Intelligent Millimeter-Wave System for Human Activity Monitoring for Telemedicine.

Telemedicine has the potential to improve access and delivery of healthcare to diverse and aging populations. Recent advances in technology allow for remote monitoring of physiological measures such as heart rate, oxygen saturation, blood glucose, and blood pressure. However, the ability to accurately detect falls and monitor physical activity remotely without invading privacy or remembering to wear a costly device remains an ongoing concern. Our proposed system utilizes a millimeter-wave (mmwave) radar sensor (IWR6843ISK-ODS) connected to an NVIDIA Jetson Nano board for continuous monitoring of human activity. We developed a PointNet neural network for real-time human activity monitoring that can provide activity data reports, tracking maps, and fall alerts. Using radar helps to safeguard patients' privacy by abstaining from recording camera images. We evaluated our system for real-time operation and achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Our system would facilitate the ability to detect falls and monitor physical activity in home and institutional settings to improve telemedicine by providing objective data for more timely and targeted interventions. This work demonstrates the potential of artificial intelligence algorithms and mmwave sensors for HAR.

Low-Cost, Real-Time Polymerase Chain Reaction System with Integrated RNA Extraction.

Rapid, easy-to-use, and low-cost systems for biological sample testing are important for point-of-care diagnostics and various other health applications. The recent pandemic of Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) showed an urgent need to rapidly and accurately identify the genetic material of SARS-CoV-2, an enveloped ribonucleic acid (RNA) virus, in upper respiratory specimens from people. In general, sensitive testing methods require genetic material extraction from the specimen. Unfortunately, current commercially available extraction kits are expensive and involve time-consuming and laborious extraction procedures. To overcome the difficulties associated with common extraction methods, we propose a simple enzymatic assay for the nucleic acid extraction step using heat mediation to improve the polymerase chain reaction (PCR) reaction sensitivity. Our protocol was tested on Human Coronavirus 229E (HCoV-229E) as an example, which comes from the large coronaviridae family of viruses that affect birds, amphibians, and mammals, of which SARS-CoV-2 is a member. The proposed assay was performed using a low-cost, custom-made, real-time PCR system that incorporates thermal cycling and fluorescence detection. It had fully customizable reaction settings to allow versatile biological sample testing for various applications, including point-of-care medical diagnosis, food and water quality testing, and emergency health situations. Our results show that heat-mediated RNA extraction is a viable extraction method when compared to commercial extraction kits. Further, our study showed that extraction has a direct impact on purified laboratory samples of HCoV-229E, but no direct impact on infected human cells. This is clinically relevant, as it allows us to circumvent the extraction step on clinical samples when using PCR.

A Study on the Effect of Temperature Variations on FPGA-Based Multi-Channel Time-to-Digital Converters.

We describe a study on the effect of temperature variations on multi-channel time-to-digital converters (TDCs). The objective is to study the impact of ambient thermal variations on the performance of field-programmable gate array (FPGA)-based tapped delay line (TDL) TDC systems while simultaneously meeting the requirements of high-precision time measurement, low-cost implementation, small size, and low power consumption. For our study, we chose two devices, Artix-7 and ProASIC3L, manufactured by Xilinx and Microsemi, respectively. The radiation-tolerant ProASIC3L device offers better stability in terms of thermal sensitivity and power consumption compared to the Artix-7. To assess the performance of the TDCs under varying thermal conditions, a laboratory thermal chamber was utilized to maintain ambient temperatures ranging from -75 to 80 °C. This analysis ensured a comprehensive evaluation of the TDCs' performance across a wide operational range. By utilizing the Artix-7 and ProASIC3L devices, we achieved root mean square (RMS) resolution of 24.7 and 554.59 picoseconds, respectively. Total on-chip power of 0.968 W was achieved using Artix-7, while 1.997 mW of power consumption was achieved using the ProASIC3L device. We worked to determine the temperature sensitivity for both FPGA devices, which could help in the design and optimization of FPGA-based TDCs for many applications.

A Size, Weight, Power, and Cost-Efficient 32-Channel Time to Digital Converter Using a Novel Wave Union Method.

