This PDF file contains the front matter associated with SPIE Proceedings Volume 10216, including the Title Page, Copyright information, Table of Contents, Introduction (if any), and Conference Committee listing.
We present both experimental and theoretical studies for investigating DNA molecules attached on metallic nanospheres. We have developed an efficient and accurate numerical method to investigate light scattering from plasmonic nanospheres on a substrate covered by a shell, based on the Green’s function approach with suitable spherical harmonic basis. Next, we use this method to study optical scattering from DNA molecules attached to metallic nanoparticles placed on a substrate and compare with experimental results. We obtain fairly good agreement between theoretical predictions and the measured ellipsometric spectra. The metallic nanoparticles were used to detect the binding with DNA molecules in a microfluidic setup via spectroscopic ellipsometry (SE), and a detectable change in ellipsometric spectra was found when DNA molecules are captured on Au nanoparticles. Our theoretical simulation indicates that the coverage of Au nanosphere by a submonolayer of DNA molecules, which is modeled by a thin layer of dielectric material (which may absorb light), can lead to a small but detectable spectroscopic shift in both the Ψ and Δ spectra with more significant change in Δ spectra in agreement with experimental results. Our studies demonstrated the ultrasensitive capability of SE for sensing submonolayer coverage of DNA molecules on Au nanospheres. Hence the spectroscopic ellipsometric measurements coupled with theoretical analysis via an efficient computation method can be an effective tool for detecting DNA molecules attached on Au nanoparticles, thus achieving label-free, non-destructive, and high-sensitivity biosensing with nanoscale resolution.
An important problem in modern therapeutics at the proteomic level remains to identify therapeutic targets in a plentitude of high-throughput data from experiments relevant to a variety of diseases. This paper presents the application of novel modern control concepts, such as pinning controllability and observability applied to the glioma cancer stem cells (GSCs) protein graph network with known and novel association to glioblastoma (GBM). The theoretical frameworks provides us with the minimal number of "driver nodes", which are necessary, and their location to determine the full control over the obtained graph network in order to provide a change in the network’s dynamics from an initial state (disease) to a desired state (non-disease). The achieved results will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, to design and test novel therapeutic solutions.
Obstructive sleep apnea syndrome is becoming a prevalent disease for both adults and children. It is described as the cessation of breath for at least 10 seconds during sleep. Detecting sleep apnea is considered as a troublesome and timeconsuming method, which requires the patients to stay one or more nights in dedicated sleep disorder rooms with sensors physically attached to their body. Undiagnosed thereby untreated sleep apnea patients are under high risk of hypertension, heart attack, traffic accident through fatigue and sleeplessness. In this project, nasal and oral respiratory information is obtained with utilizing thermocouple and oxygen saturation in the blood is obtained with utilizing pulse oximeter. An analog hardware circuit is designed to readout thermocouple and pulse oximeter signals. According to this respiratory and pulse oximetry signals, obstructive sleep apnea is detected in real time with using a software implemented into an ARM based processor. An Android mobile application is developed to record and display the oxygen saturation, heart rate and respiratory signal data during sleep. ARM based processor and mobile application communication is established via Bluetooth interface to reduce cabling on the patient. In summary, a portable, low cost and user friendly device to detect obstructive sleep apnea which is able to share the necessary information to the patients and doctors for the duration of the whole sleep cycle is developed.
Heart rate measurement using a visual spectrum recording of the face has drawn interest over the last few years as a technology that can have various health and security applications. In our previous work, we have shown that it is possible to estimate the heart beat timing accurately enough to perform heart rate variability analysis for contactless stress detection. However, a major confounding factor in this approach is the presence of movement, which can interfere with the measurements. To mitigate the effects of movement, in this work we propose the use of face detection and tracking based on the Karhunen-Loewe algorithm in order to counteract measurement errors introduced by normal subject motion, as expected during a common seated conversation setting. We analyze the requirements on image acquisition for the algorithm to work, and its performance under different ranges of motion, changes of distance to the camera, as well and the effect of illumination changes due to different positioning with respect to light sources on the acquired signal. Our results suggest that the effect of face tracking on visual-spectrum based cardiac signal estimation depends on the amplitude of the motion. While for larger-scale conversation-induced motion it can significantly improve estimation accuracy, with smaller-scale movements, such as the ones caused by breathing or talking without major movement errors in facial tracking may interfere with signal estimation. Overall, employing facial tracking is a crucial step in adapting this technology to real-life situations with satisfactory results.
