A Context-Aware S-Health Service System for Drivers

Advanced e-Call Support Based on Non-Intrusive Driver Condition Monitoring for Connected and Autonomous Vehicles

BACKGROUND

The growth of the number of vehicles in traffic has led to an exponential increase in the number of road accidents with many negative consequences, such as loss of lives and pollution.

METHODS

This article focuses on using a new technology in automotive electronics by equipping a semi-autonomous vehicle with a complex sensor structure that is able to provide centralized information regarding the physiological signals (Electro encephalogram-EEG, electrocardiogram-ECG) of the driver/passengers and their location along with indoor temperature changes, employing the Internet of Things (IoT) technology. Thus, transforming the vehicle into a mobile sensor connected to the internet will help highlight and create a new perspective on the cognitive and physiological conditions of passengers, which is useful for specific applications, such as health management and a more effective intervention in case of road accidents. These sensor structures mounted in vehicles will allow for a higher detection rate of potential dangers in real time. The approach uses detection, recording, and transmission of relevant health information in the event of an incident as support for e-Call or other emergency services, including telemedicine.

RESULTS

The novelty of the research is based on the design of specialized non-invasive sensors for the acquisition of EEG and ECG signals installed in the headrest and backrest of car seats, on the algorithms used for data analysis and fusion, but also on the implementation of an IoT temperature measurement system in several points that simultaneously uses sensors based on MEMS technology. The solution can also be integrated with an e-Call system for telemedicine emergency assistance.

CONCLUSION

The research presents both positive and negative results of field experiments, with possible further developments. In this context, the solution has been developed based on state-of-the-art technical devices, methods, and technologies for monitoring vital functions of the driver/passengers (degree of fatigue, cognitive state, heart rate, blood pressure). The purpose is to reduce the risk of accidents for semi-autonomous vehicles and to also monitor the condition of passengers in the case of autonomous vehicles for providing first aid in a timely manner. Reported abnormal values of vital parameters (critical situations) will allow interveneing in a timely manner, saving the patient's life, with the support of the e-Call system.

Statistical Properties of a New Social Media Context Awareness Scale (SMCA)—A Preliminary Investigation

In the Internet of Things era, or in the digitalization and mediatization of everything paradigm, where context awareness computing is on the rise, people are also facing a new challenge, that of being aware of the digital contexts, in all situations when surfing the internet’s ocean of row information. The emerging social media context awareness competency refers to a new emerging skill regarding the trust load people give to a specific social media context they encounter. Since it is an emergent competence, it cannot be understood as standalone. If the digital context would not be available, we would not develop such a competence. Being a competence, it must be defined by three core elements: Knowledge, skills, and attitudes. Consequently, we have operationalized the competence of social media context awareness in terms of social media literacy, social media communication process understanding, social media content impact awareness, and social media confidence. An online questionnaire was created under the Erasmus+ project Hate’s Journey, addressing a convenience sample of 206 online youth respondents from Turkey, Spain, Latvia, and Romania. Our team has computed a reliability analysis on the social media context awareness scale designed with four items referring to social media literacy (m = 3.79, SD = 1), social media communication process understanding (m = 3.77, SD = 0.9), social media content impact awareness (m = 3.88, SD = 1), and social media confidence (m = 3.45, SD = 1). Cronbach’s alpha coefficient and the Exploratory Factor Analysis demonstrated the acceptable reliability of the SMCA scale, α = 0.87. Conclusions, implications, and limitations are discussed in the context of social sustainability.

Optimal Learning and Self-Awareness Versus PDI

This manuscript will explore and analyze the effects of different paradigms for the control of rigid body motion mechanics. The experimental setup will include deterministic artificial intelligence composed of optimal self-awareness statements together with a novel, optimal learning algorithm, and these will be re-parameterized as ideal nonlinear feedforward and feedback evaluated within a Simulink simulation. Comparison is made to a custom proportional, derivative, integral controller (modified versions of classical proportional-integral-derivative control) implemented as a feedback control with a specific term to account for the nonlinear coupled motion. Consistent proportional, derivative, and integral gains were used throughout the duration of the experiments. The simulation results will show that akin feedforward control, deterministic self-awareness statements lack an error correction mechanism, relying on learning (which stands in place of feedback control), and the proposed combination of optimal self-awareness statements and a newly demonstrated analytically optimal learning yielded the highest accuracy with the lowest execution time. This highlights the potential effectiveness of a learning control system.