Mairo Leier

The Benefits of Self-Awareness and Attention in Fog and Mist Computing

Self-awareness facilitates a proper assessment of cost-constrained cyber-physical systems, allocating limited resources where they are most needed. Together, situation awareness and attention are key enablers for self-awareness in efficient distributed sensing and computing networks.

Respiration signal extraction from photoplethysmogram using pulse wave amplitude variation

Respiratory information is usually measured directly with chest and abdominal belt or from the nasal airflow. There are several methods to extract respiration also from the electrocardiogram (ECG) and photoplethysmogram (PPG). In this paper we propose a methodology that detects the amplitude changes in the PPG signal to estimate the respiration rate. During exhalation, our parasympathetic nervous system makes the blood vessels more flexible than during inhalation. Blood vessels flexibility affects the propagation velocity of the pulse wave. In that way respiration also modulates the amplitude of the pulse wave signal. Comparing with other respiration signal extraction techniques our method has excellent results with limited processing power. The long-term objective of this work is to use the respiration signal together with heart rate and blood oxygen saturation level (SpO2), that are extracted from the pulse wave, for sleep apnea detection and screening purposes.

Sleep apnea pre-screening on neonates and children with shoe integrated sensors

The aim of this paper is to provide a high level architectural description of portable sleep apnea screening system. Proposed patient mobile monitoring solution increases their mobility, flexibility and measures vital data with minimal patient disturbance with increased reliability. Data is acquired with synchronous dual multi-wavelength optical sensors, placed on the foot. Movement artefacts are reduced with the correlation of accelerometer readings. Results are sent over the wireless link to the smartphone for further diagnosis which gives also feedback to the end user. Because of the limitation of the measuring device dimensions and computational power some of the signal processing is done on the smartphone. Combining different latest technology achievements makes our proposed solution suitable for neonates and children for sleep apnea pre-screening.

Activity classification for real-time wearable systems: Effect of window length, sampling frequency and number of features on classifier performance

The aim of this study was to evaluate how the physical activity classification window length, accelerometer sampling frequency and the number of correlating features affect the classifier performance. It is important to study the effect of these elements in order to reduce the computational power and memory buffers needed for wearable systems, where classification is done in real-time. Three different window lengths (5 s, 3 s, 1s), sampling frequencies (50 Hz, 25 Hz, 13 Hz) and two feature sets (110 and 43 features) were tested and evaluated in this study. As a result, it was found that the classifier performed similarly with the window lengths of 5 s and 3 s and the sampling rates of 50 Hz and 25 Hz, but the results with the window length of 1 s or the sampling rate of 13 Hz were lower. No noticeable difference in classifier sensitivity was found by decreasing the number of features based on correlation.

Motivation-driven learning processes at the example of embedded systems

This paper addresses motivation-driven learning processes, applied to the field of education in computer engineering. Advantages and drawbacks of classical university teaching approaches are analyzed and evaluated. Based on literature and own experiences a new model for motivation-based learning is derived and presented and illustrated along with a recently developed lecture for Embedded Systems Design. Finally conclusions for concrete future improvements of this course in accordance with the model will be presented.

Classification Algorithm Improvement for Physical Activity Recognition in Maritime Environments

Human activity recognition using wearable sensors and classification methods provides valuable information for the assessment of user’s physical activity levels and for the development of more precise energy expenditure models, which can be used to proactively prevent cardiovascular diseases and obesity. The aim of this study was to evaluate how maritime environment and sea waves affect the performance of modern physical activity recognition methods, which has not yet been investigated. Two similar test suits were conducted on land and on a small yacht where subjects performed various activities, which were grouped into five different activity types of static, transitions, walking, running and jumping. Average activity type classification sensitivity with a decision tree classifier trained using land-based signals from one tri-axial accelerometer placed on lower back and leave-one-subject-out cross-validation scheme was 0.95 ± 0.01 while classifying the activities performed on land, but decreased to 0.81 ± 0.17 while classifying the activities on sea. An additional component produced by sea waves with a frequency of 0.3–0.8 Hz and a peak-to-peak amplitude of 2 m/s2 was noted in sea-based signals. Additional filtration methods were developed with the aim to remove the effect of sea waves using the least amount of computational power in order to create a suitable solution for real-time activity classification. The results of this study can be used to develop more precise physical activity classification methods in maritime areas or other locations where background affects the accelerometer signals.