This article presents a preliminary prototype of a wearable instrument for oxygen saturation and ECG monitoring. The proposed measuring system is based on the light reflection variability of a LED emission on the subject temple. Besides, the system has the capacity to incorporate electrodes to obtain ECG measurements. All measurements are stored and transmitted to a mobile device (tablet or smartphone) through a Bluetooth link.
Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links.
In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system’s eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.
In the near future, robots and humans will share the same environment and perform tasks cooperatively. For intuitive, safe, and reliable physical human-robot interaction (pHRI), sensorized robot skins for tactile measurements of contact are necessary. In a previous study, we presented skins consisting of strain gauge arrays encased in silicone encapsulants. Although these structures could measure normal forces applied directly onto the sensing elements, they also exhibited blind spots and response asymmetry to certain loading patterns. This study presents a parametric investigation of piezoresistive polymeric strain gauge that exhibits a symmetric omniaxial response thanks to its novel star-shaped structure. This strain gauge relies on the use of gold micro-patterned star-shaped structures with a thin layer of PEDOT:PSS which is a flexible polymer with piezoresistive properties. In this paper, the sensor is first modeled and comprehensively analyzed in the finite-element simulation environment COMSOL. Simulations include stress-strain loading for a variety of structure parameters such as gauge lengths, widths, and spacing, as well as multiple load locations relative to the gauge. Subsequently, sensors with optimized configurations obtained through simulations were fabricated using cleanroom photolithographic and spin-coating processes, and then experimentally tested. Results show a trend-wise agreement between experiments and simulations.
The performance of robots to carry out tasks depends in part on the sensor information they can utilize. Usually, robots are fitted with angle joint encoders that are used to estimate the position and orientation (or the pose) of its end-effector. However, there are numerous situations, such as in legged locomotion, mobile manipulation, or prosthetics, where such joint sensors may not be present at every, or any joint. In this paper we study the use of inertial sensors, in particular accelerometers, placed on the robot that can be used to estimate the robot pose. Studying accelerometer placement on a robot involves many parameters that affect the performance of the intended positioning task. Parameters such as the number of accelerometers, their size, geometric placement and Signal-to-Noise Ratio (SNR) are included in our study of their effects for robot pose estimation. Due to the ubiquitous availability of inexpensive accelerometers, we investigated pose estimation gains resulting from using increasingly large numbers of sensors. Monte-Carlo simulations are performed with a two-link robot arm to obtain the expected value of an estimation error metric for different accelerometer configurations, which are then compared for optimization. Results show that, with a fixed SNR model, the pose estimation error decreases with increasing number of accelerometers, whereas for a SNR model that scales inversely to the accelerometer footprint, the pose estimation error increases with the number of accelerometers. It is also shown that the optimal placement of the accelerometers depends on the method used for pose estimation. The findings suggest that an integration-based method favors placement of accelerometers at the extremities of the robot links, whereas a kinematic-constraints-based method favors a more uniformly distributed placement along the robot links.
In order to carry out nanomanufacturing tasks, a microrobot requires both high precision and high reliability over prolonged periods of time. Articulated Four-Axis Microrobots (AFAM) have been introduced a decade ago as millimetric microrobots capable of carrying out nanoscale tasks. The original robot design relied on a Micro Electro Mechanical (MEMS) actuator bank positioned onto a Silicon substrate, and an assembled arm mechanically coupled to the actuators through a cable. Movement of two thermal actuator banks positions the AFAM’s end effector in 3-Dimensional space with approximately 75 microns workspace and 50 nm repeatability. However, failure of the AFAM’s cable mechanism was observed after less than 1 million cycles. In this paper, we propose a novel arm mechanism for AFAM that improve its performance. The design presented in this article substitutes the "wire-gluing" cable with an anchored electrostatic actuator, and therefore it simplifies assembly requirements, reduces overall footprint of the microrobot, and achieves higher operating frequency. Simulation results are presented for a rotary electrostatic comb drive as basis for the microrobot arm with overall dimensions of 2 mm × 2 mm. The AFAM arm cantilever is 1 mm long to achieve a workspace of dimension of 75 microns along the vertical axis. Experimental evaluation of the design was accomplished using a prototype fabricated on a silicon on insulator (SOI) wafer processed with the deep reactive ion etching (DRIE) process.
