About the New Methodology and XAI-Based Software Toolkit for Risk Assessment

There are different approaches in different areas of what the risk is. ISO 31000 risk management standards describe risk as the effect of uncertainty on objectives. Many existing risk assessment procedures are based on the assumption that risk is the amount of any damage or loss multiplied by the probability of an event that could cause the damage. We are proposing a new risk approach, based on Hillson’s positive risk philosophy, that risk is not just a threat but also a composition of new opportunities, efforts that need to be put in, and uncertainty. For this approach, we composed a risk formula and a methodology based on that formula. A prototypical software tool was developed, and an experiment was performed using this tool to evaluate the risk of several interconnected projects and validate the developed risk assessment methodology. It should be mentioned that, in the methodology, the decision-making process is performed traceably; therefore, it can be stated that it has explainable artificial intelligence (XAI) traits

Intelligent Lighting Control Providing Semi-Autonomous Assistance

Increasing resident's comfort and reducing energy costs have always been two primary objectives of intelligent lighting control systems. It is quite difficult to provide control satisfying the level of individual comfort, sufficient illumination and the energy reduction goals simultaneously. However, finding the balance between resident's preferred and recommended illumination for the current resident's activity may be beneficial. This paper addresses the problem of ensuring semi–autonomous assistance in controlling the intensity of light sources. The proposed decision making algorithm allows to provide gradual adaptation to the recommended illumination according the resident's activity. Resident's activity recognition is performed using one of the most popular models of deep learning, such as Convolutional Neural Networks (CNNs)

Photoplethysmography-Based Continuous Systolic Blood Pressure Estimation Method for Low Processing Power Wearable Devices

Regardless of age, it is always important to detect deviations in long-term blood pressure from normal levels. Continuous monitoring of blood pressure throughout the day is even more important for elderly people with cardiovascular diseases or a high risk of stroke. The traditional cuff-based method for blood pressure measurements is not suitable for continuous real-time applications and is very uncomfortable. To address this problem, continuous blood pressure measurement methods based on photoplethysmogram (PPG) have been developed. However, these methods use specialized high-performance hardware and sensors, which are not available for common users. This paper proposes the continuous systolic blood pressure (SBP) estimation method based on PPG pulse wave steepness for low processing power wearable devices and evaluates its suitability using the commercially available CMS50FW Pulse Oximeter. The SBP estimation is done based on the PPG pulse wave steepness (rising edge angle) because it is highly correlated with systolic blood pressure. The SBP estimation based on this single feature allows us to significantly reduce the amount of data processed and avoid errors, due to PPG pulse wave amplitude changes resulting from physiological or external factors. The experimental evaluation shows that the proposed SBP estimation method allows the use of off-the-shelf wearable PPG measurement devices with a low sampling rate (up to 60 Hz) and low resolution (up to 8-bit) for precise SBP measurements (mean difference MD = −0.043 and standard deviation SD = 6.79). In contrast, the known methods for continuous SBP estimation are based on equipment with a much higher sampling rate and better resolution characteristics