- Kalman filter (1)
- Knee (1)
- Microsurgery (1)
- Neurophysiology (1)
- Osteoarthritis (1)
- Physiological tremor (1)
- Real-time (1)
- SEMG (1)
- Segmentation (1)
- Signal processing (1)
- Somatosensory evoked potentials (1)
- Spinal injury (1)
- Tremor modeling (1)
- Publication
- Publication Type
Articles 1 - 5 of 5
Full-Text Articles in Signal Processing
Analysis, Segmentation And Prediction Of Knee Cartilage Using Statistical Shape Models, Joseph Michael Johnson
Analysis, Segmentation And Prediction Of Knee Cartilage Using Statistical Shape Models, Joseph Michael Johnson
Doctoral Dissertations
Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from …
A Novel Signal Processing Method For Intraoperative Neurophysiological Monitoring In Spinal Surgeries, Krishnatej Vedala
A Novel Signal Processing Method For Intraoperative Neurophysiological Monitoring In Spinal Surgeries, Krishnatej Vedala
FIU Electronic Theses and Dissertations
Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas …
Development Of A Novel Handheld Device For Active Compensation Of Physiological Tremor, Abhijit Saxena
Development Of A Novel Handheld Device For Active Compensation Of Physiological Tremor, Abhijit Saxena
Abhijit Saxena
In microsurgery, the human hand imposes certain limitations in accurately positioning the tip of a device such as scalpel. Any errors in the motion of the hand make microsurgical procedures difficult and involuntary motions such as hand tremors can make some procedures significantly difficult to perform. This is particularly true in the case of vitreoretinal microsurgery. The most familiar source of involuntary motion is physiological tremor. Real-time compensation of tremor is, therefore, necessary to assist surgeons to precisely position and manipulate the tool-tip to accurately perform a microsurgery. In this thesis, a novel handheld device (AID) is described for compensation …
Activity Intent Recognition Of The Torso Based On Surface Electromyography And Inertial Measurement Units, Zhe Zhang
Masters Theses 1911 - February 2014
This thesis presents an activity mode intent recognition approach for safe, robust and reliable control of powered backbone exoskeleton. The thesis presents the background and a concept for a powered backbone exoskeleton that would work in parallel with a user. The necessary prerequisites for the thesis are presented, including the collection and processing of surface electromyography signals and inertial sensor data to recognize the user’s activity. The development of activity mode intent recognizer was described based on decision tree classification in order to leverage its computational efficiency. The intent recognizer is a high-level supervisory controller that belongs to a three-level …
Independent Component Analysis Enhancements For Source Separation In Immersive Audio Environments, Yue Zhao
Independent Component Analysis Enhancements For Source Separation In Immersive Audio Environments, Yue Zhao
Theses and Dissertations--Electrical and Computer Engineering
In immersive audio environments with distributed microphones, Independent Component Analysis (ICA) can be applied to uncover signals from a mixture of other signals and noise, such as in a cocktail party recording. ICA algorithms have been developed for instantaneous source mixtures and convolutional source mixtures. While ICA for instantaneous mixtures works when no delays exist between the signals in each mixture, distributed microphone recordings typically result various delays of the signals over the recorded channels. The convolutive ICA algorithm should account for delays; however, it requires many parameters to be set and often has stability issues. This thesis introduces the …