Human environments are often unstructured and unpredictable, thus making the autonomous operation of robots in such environments is very difficult. Despite many remaining challenges in perception, learning, and manipulation, more and more studies involving assistive robots have been carried out in recent years. In hospital environments, and in particular in patient rooms, there are well-established practices with respect to the type of furniture, patient services, and schedule of interventions. As a result, adding a robot into semi-structured hospital environments is an easier problem to tackle, with results that could have positive benefits to the quality of patient care and the help that robots can offer to nursing staff. When working in a healthcare facility, robots need to interact with patients and nurses through Human-Machine Interfaces (HMIs) that are intuitive to use, they should maintain awareness of surroundings, and offer safety guarantees for humans. While fully autonomous operation for robots is not yet technically feasible, direct teleoperation control of the robot would also be extremely cumbersome, as it requires expert user skills, and levels of concentration not available to many patients.
Therefore, in our current study we present a traded control scheme, in which the robot and human both perform expert tasks. The human-robot communication and control scheme is realized through a mobile tablet app that can be customized for robot sitters in hospital environments. The role of the mobile app is to augment the verbal commands given to a robot through natural speech, camera and other native interfaces, while providing failure mode recovery options for users. Our app can access video feed and sensor data from robots, assist the user with decision making during pick and place operations, monitor the user health over time, and provides conversational dialogue during sitting sessions. In this paper, we present the software and hardware framework that enable a patient sitter HMI, and we include experimental results with a small number of users that demonstrate that the concept is sound and scalable.
A vital part of human interactions with a machine is the control interface, which single-handedly could define the user satisfaction and the efficiency of performing a task. This paper elaborates the implementation of an experimental setup to study an adaptive algorithm that can help the user better tele-operate the robot. The formulation of the adaptive interface and associate learning algorithms are general enough to apply when the mapping between the user controls and the robot actuators is complex and/or ambiguous. The method uses a genetic algorithm to find the optimal parameters that produce the input-output mapping for teleoperation control. In this paper, we describe the experimental setup and associated results that was used to validate the adaptive interface to a differential drive robot from two different input devices; a joystick, and a Myo gesture control armband. Results show that after the learning phase, the interface converges to an intuitive mapping that can help even inexperienced users drive the system to a goal location.
Fabricating cost effective, reliable and functional sensors for electronic skins has been a challenging undertaking for the last several decades. Application of such skins include haptic interfaces, robotic manipulation, and physical human-robot interaction. Much of our recent work has focused on producing compliant sensors that can be easily formed around objects to sense normal, tension, or shear forces. Our past designs have involved the use of flexible sensors and interconnects fabricated on Kapton substrates, and piezoresistive inks that are 3D printed using Electro Hydro Dynamic (EHD) jetting onto interdigitated electrode (IDE) structures. However, EHD print heads require a specialized nozzle and the application of a high-voltage electric field; for which, tuning process parameters can be difficult based on the choice of inks and substrates. Therefore, in this paper we explore sensor fabrication techniques using a novel wet lift-off photolithographic technique for patterning the base polymer piezoresistive material, specifically Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) or PEDOT:PSS. Fabricated sensors are electrically and thermally characterized, and temperaturecompensated designs are proposed and validated. Packaging techniques for sensors in polymer encapsulants are proposed and demonstrated to produce a tactile interface device for a robot.
Sensorized robot skin has considerable promise to enhance robots’ tactile perception of surrounding environments. For physical human-robot interaction (pHRI) or autonomous manipulation, a high spatial sensor density is required, typically driven by the skin location on the robot. In our previous study, a 4x4 flexible array of strain sensors were printed and packaged onto Kapton sheets and silicone encapsulants. In this paper, we are extending the surface area of the patch to larger arrays with up to 128 tactel elements. To address scalability, sensitivity, and calibration challenges, a novel electronic module, free of the traditional signal conditioning circuitry was created. The electronic design relies on a software-based calibration scheme using high-resolution analog-to-digital converters with internal programmable gain amplifiers. In this paper, we first show the efficacy of the proposed method with a 4x4 skin array using controlled pressure tests, and then perform procedures to evaluate each sensor’s characteristics such as dynamic force-to-strain property, repeatability, and signal-to-noise-ratio. In order to handle larger sensor surfaces, an automated force-controlled test cycle was carried out. Results demonstrate that our approach leads to reliable and efficient methods for extracting tactile models for use in future interaction with collaborative robots.