The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

The evolution of ubiquitous sensing technologies has led to intelligent environments that can monitor and react to our daily activities, such as adapting our heating and cooling systems, responding to our gestures, and monitoring our elderly. In this paper, we ask whether it is possible for smart environments to monitor our vital signs remotely, without instrumenting our bodies. We introduce Vital-Radio, a wireless sensing technology that monitors breathing and heart rate without body contact. Vital-Radio exploits the fact that wireless signals are affected by motion in the environment, including chest movements due to inhaling and exhaling and skin vibrations due to heartbeats. We describe the operation of Vital-Radio and demonstrate through a user study that it can track users' breathing and heart rates with a median accuracy of 99%, even when users are 8~meters away from the device, or in a different room. Furthermore, it can monitor the vital signs of multiple people simultaneously. We envision that Vital-Radio can enable smart homes that monitor people's vital signs without body instrumentation, and actively contribute to their inhabitants' well-being.

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Smart Homes that Monitor Breathing and Heart Rate

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Photoplethysmography and its application in clinical physiological measurement

Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image or a sequence of images. The extracted information can be used with or without a database in many applications, such as electronic payment systems (toll payment, parking fee payment), and freeway and arterial monitoring systems for traffic surveillance. The ALPR uses either a color, black and white, or infrared camera to take images. The quality of the acquired images is a major factor in the success of the ALPR. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as indoors, outdoors, day or night time. It should also be generalized to process license plates from different nations, provinces, or states. These plates usually contain different colors, are written in different languages, and use different fonts; some plates may have a single color background and others have background images. The license plates can be partially occluded by dirt, lighting, and towing accessories on the car. In this paper, we present a comprehensive review of the state-of-the-art techniques for ALPR. We categorize different ALPR techniques according to the features they used for each stage, and compare them in terms of pros, cons, recognition accuracy, and processing speed. Future forecasts of ALPR are given at the end.

https://romisatriawahono.net/lecture/rm/survey/computer%20vision/Du%20-%20Automatic%20License%20Plate%20Recognition%20-%202013.pdf

Automatic License Plate Recognition (ALPR): A State-of-the-Art Review

Together with light and soil, air and water quality control are fundamental to the productivity and quality of greenhouse grown products. But control implies monitoring, i.e. measuring the physical, chemical and biology parameters used to access quality.

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Greenhouse Environment: Air and Water Monitoring

The design and implementation of a microcontroller based platform compatible with IEEE 1451.4 standard for high performance unobtrusive accurate cardio-respiratory and motor activity estimation of wheelchair users are presented in this paper. The platform is a multi-sensor architecture that includes sensors with plug-and-play and auto-identification capabilities, a RFID reader that is used to identify the wheelchair user, and RFID tags mounted in critical regions of the indoor habitat. The acquired data from the smart sensor channels are delivered through Bluetooth communication or wired serial communication to a host computer that includes a server application associated with IEEE 1451.4 TEDS management and display of the measured data. Elements of human-machine-interface (HMI) implementation and several experimental results are included in the paper.

IEEE 1451.4 embedded smart sensors architecture for wheelchair user monitoring

Remote sensing and Virtual Reality (VR) are technologies that create new development opportunities in the field of serious games with application in physiotherapy. Thus, during a physiotherapy training session expressed by a game round the remote sensing of user body motion provides measurements that can be used for objective evaluation of physical therapy outcomes. In this work is presented a serious game for physiotherapy characterized by Kinect natural user interface and a set of VR games developed in the Unity3D. To provide patient electronic health record, game remote configuration as well as for data presentation for physiotherapist a mobile application was developed. Additionally, several training results expressed by upper limb, neck and spine angles are included in the paper.

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Tailored Virtual Reality for Smart Physiotherapy

This work reports a electrocardiograph and skin conductivity hardware architecture, based on E-textile electrodes, attached to a wheelchair for affective and physiological computing. Appropriate conditioning circuits and a microcontroller platform that performs acquisition, primary processing, and communication using Bluetooth were designed and implemented. To increase the accuracy and repeatability of the skin conductivity measuring channel, force measurement sensors were attached to the system certifying measuring contact force on the electrode level. Advanced processing including R-wave peak detector, adaptive filtering and autonomic nervous system analysis based on wavelets transform was designed and implemented on a server. A central design of affective recognition and biofeedback system is described.

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Treat me well: Affective and physiological feedback for wheelchair users

The article summarizes ongoing research on intelligent Tele-rehabilitation Assistants for in home rehabilitation of post stroke patients The designed system is expressed by a set of smart sensors including a microwave FMCW Doppler radar that are connected to an embedded computing platform based on digital signal processor (DSP) This platform assures advanced data processing and wireless data transmission to a human machine interface (HMI) Android OS compatible device expressed by a tablet or by a smart phone as part of a health care intelligent ambient The information from the health status and motor activity smart sensors is added to the patient localization given by a radar RFID device Additionally, well-established neuron-rehabilitation instruments are used to evaluate the results obtained by the proposed architecture implemented in a ubiquitous and pervasive manner

Pedro Silva Girao

Papers

Greenhouse Environment: Air and Water Monitoring

IEEE 1451.4 embedded smart sensors architecture for wheelchair user monitoring

Tailored Virtual Reality for Smart Physiotherapy

Treat me well: Affective and physiological feedback for wheelchair users

Rehabilitative TeleHealthCare for post-stroke outcome assessment