We present a Tapped Delay Line (TDL)-based Time to Digital Converter (TDC) using Wave Union type A (WU-A) architecture for applications that require high-precision time interval measurements with low size, weight, power, and cost (SWaP-C) requirements. The proposed TDC is implemented on a low-cost Field-Programmable Gate Array (FPGA), Artix-7, from Xilinx. Compared to prior works, our high-precision multi-channel TDC has the lowest SWaP-C requirements. We demonstrate an average time precision of less than 3 ps and a Root Mean Square resolution of about 1.81 ps. We propose a novel Wave Union type A architecture where only the first multiplexer is used to generate the wave union pulse train at the arrival of the start signal to minimize the required computational processing. In addition, an auto-calibration algorithm is proposed to help improve the TDC performance by improving the TDC Differential Non-Linearity and Integral Non-Linearity.

Experimental Evaluation of Sensor Fusion of Low-Cost UWB and IMU for Localization under Indoor Dynamic Testing Conditions.

Autonomous systems usually require accurate localization methods for them to navigate safely in indoor environments. Most localization methods are expensive and difficult to set up. In this work, we built a low-cost and portable indoor location tracking system by using Raspberry Pi 4 computer, ultra-wideband (UWB) sensors, and inertial measurement unit(s) (IMU). We also developed the data logging software and the Kalman filter (KF) sensor fusion algorithm to process the data from a low-power UWB transceiver (Decawave, model DWM1001) module and IMU device (Bosch, model BNO055). Autonomous systems move with different velocities and accelerations, which requires its localization performance to be evaluated under diverse motion conditions. We built a dynamic testing platform to generate not only the ground truth trajectory but also the ground truth acceleration and velocity. In this way, our tracking system's localization performance can be evaluated under dynamic testing conditions. The novel contributions in this work are a low-cost, low-power, tracking system hardware-software design, and an experimental setup to observe the tracking system's localization performance under different dynamic testing conditions. The testing platform has a 1 m translation length and 80 µm of bidirectional repeatability. The tracking system's localization performance was evaluated under dynamic conditions with eight different combinations of acceleration and velocity. The ground truth accelerations varied from 0.6 to 1.6 m/s2 and the ground truth velocities varied from 0.6 to 0.8 m/s. Our experimental results show that the location error can reach up to 50 cm under dynamic testing conditions when only relying on the UWB sensor, with the KF sensor fusion of UWB and IMU, the location error decreases to 13.7 cm.

Low-Cost, Real-Time Polymerase Chain Reaction System for Point-of-Care Medical Diagnosis.

Global health crises due to the prevailing Coronavirus Disease 2019 (COVID-19) pandemic have placed significant strain on health care facilities such as hospitals and clinics around the world. Further, foodborne and waterborne diseases are not only spreading faster, but also appear to be emerging more rapidly than ever before and are able to circumvent conventional control measures. The Polymerase Chain Reaction (PCR) system is a well-known diagnostic tool for many applications in medical diagnostics, environmental monitoring, and food and water quality assessment. Here, we describe the design, development, and testing of a portable, low-cost, and real-time PCR system that can be used in emergency health crises and resource-poor situations. The described PCR system incorporates real-time reaction monitoring using fluorescence as an alternative to gel electrophoresis for reaction analysis, further decreasing the need of multiple reagents, reducing sample testing cost, and reducing sample analysis time. The bill of materials cost of the described system is approximately $340. The described PCR system utilizes a novel progressive selective proportional-integral-derivative controller that helps in reducing sample analysis time. In addition, the system employs a novel primer-based approach to quantify the initial target amplicon concentration, making it well-suited for food and water quality assessment. The developed PCR system performed DNA amplification at a level and speed comparable to larger and more expensive commercial table-top systems. The fluorescence detection sensitivity was also tested to be at the same level as commercially available multi-mode optical readers, thus making the PCR system an attractive solution for medical point-of-care and food and water quality assessment.

Towards a Low-Cost Solution for Gait Analysis Using Millimeter Wave Sensor and Machine Learning.

