Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Smartwatch-Based Information System as a Compliance Detector in Traffic

The purpose of this study is to design a mobile-based application that serves to provide reminders to vehicle users to create orderly traffic and aims to build an obedient attitude to applicable traffic rules. To support this research, we used a qualitative method. While in the application development process using the Prototype approach. The results of this study indicate that the development of this system can increase compliance in traffic and reduce the number of accidents so that an orderly traffic can be created. The main concept of this system is as a reminder so that drivers are orderly traffic, for example, when a driver commits a violation by driving on the wrong road, he will get a reminder. This information system will later get a warning signal that will be sent to the smartwatch when the user violates traffic, the user will be aware of things that endanger his life or others

Real-time head movement tracking through earables in moving vehicles

Abstract. The Internet of Things is enabling innovations in the automotive industry by expanding the capabilities of vehicles by connecting them with the cloud. One important application domain is traffic safety, which can benefit from monitoring the driver’s condition to see if they are capable of safely handling the vehicle. By detecting drowsiness, inattentiveness, and distraction of the driver it is possible to react before accidents happen. This thesis explores how accelerometer and gyroscope data collected using earables can be used to classify the orientation of the driver’s head in a moving vehicle. It is found that machine learning algorithms such as Random Forest and K-Nearest Neighbor can be used to reach fairly accurate classifications even without applying any noise reduction to the signal data. Data cleaning and transformation approaches are studied to see how the models could be improved further. This study paves the way for the development of driver monitoring systems capable of reacting to anomalous driving behavior before traffic accidents can happen

Smart Safety and Accident Prevention System

The primary cause of road accident results in fatalities, serious injuries and monetary losses is known to be due to drowsy or sleepy drivers, according to analysis reports on recent traffic accidents. Lack of sleep, medication, drugs, or prolonged driving contributes to drowsiness. A system that can identify a driver’s drowsy state and warn him before an accident occurs is required to avoid roadside accidents caused by distracted driving. Many researchers have recently expressed their interest in drowsiness detection. The methods essentially involve monitoring the driver’s physiological or behavioral 1summarizes some of the most recent methods put forth in this field is given. We propose an algorithm to monitor eye blinks that uses eye feature points to determine whether the eye is open or closed and sets off an alarm if the driver is drowsy. In-depth experimental results are also provided to highlight the benefits and drawbacks of the proposed method

Sympathetic Loading in Critical Tasks

In this dissertation I developed or perfected unobtrusive methods to quantify sympathetic arousals. Furthermore, I used these methods to study the sympathetic system's role on critical activities, arriving at intriguing conclusions. Sympathetic arousals occur during states of mental, emotional, and/or sensorimotor strain resulting from adverse or demanding circumstances. They are key elements of human physiology's coping mechanism, shoring up resources to a good effect. When the intensity and duration of these arousals are overwhelming, however, then they may block memory and disrupt rational thought or actions at the moment they are needed the most. Arousals abound in three types of critical activities: high-stakes situations, challenging tasks, and critical multitasking. Accordingly, my research was based on three studies representative of these three activity types: `Subject Screening', `Educational Exam', and `Distracted Driving'. In the first study I investigated the association of sympathetic arousals with deceptive behavior in interrogations. In the second study, I investigated the relationship between sympathetic arousals and exam performance. In the third study, I investigated the interaction between sympathetic arousals and driving performance under cognitive, emotional, and sensorimotor distractions. In the interrogation study, I used for the first time a contact-free electrodermal activity measurement method to quantify arousals. The method detected deceptive behavior based on differential sympathetic responses in well-structured interviews. In the exam study, I documented that sympathetic arousals positively correlate with students' exam performance, dispelling the myth of `easy going' super achievers. Finally, in the driving study, my results revealed that not only apparent sensorimotor stressors (texting while driving) but also hidden stressors (cognitive or emotional) could have a significant effect on driving performance.Computer Science, Department o

Trends in Electrodermal Activity, Heart Rate and Temperature during Distracted Driving among Young Novice Drivers.

Driver distraction, defined as the scattering of attention from critical activities for safe driving, is among the key globally recognized contributing factors to road crashes. The trend keeps increasing with in-vehicle information systems and hand-held devices, leading to inattention. Of people in all age groups, young novice teenagers are prone to the risk of road crashes and are also more likely to exhibit risky and unsafe driving behavior. Data shows that the involvement of distracted drivers in fatal & injury collisions is higher for people aged between 16 -34, which is about 55%. Therefore, young drivers are of great concern for the research about driving and evaluation of safe driving conditions, which is vital in upcoming advancements in autonomous vehicles. Several research studies have explored the effects of distracted driving using face tracking and eye glance monitoring. Previous research [50] did not consider much about the effect of distraction on physiological factors and their impact during driving. The current study used data collected from a previous thesis work titled “Detection of Driver Cognitive Distraction Using Machine Learning Methods” by Apurva Misra and conducted new data analysis focusing on new research questions. The main objective of this thesis is to study, identify and discuss the effects on physiological factors like heart rate (HR), electrodermal activity (EDA), body temperature, and motion sickness during distracted driving among young drivers. The data was collected from a driving simulator study comprising 42 participants aged 16 – 23 under normal and distracted driving conditions. Their driving experience ranges from 0 to a maximum of 5 years. Each participant navigated six scenarios, three with distraction and the rest without distraction. Each scenario has a hidden, latent hazard depending on the surrounding; for example, in the work zone scenario, a worker is hidden behind the bulldozer in the work zone. The distraction task is a spoken task for which the driver has to respond verbally, which exerts a workload similar to that observed in conversations using a hands-free mobile phone. The physiological data collected through the Empatica4 wristband was analyzed and compared against age, gender, driver experience, and another parameter like motion sickness score (MSS) obtained from a questionnaire the participants completed after the experiment. Of the physiological factors stated above, it was found that HR and EDA play a significant role while studying distraction. Data analysis showed that HR and EDA increase more during distraction than baseline events. Nearly 80% of drivers with 0 or 1 year of experience tend to have a higher range of HR and EDA, which reveals that they are more distracted than their peers with more experience. From the results of the Load index questionnaire and Motion Sickness susceptibility questionnaire, it is inferred that when MSS increases, there is an increase in HR and EDA. These findings will provide insights into physiological factors for developing distraction mitigation systems or in-vehicle warning systems for distracted drivers

Wearable devices for remote vital signs monitoring in the outpatient setting: an overview of the field

Early detection of physiological deterioration has been shown to improve patient outcomes. Due to recent improvements in technology, comprehensive outpatient vital signs monitoring is now possible. This is the first review to collate information on all wearable devices on the market for outpatient physiological monitoring. A scoping review was undertaken. The monitors reviewed were limited to those that can function in the outpatient setting with minimal restrictions on the patient’s normal lifestyle, while measuring any or all of the vital signs: heart rate, ECG, oxygen saturation, respiration rate, blood pressure and temperature. A total of 270 papers were included in the review. Thirty wearable monitors were examined: 6 patches, 3 clothing-based monitors, 4 chest straps, 2 upper arm bands and 15 wristbands. The monitoring of vital signs in the outpatient setting is a developing field with differing levels of evidence for each monitor. The most common clinical application was heart rate monitoring. Blood pressure and oxygen saturation measurements were the least common applications. There is a need for clinical validation studies in the outpatient setting to prove the potential of many of the monitors identified. Research in this area is in its infancy. Future research should look at aggregating the results of validity and reliability and patient outcome studies for each monitor and between different devices. This would provide a more holistic overview of the potential for the clinical use of each device