Wearable Mobile-Based Emotional Response-Monitoring System for Drivers

Lee, Boon-Giin; Chong, Teak Wei; Lee, Boon Leng; Park, Hee Joon; Kim, Yoon Nyun; Kim, Beom Joon

doi:10.1109/thms.2017.2658442

Cited by 37 publications

(17 citation statements)

References 32 publications

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“…For instance, variations in the trapezius muscle (a major back muscle that extends from the neck to shoulder blades and lower spine) and deltoid (triangular muscle located on uppermost part of an arm and the top of shoulder) are influenced by mental stress (Wikström, 1993 ; Balasubramanian and Adalarasu, 2007 ; Hirao et al, 2007 ; Wijsman et al, 2013 ; Luijcks et al, 2014 ; cf., Morris et al, 2017 ). A recent study (Lee et al, 2017a ) recorded trapezius muscle activity to detect stress in a driving simulator under relaxed and stressed conditions. A continuous increase over time in muscular tension was associated with greater stress experienced due to driving task (Lee et al, 2017a ).…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

“…A recent study (Lee et al, 2017a ) recorded trapezius muscle activity to detect stress in a driving simulator under relaxed and stressed conditions. A continuous increase over time in muscular tension was associated with greater stress experienced due to driving task (Lee et al, 2017a ). Muscular tension can thus be a useful metric of stress level that can be utilized in driving research.…”

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

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A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

234

181

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As driving functions become increasingly automated, motorists run the risk of becoming cognitively removed from the driving process. Psychophysiological measures may provide added value not captured through behavioral or self-report measures alone. This paper provides a selective review of the psychophysiological measures that can be utilized to assess cognitive states in real-world driving environments. First, the importance of psychophysiological measures within the context of traffic safety is discussed. Next, the most commonly used physiology-based indices of cognitive states are considered as potential candidates relevant for driving research. These include: electroencephalography and event-related potentials, optical imaging, heart rate and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. For each of these measures, an overview is provided, followed by a discussion of the methods for measuring it in a driving context. Drawing from recent empirical driving and psychophysiology research, the relative strengths and limitations of each measure are discussed to highlight each measures' unique value. Challenges and recommendations for valid and reliable quantification from lab to (less predictable) real-world driving settings are considered. Finally, we discuss measures that may be better candidates for a near real-time assessment of motorists' cognitive states that can be utilized in applied settings outside the lab. This review synthesizes the literature on in-vehicle psychophysiological measures to advance the development of effective human-machine driving interfaces and driver support systems.

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Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

Section: Psychophysiological Measures To Assess Cognitive Statesmentioning

confidence: 99%

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Lohani

Payne

Strayer

2019

Front. Hum. Neurosci.

234

181

View full text Add to dashboard Cite

show abstract

“…This adds latency to the model that is able to provide alert information every 2 s using regression to obtain the reaction time and awareness state. In [75], authors use also an Arduino platforms (plus BLE and sensors shields) to connect to a Google Nexus 5 smartphone running a mobile application. The app uses SVM to predict the emotional state of the driver using a three class (relaxed, stressed, and fatigued) classification.…”

Section: ) Embedding Methods and Performancementioning

confidence: 99%

“…Another study where authors use features coming from human biosignals for the classification of the driver state with an SVM is [75]. They define the state of the driver with regards the Abraham-Hicks emotional guidance scale and establish three states: relaxed, stressed and fatigued.…”

Section: ) Algorithms Structurementioning

confidence: 99%

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Borrego-Carazo

Castells-Rufas

Biempica³

et al. 2020

IEEE Access

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The advent of machine learning (ML) methods for the industry has opened new possibilities in the automotive domain, especially for Advanced Driver Assistance Systems (ADAS). These methods mainly focus on specific problems ranging from traffic sign and light recognition to pedestrian detection. In most cases, the computational resources and power budget found in ADAS systems are constrained while most machine learning methods are computationally intensive. The usual solution consists in adapting the ML models to comply with the memory and real-time (RT) requirements for inference. Some models are easily adapted to resource-constrained hardware, such as Support Vector Machines, while others, like Neural Networks, need more complex processes to fit into the desired hardware. The ADAS hardware (HW platforms) are diverse, from complex MPSoC CPUs down to classical MCUs, DPSs and application-specific FPGAs and ASICs or specific GPU platforms (such as the NVIDIA families Tegra or Jetson). Therefore, there is a tradeoff between the complexity of the ML model implemented and the selected platform that impacts the performance metrics: function results, energy consumption and speed (latency and throughput). In this paper, a survey in the form of systematic review is conducted to analyze the scope of the published research works that embed ML models into resource-constrained implementations for ADAS applications and what are the achievements regarding the ML performance, energy and speed trade-off. INDEX TERMS Machine learning, embedded software, automotive engineering, GPU, FPGA, ADAS. I. INTRODUCTION Safety is increasingly important for drivers and other users of the driving environment such as pedestrians, cyclists, bikers, scooters, etc. Only in 2016, road accidents in the European Union summed up to 25.600 fatalities and 1.4 million people injured [1]. Many measures are being taken by public institutions (improving road quality, enhancing driver consciousness and driving regulations, etc.) to improve these numbers. However, the cause of most of these accidents resides in human errors or distractions. For this reason, automotive firms are pushing up research on improving safety. The cornerstone of these advances are Advanced Driver Assistance Systems (ADAS). ADAS systems consist in several sensors, processing and actuators components that help drivers to avoid accidents and The associate editor coordinating the review of this manuscript and approving it for publication was Ye Duan.

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“…Nonetheless, due to the lack of data and the immaturity of the technology required to acquire and utilize non-intrusive user satisfaction feedback in wireless networks, it is not a common discussion topic in the literature and is limited to but a few applications [42]. Nevertheless, nonintrusive user feedback has been proposed to make relevant automotive decisions in many applications, such as cloud gaming, healthcare, and human-computer interaction [43][44][45]. In this thesis, the proposed framework will enable non-intrusive user satisfaction feedback collection in wireless networks.…”

Section: Examples Of Intrusive Collection Methods Include Surveys Andmentioning

confidence: 99%

Big Data-Driven AI-based Wireless Network Personalization

Alkurd¹

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Wearable Mobile-Based Emotional Response-Monitoring System for Drivers

Cited by 37 publications

References 32 publications

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

A Review of Psychophysiological Measures to Assess Cognitive States in Real-World Driving

Resource-Constrained Machine Learning for ADAS: A Systematic Review

Big Data-Driven AI-based Wireless Network Personalization

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