It could also replace the cumbersome combination of ECG sensors and wires attached to patients while they walk on treadmills. Vijay Varadan, distinguished professor, electrical engineering College of Engineering , vjvesm uark. Matt McGowan, science and research communications officer University Relations , dmcgowa uark. Alumni Thomas Kidd and Jon Melia are working as athletic trainers for the Arkansas and Kentucky football teams, respectively, and the two teams will compete this coming weekend.
Mary Purvis has been promoted to senior director of development, and Ryan Peters has been promoted to assistant director of development in the Fay Jones School of Architecture and Design. Tuesday, Oct. Attire is business casual. Contacts Vijay Varadan, distinguished professor, electrical engineering College of Engineering , vjvesm uark. Staff Appreciation Week is Oct. More Stories. Submit News. Newswire Daily. Campus Experts. The difference between both of them is that compressible capacitive strain sensors are composed of elastic dielectric, while the piezoresistive sensors are composed of robust dielectric placed between 2 flexible electrodes.
When an external pressure is applied to the dielectric, it will lead to change in the capacitance of the device. In the same way, if the piezoelectric material is strained, this will generate an induced voltage in the device. For example, in [ 85 ] a conformable lead zirconate titanate sensors are presented, which have piezoelectric response. It is reported that these sensors have 0. Such kind of performance ensures that the sensor can be used for blood pressure measurements.
Another approach that can be used for blood pressure measurements is the RFID radio-frequency identification technique, but such device require implantation under skin, such as presented in [ 86 ]. Oxygenation is the oxygen saturated hemoglobin compared to total hemoglobin in the blood, which is saturated and unsaturated. The oxygenation may be separated into three groups: tissue, venous and peripheral oxygenation. The measurement technique is non-invasive in fresh pulsatile arterial blood.
The most common method for measurements is using optic-based device, such as a pulse oximeter. The working principle is based on generated light by light emitting diodes through parts of the body as earlobe, forehead, wrist, fingertips, etc.
Nowadays with the advances in organic electronics, the production of OLED organic light emitting diode and organic photo-detectors became prime devices for use in pulse oxygenation measurement due to their comfort in use [ 77 ]. Such sensors are described in details in [ 87 ].
The measurements for blood glucose involve the glucose amount in human blood which concentration is usually lower in the morning and increases after every meal. If the blood glucose is out of its normal range, this may indicate health problems as hyperglycemia low levels or diabetes high levels. In recent years, the number of people with diabetes has increased. It has been found that frequent possibly continuous measurement of blood glucose levels is essential for conducting insulin therapy and minimizing the harmful effects on the body.
Modern methods of testing include periodic tests in specialized laboratories or analysis of daily profiles periodically over several hours , using a portable blood analyzer at home.
Manual Mobile Wearable Nano-Bio Health Monitoring Systems with Smartphones as Base Stations
For this purpose, after a pinch, usually on the fingertip, a certain amount drop of blood is delivered to a special test strip which is placed in the analyzer and within a few seconds the current blood glucose level is indicated. These persistent pricks cause discomfort, especially in young children, and rarely can lead to infections. New developments in the art are directed to alternative methods for measuring glucose concentration, e.
Saliva nano-bio-sensor is presented there for noninvasive glucose monitoring which provide low-cost, accurate and disposable bio-sensor. Another method for non-invasive method is proposed in [ 89 ]. The described methods are still not applicable in mass practice. Another part of the research is directed to the development of invasive methods for the delivery and analysis of blood micro-bleeds.
At this stage, there are no data on the implementation and applications in the mass practice of nanobiobs for determining blood glucose levels by analyzing blood micro beats in the absence of pain sensations for the patient.
Research of human activity is becoming a most popular and relevant topic for multiple scientific areas. Human activity recognition includes mobile computing [ 90 ], surveillance-based security [ 91 ], context-aware computing [ 92 ] and ambient assistive living [ 93 ]. The sensor technologies and data processing techniques have achieved much progress.
