Bluetooth Low Energy (BLE) has reduced the energy consumption for sensor nodes drastically. One major reason for this improvement is a non-continuous connection between the nodes. But this causes also a nondeterministic data transmission time. Most synchronization protocols are influenced by this characteristic, with the result of less accuracy. In wireless body sensor networks this accuracy is often of vital importance. Therefore this paper evaluates different synchronization principles customized for BLE.For the evaluation measurements we used two BLE modules connected to one micro controller. This setup allowed us to calculate the error directly for the different principles. First we measured the send-receive time as a reference which influences most synchronization protocols. This time is directly affected by random transmission delays of BLE. Second we used the time difference between receiving and acknowledging a message as principle (A). The last principle (B) can only be used between nodes that use BLE that don’t require a constant connection, because it needs to connect and disconnect the nodes. After a new connection the “connected” events occur in the BLE nodes almost at the same time and can be used for synchronization. The reference measurement showed the worst results. The average delay was 4.76 ms with a standard deviation of 2.32 ms. Principle (A) showed average delays of 7.51 ms, which was almost exactly 1 connection interval in our setup. The standard deviation was 0.41 ms. Principle (B) showed the best results with an average time difference of 39.92 μs and a standard deviation of 14.19 μsThe results showed that with the principles (A) and (B) the synchronization of nodes can be highly improved compared to the reference. In future we will test the principles with synchronization protocols in real sensor nodes also with respect to the processor load.
Pulse oximetry is a standard parameter for many years in clinical patient monitoring. There are also sensors that can be used at home. But all these sensors use the transmission based method to measure the oxygen saturation which require finger or ear clips that are uncomfortable and confining and therefore not fit for long-term monitoring. Sleep-related breathing disorders or breathing thin air in high altitudes can lead to insufficient oxygen intake. Insufficient oxygen supply can cause permanent damage to the tissue and in some cases even death. For early ambulatory diagnosis, a reflection based longterm pulse oximetry sensor would be the best solution. There are no available devices on the market, even so many studies showed promising results. That why, we evaluated design parameters for a reflection based long-term pulse oximetry sensor, located at the wrist. The prototype we developed consists of two LEDs (one red and one infrared), a photodiode and the evaluation board SLAU480B from Texas Instruments. We tested three sensor layouts and discovered that the distance between the LEDs and the photodiode, the contact pressure and motion artefacts were the most important signal influencing parameters. After the Signal processing we obtained a signal to noise ratio of 22 dB (red) and 30 dB (infrared) and a AC/DC ratio of 1-3 %, which should be more than enough to calculate the SpO 2 value. Also motion artifacts were tested and seem to affect the measurement at the lower wrist strongly. In conclusion we found some reasons why there is no such device on the market yet.
Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.
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