Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.
Cell migration studies in 3D environments become more popular, as cell behaviors in 3D are more similar to the behaviors of cells in a living organism (in vivo). We focus on the 3D angiogenic sprouting in microfluidic devices, where Endothelial Cells (ECs) burrow into the gel matrix and form solid lumen vessels. Phase contrast microscopy is used for long-term observation of the unlabeled ECs in the 3D microfluidic devices. Two template matching based approaches are proposed to automatically detect the unlabeled ECs in the angiogenic sprouts from the acquired experimental phase contrast images. Cell and non-cell templates are obtained from these phase contrast images as the training data. The first approach applies Partial Least Square Regression (PLSR) to find the discriminative features and their corresponding weight to distinguish cells and non-cells, whereas the second approach relies on Principal Component Analysis (PCA) to reduce the template feature dimension and Support Vector Machine (SVM) to find their corresponding weight. Through a sliding window manner, the cells in the test images are detected. We then validate the detection accuracy by comparing the results with the same images acquired with a confocal microscope after cells are fixed and their nuclei are stained. More accurate numerical results are obtained for approach I (PLSR) compared to approach II (PCA & SVM) for cell detection. Automatic cell detection will aid in the understanding of cell migration in 3D environment and in turn result in a better understanding of angiogenesis.
Microcirculation lesion is a common symptom of chronic liver diseases in the form of vasculature deformation and circulation alteration. In acute to chronic liver diseases such as biliary atresia, microcirculation lesion can have an early onset. Detection of microcirculation lesion is meaningful for studying the progression of liver disease. We have combined wide-field fluorescence microscopy and a laser speckle contrast technique to characterize hepatic microcirculation in vivo without labeling in a bile-duct ligation rat fibrosis model of biliary atresia. Through quantitative image analysis of four microcirculation parameters, we observed significant microcirculation lesion in the early to middle stages of fibrosis. This bimodal imaging method is useful to assess hepatic microcirculation lesion for the study of liver diseases.
In this paper the design and static analysis of a novel artificial muscle system are presented. The proposed design is
based on exponential strain amplification applied to PZT stack actuators. Exponential strain amplification is achieved by
means of a nested cellular architecture. The primary limitation of the nested strain amplification mechanisms is the loss
of blocking force due to structural compliance. Therefore, to quantify and improve the performance of the design,
analytical expressions are obtained for the blocking force and free displacement of two separate amplification
mechanisms using Castigliano's strain energy and displacement theorem. Measured values for blocking force and free
displacement validate the static behavior predicted by the solid mechanics. Design implications for the amplification
mechanisms are then enumerated based on the theoretical modeling.
Wearable sensors for continuous monitoring of vital signs for extended periods of weeks or months are expected to revolutionize healthcare services in the home and workplace as well as in hospitals and nursing homes. This invited paper describes recent research progress in wearable health monitoring technology and its clinical applications, with emphasis on blood pressure and circulatory monitoring. First, a finger ring-type wearable blood pressure sensor based on photo plethysmogram is presented. Technical issues, including motion artifact reduction, power saving, and wearability enhancement, will be addressed. Second, sensor fusion and sensor networking for integrating multiple sensors with diverse modalities will be discussed for comprehensive monitoring and diagnosis of health status. Unlike traditional snap-shot measurements, continuous monitoring with wearable sensors opens up the possibility to treat the physiological system as a dynamical process. This allows us to apply powerful system dynamics and control methodologies, such as adaptive filtering, single- and multi-channel system identification, active noise cancellation, and adaptive control, to the monitoring and treatment of highly complex physiological systems. A few clinical trials illustrate the potentials of the wearable sensor technology for future heath care services.
A new approach to designing and controlling multiple artificial muscle actuators using Segmented Binary Control (SBC) is presented and is implemented using shape memory alloys (SMA). SMA actuators are segmented into many independently controlled, spatially discrete volumes, each contributing a small displacement to create a large motion. The segmented architecture of SMA wires is extended to a multi-axis actuator array by arranging them in a two-dimensional array. For multi axis case, the number of segments can be reduced by activating adjacent SMA wires with coupled segments. Coupled segments activate multiple actuators that the segment covers. Although independence of the adjacent SMA wires is reduced to a certain degree, coordinated movements are generated. The shape and position of the coupled segments can be designed using the "similarity" of output trajectories of each actuator. SBC is extended into Hysteresis Loop Control, which reduces the delay in the system by using four different temperatures instead of just two temperatures that the SBC uses. Thermoelectric devices are used to locally heat and cool the SMA wires. Single-axis experimental setup is built to verify and compare the SBC and HLC, and multi-axis array actuator system that uses SBC is built with ten SMA actuators in parallel.
This paper presents an active noise cancellation technique for recovering wearable biosensor signals corrupted by bodily motion. A finger mounted photoplethysmograph (PPG) ring sensor with a collocated MEMS accelerometer is considered. The system by which finger acceleration disturbs PPG output is identified and a means of modeling this relationship is prescribed using either FIR or Laguerre models. This means of modeling motivates the use of a recursive least squares active noise cancellation technique using the MEMS accelerometer reading as an input for a FIR or Laguerre model. The model parameters are identified and tuned in real time to minimize the power of the recovered PPG signal. Experiments show that the active noise cancellation method can recover pulse information from PPG signals corrupted with up to 2G of acceleration with 85% improvement in mean squared error.
Wearable sensing networks have been the focus of the robotics and biotechnology industry for a number of years. While there has been quite a bit of work on sensor technologies, the physical integration of the electronic components with the human body has not received much attention. We have created a body area network that seeks to address this issue by relying on two innovations; the use of conductive fabrics, and the use of DC powerline communication. By combining these innovations, we have created a truly wearable network that allows full generality of sensor location, spatial distribution of the medium to reduce overall bulk, and maintains sufficiently low line impedance for simultaneous power and data delivery over a single conductor. We have created a method for analysis of the transmission properties of conductive fabric garments that takes into account the unique geometry of the human body. We will provide a verification of our analysis method experimental results.
A new type of touch sensor for detecting contact pressure at human fingertips is presented. Fingernails are instrumented with miniature LEDs and photodetectors in order to measure changes in the nail color when the fingers are pressed against a surface, this new sensor allows the fingers to directly contact the environment without obstructing the human's natural haptic rather than the finger pad. Photo- reflective plethysmorgraphy is used for measuring the nail color. A prototype fingernail sensor and is constructed and used to create a fingertip-free electronic glove. Using these new touch senors, a novel human-machine interface, termed 'virtual switch', is developed and applied to robot programming. The virtual switch detects human intention of pressing a switch by measuring the finger touch signal and the hand location. Instead of embedding a physical switch in a wall or panel, the virtual switch requires merely an image of a switch posted on the surface, and hence can be placed on any surface where one wants to place switches.