Work on these supporting technologies has led to developments in the area of data collection and transfer and information integration. Many of the solutions to real problems related to human life are increasingly dependent on the human activity recognition. Recognizing human activity as a topic of work can contribute to many important activities related to security and monitoring, preservation of the environment, help in maintaining independent living and aging, etc.
To develop such a system, it is crucial to work on four main tasks. There is a variety of tools, methods and technologies available to implement each task. Sensor-based activity is used for activity monitoring. The approaches involve computer surveillance, structural modelling, characteristic elements extraction, action extraction and movement tracking with the main purpose being to make analysis aiming to recognize certain pattern based on collected visual information.
Another category is based on the application of recently developed sensor network technologies for activity monitoring [ 94 ]. Sensors are attached to the monitored person. This approach is applicable in order to follow physical movements such as workouts.
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There are multiple types of sensors available for activity monitoring contact sensors, accelerometers, audio and motion detectors etc. The sensors are divided according to their purpose — there are different types based on particular output signals, involving theoretical principles and defined by technical infrastructure. They are represented within two basic categories according to the way they are positioned during the activity monitoring process. Activity monitoring based on Wearable sensor.
This type of sensor is attached directly or indirectly to the observed person. While the monitored object performs any type of action, the sensors generate signals. In this way we are able to monitor features which describe the human state of mind and respective motion patterns. The sensors can be put into clothing, in shoe soles or heels, inside cell phones, watches and other mobile devices etc. They can be located directly on the body as well. From them we get the necessary indicators about the position and movement of the test object at a given moment, the pulse, temperature, and so on.
There are different types of relevant sensor information applicable for various types of activities. Accelerometer sensors are sensors for activity monitoring. They are used to monitor actions such as body movements such as walking, running, jumping and more.
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In a paper [ 95 ] a network of three-axis accelerometers has been reported. In [ 96 ] are used body worn microphones and accelerometers to measure acceleration and angular velocity through accelerometers and gyroscopes. With this method, the behavior of sitting, standing and walking can be recognized. Another used wearable sensor are GPS sensors.
These types of sensors are mostly applied when monitoring activities involving location changes or open air and mobile environments [ 98 ]. This task is performed through data analysis techniques.
Creating computing models so that software systems generate reasoning and manipulation. Depending on the type of sensor, there are two categories of activity recognition sensors. The provided data can be a series of video or digitally presented visual image. Common are computer vision techniques for action extraction, feature extraction, structural modelling, motion detection, and motion tracking to specify pattern recognition. The second one is based on sensor-based activity recognition using the newly developed sensor network systems for motion monitoring.
The acquired sensor data is presented as time series of state changes. They can be used as parameters for data integration, probabilistic or statistical analysis. This method is used for registering motion. The use of multiple multi-modal miniature sensors enables a robust capture of activities to be accomplished by monitoring interactions between a person and an object. The activity information can be acquired through motion monitoring models.
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For this purpose, they can be used data mining and machine learning techniques through creation of statistical activity models. This method is based on data. The actions that follow are based on probabilistic or statistical classification. This approach has its advantages as handling uncertainty and timing information. On the other hand, requires large datasets for training and learning, and also suffers from the problems of scalability and re-usability. Another method involves the use of predefined models with a large database and research results directly using knowledge engineering and management technologies.
The models in this method are used for activity recognition or prediction through logical reasoning. Knowledge-driven approaches are semantically clear and easy to get started. The drawback of this method comes from handling uncertainty and temporal information.
The field of vision-based activity recognition is focused on surveillance, improvement of robots and counter-terrorism, and this field includes a wide variety of options. Human body structure extraction data from images, action recognition and tracking across frames [ 99 ], survey on the approaches based on the movement recognition as opposed to structured approaches [ ], research focused on monitoring human movement using 2D or 3D models and the other recognition techniques [ ] etc.
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