Human Activity Recognition (HAR) that includes gait analysis may be useful for various rehabilitation and telemonitoring applications. Current gait analysis methods, such as wearables or cameras, have privacy and operational constraints, especially when used with older adults. Millimeter-Wave (MMW) radar is a promising solution for gait applications because of its low-cost, better privacy, and resilience to ambient light and climate conditions. This paper presents a novel human gait analysis method that combines the micro-Doppler spectrogram and skeletal pose estimation using MMW radar for HAR. In our approach, we used the Texas Instruments IWR6843ISK-ODS MMW radar to obtain the micro-Doppler spectrogram and point clouds for 19 human joints. We developed a multilayer Convolutional Neural Network (CNN) to recognize and classify five different gait patterns with an accuracy of 95.7 to 98.8% using MMW radar data. During training of the CNN algorithm, we used the extracted 3D coordinates of 25 joints using the Kinect V2 sensor and compared them with the point clouds data to improve the estimation. Finally, we performed a real-time simulation to observe the point cloud behavior for different activities and validated our system against the ground truth values. The proposed method demonstrates the ability to distinguish between different human activities to obtain clinically relevant gait information.

Printed Textile-Based Ag2O-Zn Battery for Body Conformal Wearable Sensors.

Wearable electronics are playing an important role in the health care industry. Wearable sensors are either directly attached to the body surface or embedded into worn garments. Textile-based batteries can help towards development of body conformal wearable sensors. In this letter, we demonstrate a 2D planar textile-based primary Ag2O-Zn battery fabricated using the stencil printing method. A synthetic polyester woven fabric is used as the textile substrate and polyethylene oxide material is used as the separator. The demonstrated battery achieves an areal capacity of 0.6 mAh/cm2 with an active electrode area of 0.5 cm × 1 cm.

Differential wide temperature range CMOS interface circuit for capacitive MEMS pressure sensors.

We describe a Complementary Metal-Oxide Semiconductor (CMOS) differential interface circuit for capacitive Micro-Electro-Mechanical Systems (MEMS) pressure sensors that is functional over a wide temperature range between -55 °C and 225 °C. The circuit is implemented using IBM 0.13 µm CMOS technology with 2.5 V power supply. A constant-gm biasing technique is used to mitigate performance degradation at high temperatures. The circuit offers the flexibility to interface with MEMS sensors with a wide range of the steady-state capacitance values from 0.5 pF to 10 pF. Simulation results show that the circuitry has excellent linearity and stability over the wide temperature range. Experimental results confirm that the temperature effects on the circuitry are small, with an overall linearity error around 2%.

A wafer level vacuum encapsulated capacitive accelerometer fabricated in an unmodified commercial MEMS process.

We present the design and fabrication of a single axis low noise accelerometer in an unmodified commercial MicroElectroMechanical Systems (MEMS) process. The new microfabrication process, MEMS Integrated Design for Inertial Sensors (MIDIS), introduced by Teledyne DALSA Inc. allows wafer level vacuum encapsulation at 10 milliTorr which provides a high Quality factor and reduces noise interference on the MEMS sensor devices. The MIDIS process is based on high aspect ratio bulk micromachining of single-crystal silicon layer that is vacuum encapsulated between two other silicon handle wafers. The process includes sealed Through Silicon Vias (TSVs) for compact design and flip-chip integration with signal processing circuits. The proposed accelerometer design is sensitive to single-axis in-plane acceleration and uses a differential capacitance measurement. Over ±1 g measurement range, the measured sensitivity was 1 fF/g. The accelerometer system was designed to provide a detection resolution of 33 milli-g over the operational range of ±100 g.

Creating diversified response profiles from a single quenchometric sensor element by using phase-resolved luminescence.

We report a new strategy for generating a continuum of response profiles from a single luminescence-based sensor element by using phase-resolved detection. This strategy yields reliable responses that depend in a predictable manner on changes in the luminescent reporter lifetime in the presence of the target analyte, the excitation modulation frequency, and the detector (lock-in amplifier) phase angle. In the traditional steady-state mode, the sensor that we evaluate exhibits a linear, positive going response to changes in the target analyte concentration. Under phase-resolved conditions the analyte-dependent response profiles: (i) can become highly non-linear; (ii) yield negative going responses; (iii) can be biphasic; and (iv) can exhibit super sensitivity (e.g., sensitivities up to 300 fold greater in comparison to steady-state conditions).

