A Machine Learning Smartphone-based Sensing for Driver Behavior Classification
Higher School of Communications of Tunis, University of Carthage（迦太基大学突尼斯高等通信学院）；CEMSE Division, King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学（KAUST）计算工程与科学学院（CEMSE Division））
Sarra Ben Brahim, Hakim Ghazzai, Hichem Besbes, Yehia Massoud
备注：
Accepted for publication in IEEE International Symposium on Circuits and Systems (ISCAS 2022), Austin, TX, USA, May 2022
Driver behavior profiling is one of the main issues in the insurance industries and fleet management, thus being able to classify the driver behavior with low-cost mobile applications remains in the spotlight of autonomous driving. However, using mobile sensors may face the challenge of security, privacy, and trust issues. To overcome those challenges, we propose to collect data sensors using Carla Simulator available in smartphones (Accelerometer, Gyroscope, GPS) in order to classify the driver behavior using speed, acceleration, direction, the 3-axis rotation angles (Yaw, Pitch, Roll) taking into account the speed limit of the current road and weather conditions to better identify the risky behavior. Secondly, after fusing inter-axial data from multiple sensors into a single file, we explore different machine learning algorithms for time series classification to evaluate which algorithm results in the highest performance.
Benha University（本哈大学）；Egypt-Japan University of Science and Technology (E-JUST)（埃及日本科技大学）；Alexandria University（亚历山大大学）
Several intelligent transportation systems focus on studying the various driver behaviors for numerous objectives. This includes the ability to analyze driver actions, sensitivity, distraction, and response time. As the data collection is one of the major concerns for learning and validating different driving situations, we present a driver behavior switching model validated by a low-cost data collection solution using smartphones. The proposed model is validated using a real dataset to predict the driver behavior in short duration periods. A literature survey on motion detection (specifically…
Deep Learning-Based Detection of Cognitive Impairment from Passive Smartphone Sensing with Routine-Aware Augmentation and Demographic Personalization
Department of Electrical and Computer Engineering, Cockrell School of Engineering, The University of Texas at Austin（电气与计算机工程系，Cockrell工程学院，德克萨斯大学奥斯汀分校）；Department of Neurology, Dell Medical School, The University of Texas at Austin（神经病学系，德克萨斯医学学院，德克萨斯大学奥斯汀分校）；Department of Neurology, University of Texas Southwestern Medical Center（神经病学系，德克萨斯西南医学中心）；Peter O’Donnell Jr. Brain Institute, University of Texas Southwestern Medical Center（彼得·奥·唐纳德·杰罗姆脑研究所，德克萨斯西南医学中心）；Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin（语言病理学系，摩依学院，德克萨斯大学奥斯汀分校）
Politecnico di Milano（米兰理工大学）；Kubris S.r.l.（Kubris有限公司）
Smartphone usage while driving is unanimously considered to be a really dangerous habit due to strong correlation with road accidents. In this paper, the problem of detecting whether the driver is using the phone during a trip is addressed. To do this, high-frequency data from the triaxial inertial measurement unit (IMU) integrated in almost all modern phone is processed without relying on external inputs so as to provide a self-contained approach. By resorting to a frequency-domain analysis, it is possible to extract from the raw signals the useful information needed to detect when the driver…
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
Singapore University of Technology and Design（新加坡科技设计大学）；Tianqiao and Chrissy Chen Institute（田桥和克里斯西·陈研究所）；Nanyang Technological University（南洋理工大学）；Purdue University（普渡大学）
Digital contact tracing has emerged as a viable tool supplementing manual contact tracing. To date, more than 100 contact tracing applications have been published to slow down the spread of highly contagious Covid-19. Despite subtle variabilities among these applications, all of them achieve contact tracing by manipulating the following three components: a) use a personal device to identify the user while designing a secure protocol to anonymize the user's identity; b) leverage networking technologies to analyze and store the data; c) exploit rich sensing features on the user device to detect …
Universidad Autónoma de Chihuahua（奇瓦瓦自治大学）；Facultad de Ingeniería（工程学院）；Secretaría de Investigación y Posgrado（研究与研究生部）
Road transportation is of critical importance for a nation, having profound effects in the economy, the health and life style of its people. With the growth of cities and populations come bigger demands for mobility and safety, creating new problems and magnifying those of the past. New tools are needed to face the challenge, to keep roads in good conditions, their users safe, and minimize the impact on the environment. This dissertation is concerned with road quality assessment and aggressive driving, two important problems in road transportation, approached in the context of Intelligent Tran…
