Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program Final report, 1 Feb. 1969 - 24 Aug. 1970

Research progress in technology transfer by NASA Biomedical Application Tea

Southwest Research Institute assistance to NASA in biomedical areas of the technology

Significant applications of aerospace technology were achieved. These applications include: a miniaturized, noninvasive system to telemeter electrocardiographic signals of heart transplant patients during their recuperative period as graded situations are introduced; and economical vital signs monitor for use in nursing homes and rehabilitation hospitals to indicate the onset of respiratory arrest; an implantable telemetry system to indicate the onset of the rejection phenomenon in animals undergoing cardiac transplants; an exceptionally accurate current proportional temperature controller for pollution studies; an automatic, atraumatic blood pressure measurement device; materials for protecting burned areas in contact with joint bender splints; a detector to signal the passage of animals by a given point during ecology studies; and special cushioning for use with below-knee amputees to protect the integrity of the skin at the stump/prosthesis interface

Advanced sensors technology survey

This project assesses the state-of-the-art in advanced or 'smart' sensors technology for NASA Life Sciences research applications with an emphasis on those sensors with potential applications on the space station freedom (SSF). The objectives are: (1) to conduct literature reviews on relevant advanced sensor technology; (2) to interview various scientists and engineers in industry, academia, and government who are knowledgeable on this topic; (3) to provide viewpoints and opinions regarding the potential applications of this technology on the SSF; and (4) to provide summary charts of relevant technologies and centers where these technologies are being developed

Design Experiences on Single and Multi Radio Systems in Wireless Embedded Platforms

The progress of radio technology has made several flavors of radio available on the market.Wireless sensor network platform designers have used these radios to build a variety of platforms. Withnew applications and different types of radios on wireless sensing nodes, it is often hard to interconnectdifferent types of networks. Hence, often additional radios have to be integrated onto existingplatforms or new platforms have to be built. Additionally, the energy consumption of these nodes have to be optimized to meetlifetime requirements of years without recharging.In this thesis, we address two issues of single and multi radio platform designfor wireless sensor network applications - engineering issues and energy optimization.We present a set of guiding principles from our design experiences while building 3 real life applications,namely asset tracking, burglar tracking and finally in-situ psychophysiological stress monitoring of human subjects in behavioral studies.In the asset tracking application, we present our design of a tag node that can be hidden inside valuable personal assets such asprinters or sofas in a home. If these items are stolen, a city wide anchor node infrastructure networkwould track them throughout the city. We also present our design for the anchor node.In the burglar tracking application, we present the design of tag nodes and the issueswe faced while integrating it with a GSM radio. Finally, we discuss our experiencesin designing a bridge node, that connects body worn physiological sensorsto a Bluetooth enabled mobile smartphone. We present the software framework that acts as middleware toconnect to the bridge, parse the sensor data, and send it to higher layers of the softwareframework.We describe 2 energy optimization schemes that are used in the Asset Tracking and the Burglar Tracking applications, that enhance the lifetime of the individual applications manifold.In the asset tracking application,we design a grouping scheme that helps increase reliability of detection of the tag nodes at theanchor nodes while reducing the energy consumption of the group of tag nodes travelling together.We achieve an increase of 5 times improvement in lifetime of the entire group. In the Burglar Tracking application, weuse sensing to determine when to turn the GSM radio on and transmit data by differentiatingturns and lane changes. This helps us reduce the number of times the GSM radio is woken up, thereby increasing thelifetime of the tag node while it is being tracked. This adds 8 minutes of trackablelifetime to the burglar tracking tag node. We conclude this thesis by observing the futuretrends of platform design and radio evolution

High-Performance Accelerometer Based On Asymmetric Gapped Cantilevers For Physiological Acoustic Sensing

Continuous or mobile monitoring of physiological sounds is expected to play important role in the emerging mobile healthcare field. Because of the miniature size, low cost, and easy installation, accelerometer is an excellent choice for continuous physiological acoustic signal monitoring. However, in order to capture the detailed information in the physiological signals for clinical diagnostic purpose, there are more demanding requirements on the sensitivity/noise performance of accelerometers. In this thesis, a unique piezoelectric accelerometer based on the asymmetric gapped cantilever which exhibits significantly improved sensitivity is extensively studied. A meso-scale prototype is developed for capturing the high quality cardio and respiratory sounds on healthy people as well as on heart failure patients. A cascaded gapped cantilever based accelerometer is also explored for low frequency vibration sensing applications such as ballistocardiogram monitoring. Finally, to address the power issues of wireless sensors such as wireless wearable health monitors, a wide band vibration energy harvester based on a folded gapped cantilever is developed and demonstrated on a ceiling air condition unit

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

Technology transfer: Transportation

The application of NASA derived technology in solving problems related to highways, railroads, and other rapid systems is described. Additional areas/are identified where space technology may be utilized to meet requirements related to waterways, law enforcement agencies, and the trucking and recreational vehicle industries

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Efficient information distribution in the Internet of Medical Things (IoMT)

Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods.Towards the world of Internet of Things, people utilize knowledge from sensor streams in various kinds of smart applications including, but not limited to smart medical information systems. The number of sensed devices is rapidly increasing along with the amount of sensing data. Consequently, the bottleneck problem at the local gateway has become a huge concern given the critical loss and delay intolerant nature of medical data. Orthogonally to the existing solutions, we propose sensor data prioritization mechanism to enhance the information quality while utilizing resources using Value of Information (VoI) at the application level. Our approach adopts signal processing techniques and information theory related concepts to assess the VoI. We introduce basic yet convenient ways to enhance the efficiency of medical information systems, not only when considering the resource consumption, but also when performing updates, by selecting appropriate delay for wearable sensors to send data at optimal VoI. Our analysis shows some interesting results about the correlation and dependency of different sensor signals, that we use for the value assesment. This preliminary analysis could be an initiative for further investigation of VoI in medical data transmission using more advanced methods

A machine learning framework for automatic human activity classification from wearable sensors

Wearable sensors are becoming increasingly common and they permit the capture of physiological data during exercise, recuperation and everyday activities. This work investigated and advanced the current state-of-the-art in machine learning technology for the automatic classification of captured physiological data from wearable sensors. The overall goal of the work presented here is to research and investigate every aspect of the technology and methods involved in this field and to create a framework of technology that can be utilised on low-cost platforms across a wide range of activities. Both rudimentary and advanced techniques were compared, including those that allowed for both real-time processing on an android platform and highly accurate postprocessing on a desktop computer. State-of-the-art feature extraction methods such as Fourier and Wavelet analysis were also researched to ascertain how well they could extract discriminative physiological information. Various classifiers were investigated in terms of their ability to work with different feature extraction methods. Consequently, complex classification fusion models were created to increase the overall accuracy of the activity recognition process. Genetic algorithms were also employed to optimise classifier parameter selection in the multidimensional search space. Large annotated sporting activity datasets were created for a range of sports that allowed different classification models to be compared. This allowed for a machine learning framework to be constructed that could potentially create accurate models when applied to any unknown dataset. This framework was also successfully applied to medical and everyday-activity datasets confirming that the approach could be deployed in different application settings