Demonstration of a plasmonic thermocycler for the amplification of human androgen receptor DNA.

A plasmonic heating method for the polymerase chain reaction is demonstrated by the amplification of a section of the human androgen receptor gene. The thermocycler has a simple low-cost design, demonstrates excellent temperature stability and represents the first practical demonstration of plasmonic thermocycling.

Contact CMOS imaging of gaseous oxygen sensor array.

We describe a compact luminescent gaseous oxygen (O2) sensor microsystem based on the direct integration of sensor elements with a polymeric optical filter and placed on a low power complementary metal-oxide semiconductor (CMOS) imager integrated circuit (IC). The sensor operates on the measurement of excited-state emission intensity of O2-sensitive luminophore molecules tris(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) ([Ru(dpp)3]2+) encapsulated within sol-gel derived xerogel thin films. The polymeric optical filter is made with polydimethylsiloxane (PDMS) that is mixed with a dye (Sudan-II). The PDMS membrane surface is molded to incorporate arrays of trapezoidal microstructures that serve to focus the optical sensor signals on to the imager pixels. The molded PDMS membrane is then attached with the PDMS color filter. The xerogel sensor arrays are contact printed on top of the PDMS trapezoidal lens-like microstructures. The CMOS imager uses a 32 × 32 (1024 elements) array of active pixel sensors and each pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. Correlated double sampling circuit, pixel address, digital control and signal integration circuits are also implemented on-chip. The CMOS imager data is read out as a serial coded signal. The CMOS imager consumes a static power of 320 µW and an average dynamic power of 625 µW when operating at 100 Hz sampling frequency and 1.8 V DC. This CMOS sensor system provides a useful platform for the development of miniaturized optical chemical gas sensors.

Handheld impedance biosensor system using engineered proteinaceous receptors.

We put forward an impedometric protein-based biosensor platform suitable for point-of-care diagnostics. A hand-held scale impedance reader system is described for the detection of corresponding physiochemical changes as the immobilized proteins bind to the analyte molecules in the proximity of the microfabricated electrodes. Specifically, we study the viability of this approach for glucose biosensing purposes using genetically engineered glucokinase as receptor proteins. The proposed reagent-less biosensor offers a high sensitivity of 0.5 mM glucose concentration level in the physiologically relevant range of 0.5 mM to 7.5 mM with less than 10 s response time.

CMOS Imaging of Pin-Printed Xerogel-Based Luminescent Sensor Microarrays.

We present the design and implementation of a luminescence-based miniaturized multisensor system using pin-printed xerogel materials which act as host media for chemical recognition elements. We developed a CMOS imager integrated circuit (IC) to image the luminescence response of the xerogel-based sensor array. The imager IC uses a 26 × 20 (520 elements) array of active pixel sensors and each active pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. The imager includes a correlated double sampling circuit and pixel address/digital control circuit; the image data is read-out as coded serial signal. The sensor system uses a light-emitting diode (LED) to excite the target analyte responsive luminophores doped within discrete xerogel-based sensor elements. As a prototype, we developed a 4 × 4 (16 elements) array of oxygen (O2) sensors. Each group of 4 sensor elements in the array (arranged in a row) is designed to provide a different and specific sensitivity to the target gaseous O2 concentration. This property of multiple sensitivities is achieved by using a strategic mix of two oxygen sensitive luminophores ([Ru(dpp)3]2+ and ([Ru(bpy)3]2+) in each pin-printed xerogel sensor element. The CMOS imager consumes an average power of 8 mW operating at 1 kHz sampling frequency driven at 5 V. The developed prototype system demonstrates a low cost and miniaturized luminescence multisensor system.

NeuroMEMS: Neural Probe Microtechnologies.

Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer's, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

A Polypyrrole-based Strain Sensor Dedicated to Measure Bladder Volume in Patients with Urinary Dysfunction.

This paper describes a new technique to measure urine volume in patients with urinary bladder dysfunction. Polypyrrole - an electronically conducting polymer - is chemically deposited on a highly elastic fabric. This fabric, when placed around a phantom bladder, produced a reproducible change in electrical resistance on stretching. The resistance response to stretching is linear in 20%-40% strain variation. This change in resistance is influenced by chemical fabrication conditions. We also demonstrate the dynamic mechanical testing of the patterned polypyrrole on fabric in order to show the feasibility of passive interrogation of the strain sensor for biomedical sensing applications.

Direct-Dispense Polymeric Waveguides Platform for Optical Chemical Sensors.

We describe an automated robotic technique called direct-dispense to fabricate a polymeric platform that supports optical sensor arrays. Direct-dispense, which is a type of the emerging direct-write microfabrication techniques, uses fugitive organic inks in combination with cross-linkable polymers to create microfluidic channels and other microstructures. Specifically, we describe an application of direct-dispensing to develop optical biochemical sensors by fabricating planar ridge waveguides that support sol-gelderived xerogel-based thin films. The xerogel-based sensor materials act as host media to house luminophore biochemical recognition elements. As a prototype implementation, we demonstrate gaseous oxygen (O2) responsive optical sensors that operate on the basis of monitoring luminescence intensity signals. The optical sensor employs a Light Emitting Diode (LED) excitation source and a standard silicon photodiode as the detector. The sensor operates over the full scale (0%-100%) of O2 concentrations with a response time of less than 1 second. This work has implications for the development of miniaturized multisensor platforms that can be cost-effectively and reliably mass-produced.

Algal fluorescence sensor integrated into a microfluidic chip for water pollutant detection.

We report the first miniaturized fluorescent sensor based on algae, with an organic light emitting diode (OLED) and an organic photodetector (OPD) integrated into a microfluidic chip. The blue emission OLED was used as the excitation source, while a blend of PTB3/PC(61)BM was used for the fabrication of the organic photodetector. Excitation and emission color filters based on acid/base dyes and a metal complex were developed and assembled with the organic optoelectronic components in order to complete the fluorescent detection system. The detection system was then integrated in a microfluidic chip made from (poly)dimethylsiloxane (PDMS). The complete sensor is designed to detect algal fluorescence in the microfluidic chamber. Algal chlorophyll fluorescence enables evaluation of the toxicity of pollutants like herbicides and metals-ions from agricultural run-offs. The entirely organic bioassay here presented allowed detection of the toxic effects of the herbicide Diuron on Chlamydomonas reinhardtii green algae that gave 50% inhibition of the algae photochemistry (EC(50)) with a concentration as low as 11 nM.

CMOS Imaging of Temperature Effects on Pin-Printed Xerogel Sensor Microarrays.

In this paper, we study the effect of temperature on the operation and performance of a xerogel-based sensor microarrays coupled to a complementary metal-oxide semiconductor (CMOS) imager integrated circuit (IC) that images the photoluminescence response from the sensor microarray. The CMOS imager uses a 32 × 32 (1024 elements) array of active pixel sensors and each pixel includes a high-gain phototransistor to convert the detected optical signals into electrical currents. A correlated double sampling circuit and pixel address/digital control/signal integration circuit are also implemented on-chip. The CMOS imager data are read out as a serial coded signal. The sensor system uses a light-emitting diode to excite target analyte responsive organometallic luminophores doped within discrete xerogel-based sensor elements. As a proto type, we developed a 3 × 3 (9 elements) array of oxygen (O2) sensors. Each group of three sensor elements in the array (arranged in a column) is designed to provide a different and specific sensitivity to the target gaseous O2 concentration. This property of multiple sensitivities is achieved by using a mix of two O2 sensitive luminophores in each pin-printed xerogel sensor element. The CMOS imager is designed to be low noise and consumes a static power of 320.4 µW and an average dynamic power of 624.6 µW when operating at 100-Hz sampling frequency and 1.8-V dc power supply.