Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

Secure Communication for Connected Vehicles Safety Applications

Connected vehicles safety applications are in-vehicle applications aim to improve road safety by assisting, complementing or enhancing driver's alertness and decision making during a driving experience. Safety messages are transmitted among connected vehicles to avoid accidents and minimise the number of casualties due to road accidents. Hence, these applications have stringent security requirements especially on the network availability. In the absence of secure network availability, critical safety messages will not be transmitted and shared. This paper explores the secure communication aspects for connected vehicles safety applications and propose a framework to protect vehicular communication from Distributed Denial of Services (DDoS) attacks

Blackhole attacks in internet of things networks: a review

The internet of things (IoT) is one of data revolution area and is the following extraordinary mechanical jump after the internet. In terms of IoT, it is expected that electronic gadgets that are used on a regular basis would be connected to the current of the internet. IPv6 over low-power wireless personal area networks (6LoWPAN) is a one of IPv6 header pressure technology, and accordingly, it is vulnerable to attack. The IoT is a combination of devices with restricted resource assets like memory, battery power, and computational capability. To solve this, RPL or routing protocol for low power Lossy network is deploy by utilizing a distance vector scheme. One of denial of service (Dos) attack to RPL network is blackhole attack in which the assailant endeavors to become a parent by drawing in a critical volume of traffic to it and drop all packets. In this paper, we discuss research on numerous attacks and current protection methods, focusing on the blackhole attack. There is also discussion of challenge, open research issues and future perspectives in RPL security. Furthermore, research on blackhole attacks and specific detection technique proposed in the literature is also been presented

Lane change decision aid and warning system using LoRa-based vehicle-to-vehicle communication technology

Among the most severe crash scenarios are those caused by driver’s decisions to manoeuvre the vehicle to the adjacent lanes. In most scenarios, drivers’ intentionally change lanes to take over another slower vehicle and preserving the current vehicle speed especially on highway road. The decision may be fatal for drivers of incoming or approaching vehicles which are not aware of the intention and fail to reduce their vehicle speed to avoid lane change collision. Hence, this study proposes a lane change decision aid and warning system which aims to support the driver’s decision prior to performing the lane change on highway road where vehicles are travelling in a single direction. The system implements vehicle-to-vehicle communication (V2V) via long-range (LoRa) communication technology to alert the host vehicle of approaching vehicles and warns the approaching vehicle when a host vehicle intends to change lane. Visual and audible warning will be triggered as precaution mechanism for both host and approaching vehicle drivers. Experiments shows that V2V using LoRa can provide contextual information which are useful to assist drivers in deciding whether to change lane or not on highway use case settings

Application and growth of long-range communications technology in vehicular communications

Long range communications technology (LoRa) has been widely used in a variety of applications and researched in different domains to exploit its full potential. Its openness makes it ideal for a variety of internet of things (IoT) installations which further allows opportunities for viable solutions in vehicular communications. Hence, a bibliometric analysis was performed to distinguish the application and growth of the technology specifically in vehicular communications. The scoping review processes from Arksey and O’Malley was applied to guide the review process. The selected scholarly works adhered to the PRISMA-Sc framework where 385 articles from two main electronic databases, i.e., Scopus and Science Direct which discussed LoRa in vehicular communications contexts were assessed. This study aims to examine how LoRa’s research has grown from year 2010 to 2021 among the scholars and determine key areas discussed in LoRa’s vehicular communications research. Findings from 70 studies in the final analysis indicated that LoRa has been widely studied based on application, theoretical or protocol and performance. However, it has not been widely explored in vehicular context. Hence, our findings support the global research community in this context

Conceptualising flood warning system for connected vehicles

Floods are a common natural hazard in Malaysia during the monsoon season. It affects millions of people each year that leads to severe deaths and infrastructure destruction. In recent time, flood warning system (FWS) has been a notable topic but it has not been extensively implemented in Malaysia. In this study, we developed a FWS that can interface with connected vehicles in order to provide alerts to drivers while also sending warnings to end users. This type of FWS enables vehicles to connect with one another within a particular radius to broadcast flood information via long range (LoRa) communication technology. When the water level rises over a certain point, the system sends a warning to drivers indicated through a mobile application. Drivers have the option to take alternative route, reducing the likelihood of damage when driving into or near a flooded area. The developed application demonstrates that the warning was able to be instantly displayed to the driver if there is a significant increase in water level. Experimental evidence shows that the driver was able to receive the water level alert and a visual interpretation of the immediate area affected by flood through the application

A scoping review on current technology-based approaches to support breastfeeding and informal human milk exchange practices.

Informal human milk exchange is the practice of donating and receiving expressed human milk based on mutual consent between the donor and receiver in the need of human milk for infants below 2 years old. Main concerns related to informal human milk exchange is related to milk siblings and safety handling of the expressed breastmilk. Even though there are countries which have policies and procedures related to human milk bank, informal milk exchange has not been given much attention. Compared to human milk bank, informal human milk exchange is not regulated. This study aims to identify the system focused on personalized breastfeeding tracking and monitoring, online discussion forum, web-based consultation, and breastfeeding station locator. Review of current applications in supporting breastfeeding practices was conducted based on the PRISMA-ScR framework. A literature search was conducted in Scopus and Google Scholar databases to identify articles published in English or Malay and containing systems/applications related to breastfeeding, milk sharing, milk exchange, milk siblings/kinship within the societal context. According to the scoping review, current scientific publications mostly focused on breast milk, breastfeeding, and milk banking concerns, with recurring themes including social reasons, lactation insufficiency, and unsolved nursing problems. These themes highlight the complexities and complexities of informal human milk exchange practices. Two reviewers screened the articles, and the data were extracted and narratively synthesized. During the primary database search, 360 articles were found based on the related titles, abstracts, and keywords. Seventy eight met the inclusion criteria and were finalized in this review. We found that most scholarly works focused on breast milk, breastfeeding and milk banking challenges and issues with recurrent themes i.e., societies, lactation inadequacy and unresolved nursing problems. Based on our literature search and to the best of our knowledge, there is no recent scoping reviews which focuses on technology-based approaches on informal human milk exchange. Findings from this scoping review is important for advancing research and practice in this field, as well as improving outcomes for individuals and families affected by informal human milk exchange

Comparative Study of Machine Learning Algorithms in Classifying HRV for the Driver’s Physiological Condition

Heart Rate Variability (HRV) may be used as a psychological marker to assess drivers’ states from physiological signals such as an electrocardiogram (ECG), electroencephalogram (EEG), and photoplethysmography (PPG). This paper reviews HRV acquisition methods from drivers and machine learning approaches for driver cardiac health based on HRV classification. The study examines four publicly available ECG datasets and analyzes their HRV features, including time domain, frequency domain, short-term measures, and a combination of time and frequency domains. Eight machine learning classifiers, namely K-Nearest Neighbor, Decision Tree, Naive Bayes, Linear Discriminant Analysis, Support Vector Machine, Random Forest, Gradient Boost, and Adaboost, were used to determine whether the driver's state is normal or abnormal. The results show that K-Nearest Neighbor and Decision Tree classifiers had the highest accuracy at 92.86%. The study concludes by assessing the performance of machine learning algorithms in classifying HRV for the driver's physiological condition using the Man-Whitney U test in terms of accuracy and F1 score. We have statistical evidence to support that the prediction quality is different when HRV analysis applies these three sets: (i) time domain measures or frequency domain measures; (ii) frequency domain measures or short-term measures; and (iii) combining time and frequency domains or only frequency domains