University of Oxford（牛津大学）；The Alan Turing Institute（艾伦·图灵研究所）；University of Warwick（华威大学）；University College London（伦敦大学学院）
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature an…
To study users' travel behaviour and travel time between origin and destination, researchers employ travel surveys. Although there is consensus in the field about the potential, after over ten years of research and field experimentation, Smartphone-based travel surveys still did not take off to a large scale. Here, computer intelligence algorithms take the role that operators have in Traditional Travel Surveys; since we train each algorithm on data, performances rest on the data quality, thus on the ground truth. Inaccurate validations affect negatively: labels, algorithms' training, travel di…
Road crashes are the sixth leading cause of lost disability-adjusted life-years (DALYs) worldwide. One major challenge in traffic safety research is the sparsity of crashes, which makes it difficult to achieve a fine-grain understanding of crash causations and predict future crash risk in a timely manner. Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors. As an alternative to using sensors fixed in vehicles, this paper presents a scalable approach for detecting hard-braking events using th…
Unlocking Mental Health: Exploring College Students' Well-being through Smartphone Behaviors
Department of Economics University of Southern California（经济学系 美国南加州大学）；Electrical and Computer Engineering Purdue University（电气与计算机工程系 帕克森大学）；Information Sciences Institute University of Southern California（信息科学研究所 美国南加州大学）
Centre for Security, Communications and Network Research, Plymouth University（普利茅斯大学安全、通信与网络研究中心）；Computer Sciences and Information Technology College, Majmaah University（迈季迈阿大学计算机科学与信息技术学院）；College of Education for Pure Science, University of Baghdad（巴格达大学纯科学教育学院）；Security Research Institute, Edith Cowan University（伊迪斯科文大学安全研究所）；School of Computing, University of Portsmouth（朴茨茅斯大学计算学院）
Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates …
AcousAF: Acoustic Sensing-Based Atrial Fibrillation Detection System for Mobile Phones
Research Institute of Trustworthy Autonomous Systems（可信自主系统研究院）；Southern University of Science and Technology（南方科技大学）；Southern University of Science and Technology Hospital（南方科技大学医院）
Technische Universität Dresden（德累斯顿工业大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）；University of Padova（帕多瓦大学）；M31
Smartphones and wearable devices are fast growing technologies that, in conjunction with advances in wireless sensor hardware, are enabling ubiquitous sensing applications. Wearables are suitable for indoor and outdoor scenarios, can be placed on many parts of the human body and can integrate a large number of sensors capable of gathering physiological and behavioral biometric information. Here, we are concerned with gait analysis systems that extract meaningful information from a user's movements to identify anomalies and changes in their walking style. The solution that is put forward is sub…
Insight Centre for Data Analytics, University College Dublin（都柏林大学学院洞察数据分析中心）；Institute of Systems Engineering, Macau University of Science and Technology（澳门科技大学系统工程研究所）；China Telecom at Macau（中国电信（澳门））；Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology（新泽西理工学院海伦与约翰·C·哈特曼电气与计算机工程系）
Data volume grows explosively with the proliferation of powerful smartphones and innovative mobile applications. The ability to accurately and extensively monitor and analyze these data is necessary. Much concern in mobile data analysis is related to human beings and their behaviours. Due to the potential value that lies behind these massive data, there have been different proposed approaches for understanding corresponding patterns. To that end, monitoring people's interactions, whether counting them at fixed locations or tracking them by generating origin-destination matrices is crucial. The…
INESC TEC（INESC TEC（葡萄牙系统与计算机工程研究所））；Faculty of Engineering, University of Porto（波尔图大学工程学院）；University of São Paulo（圣保罗大学）
Human activity recognition (HAR) is a classification task that aims to classify human activities or predict human behavior by means of features extracted from sensors data. Typical HAR systems use wearable sensors and/or handheld and mobile devices with built-in sensing capabilities. Due to the widespread use of smartphones and to the inclusion of various sensors in all contemporary smartphones (e.g., accelerometers and gyroscopes), they are commonly used for extracting and collecting data from sensors and even for implementing HAR systems. When using mobile devices, e.g., smartphones, HAR sys…