Development case study of the first estonian self-driving car, iseauto

Abstract The rapid development of intelligent control technology has also brought about changes in the automotive industry and led to development of autonomous or self-driving vehicles. To overcome traffic and environment issues, self-driving cars use a number of sensors for vision as well as a navigation system and actuators to control mechanical systems and computers to process the data. All these points make a self-driving car an interdisciplinary project that requires contribution from different fields. In our particular case, four different university departments and two companies are directly involved in the self-driving car project. The main aim of the paper is to discuss the challenges faced in the development of the first Estonian self-driving car. The project implementation time was 20 months and the project included four work packages: preliminary study, software development, body assembly and system tuning/testing of the self-driving car. This paper describes the development process stages and tasks that were distributed between the sub-teams. Moreover, the paper presents the technical and software solutions that were used to achieve the goal and presents a self-driving last mile bus called ISEAUTO. Special attention is paid to the discussion of safety challenges that a self-driving electrical car project can encounter. The main outcomes and future research possibilities are outlined

Pulse wave registration from radial artery using photoplethysmographic method.

This pilot study was aimed to investigate the possibilities to use the photoplethysmographic (PPG) method for the pulse wave registration from radial artery. The PPG sensor with different separation distances between light emitting diodes (LED) and photodiode was built. The PPG signal registration was carried out at the locations with two different depths of artery and at the locations without large blood vessels under the sensor. In addition, two different forces were applied on the sensor in order to decrease the blood volume in underlying tissue and lower the pulsations that originates from smaller vessels. As a result, it was found that the artery was possible to locate under the sensor, where the value of DC component is minimal. Furthermore, the pressure has to be applied on sensor and optimal separation distance has to be selected between LED and photodiode for the pulse wave registration from radial artery. Further studies and improvements of the sensor are needed.

Smart photoplethysmographic sensor for pulse wave registration at different vascular depths.

The aim of this paper is to propose a smart optical sensor for cardiovascular activity monitoring at different tissue layers. Photoplethysmography (PPG) is a noninvasive optical technique for monitoring mainly blood volume changes in the examined tissue. However, different important physiological parameters, such as oxygen saturation, heart and breathing rate, dynamics of skin micro-circulation, vasomotion activity etc., can be extracted from the registered PPG signal. The developed sensor consists of 32 light emitting sources with four different wavelengths, which are located to the four different distances from four photo detectors. Compared to the existing sensors, the system enables to select the optimal LED (light emitting diode) and photo detector couple in order to obtain the pulse wave signal from the interested blood vessels with the highest possible signal to noise ratio. In this study, the designed PPG sensor was tested for the pulse wave registration from radial artery. The highest efficiency and signal to noise ratio was achieved using infrared LED (940 nm) and photo-diode pair.

Miniaturized wireless monitor for long-term monitoring of newborns

Wireless infant monitoring system is a small-size wearable sensor platform. There is a growing trend to simplify the measuring methods to allow a real-time monitoring of the vital signals in home environment. Most of these devices have cables, are quite large in size that may disturb infants everyday life and need continuous supervising from parent. In this paper we propose a monitoring system that detects the most important vital signals of baby and transmits results over wireless link to the control device that could be any smart-phone. The system is capable of measuring blood oxygen level, heart rate, respiration rate, body temperature, body posture and legs activity. Combining all of these raw signals it is possible to use this system in different, possible life-threatening situations during long-term monitoring. Compared to other similar solutions it has small dimensions, low weight, increased reliability of measuring photoplethysmography signal and extended battery life because of the usage of Bluetooth Smart wireless protocol.