Driver and Passenger Identification from Smartphone Data

The objective of this paper is twofold. First, it presents a brief overview of existing driver and passenger identification or recognition approaches which rely on smartphone data. This includes listing the typically available sensory measurements and highlighting a few key practical considerations for automotive settings. Second, a simple identification method that utilises the smartphone inertial measurements and, possibly, doors signal is proposed. It is based on analysing the user behaviour during entry, namely the direction of turning, and extracting relevant salient features, which are distinctive depending on the side of entry to the vehicle. This is followed by applying a suitable classifier and decision criterion. Experimental data is shown to demonstrate the usefulness and effectiveness of the introduced probabilistic, low-complexity, identification technique.Jaguar Land Rover under the Centre for Advanced Photonics and Electronics (CAPE) agreement

Quit Playing With Your Watch: Perceptions Of Smartwatch Use

This study identified perceptions and social norms that may affect smartwatch adoption. Interviews were conducted to identify perceptions of smartwatch use and norms that might affect those perceptions. Smartwatch use was found to activate norms associated with wristwatch use – specifically, smartwatch users’ peers took offense to the users looking at their wristwatches. This study also found that norms prevent the use of smartwatches’ voice controls in public and various perceptions of smartwatch use and ownership

Recognition, Analysis, and Assessments of Human Skills using Wearable Sensors

One of the biggest social issues in mature societies such as Europe and Japan is the aging population and declining birth rate. These societies have a serious problem with the retirement of the expert workers, doctors, and engineers etc. Especially in the sectors that require long time to make experts in fields like medicine and industry; the retirement and injuries of the experts, is a serious problem. The technology to support the training and assessment of skilled workers (like doctors, manufacturing workers) is strongly required for the society. Although there are some solutions for this problem, most of them are video-based which violates the privacy of the subjects. Furthermore, they are not easy to deploy due to the need for large training data. This thesis provides a novel framework to recognize, analyze, and assess human skills with minimum customization cost. The presented framework tackles this problem in two different domains, industrial setup and medical operations of catheter-based cardiovascular interventions (CBCVI). In particular, the contributions of this thesis are four-fold. First, it proposes an easy-to-deploy framework for human activity recognition based on zero-shot learning approach, which is based on learning basic actions and objects. The model recognizes unseen activities by combinations of basic actions learned in a preliminary way and involved objects. Therefore, it is completely configurable by the user and can be used to detect completely new activities. Second, a novel gaze-estimation model for attention driven object detection task is presented. The key features of the model are: (i) usage of the deformable convolutional layers to better incorporate spatial dependencies of different shapes of objects and backgrounds, (ii) formulation of the gaze-estimation problem in two different way, as a classification as well as a regression problem. We combine both formulations using a joint loss that incorporates both the cross-entropy as well as the mean-squared error in order to train our model. This enhanced the accuracy of the model from 6.8 by using only the cross-entropy loss to 6.4 for the joint loss. The third contribution of this thesis targets the area of quantification of quality of i actions using wearable sensor. To address the variety of scenarios, we have targeted two possibilities: a) both expert and novice data is available , b) only expert data is available, a quite common case in safety critical scenarios. Both of the developed methods from these scenarios are deep learning based. In the first one, we use autoencoders with OneClass SVM, and in the second one we use the Siamese Networks. These methods allow us to encode the expert’s expertise and to learn the differences between novice and expert workers. This enables quantification of the performance of the novice in comparison to the expert worker. The fourth contribution, explicitly targets medical practitioners and provides a methodology for novel gaze-based temporal spatial analysis of CBCVI data. The developed methodology allows continuous registration and analysis of gaze data for analysis of the visual X-ray image processing (XRIP) strategies of expert operators in live-cases scenarios and may assist in transferring experts’ reading skills to novices

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Edge Intelligence : Empowering Intelligence to the Edge of Network

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.Peer reviewe

Driving Manoeuvre Recognition using Mobile Sensors

Automobiles are integral in today's society as they are used for transportation, commerce, and public services. The ubiquity of automotive transportation creates a demand for active safety technologies for the consumer. Recently, the widespread use and improved sensing and computing capabilities of mobile platforms have enabled the development of systems that can measure, detect, and analyze driver behaviour. Most systems performing driver behaviour analysis depend on recognizing driver manoeuvres. Improved accuracy in manoeuvre detection has the potential to improve driving safety, through applications such as monitoring for insurance, the detection of aggressive, distracted or fatigued driving, and for new driver training. This thesis develops algorithms for estimating vehicle kinematics and recognizing driver manoeuvres using a smartphone device. A kinematic model of the car is first introduced to express the vehicle's position and orientation. An Extended Kalman Filter (EKF) is developed to estimate the vehicle's positions, velocities, and accelerations using mobile measurements from inertial measurement units and the Global Positioning System (GPS). The approach is tested in simulation and validated on trip data using an On-board Diagnostic (OBD) device as the ground truth. The 2D state estimator is demonstrated to be an effective filter for measurement noise. Manoeuvre recognition is then formulated as a time-series classification problem. To account for an arbitrary orientation of the mobile device with respect to the vehicle, a novel method is proposed to estimate the phone's rotation matrix relative to the car using PCA on the gyroscope signal. Experimental results demonstrate that e Principal Component (PC) corresponds to a frame axis in the vehicle reference frame, so that the PCA projection matrix can be used to align the mobile device measurement data to the vehicle frame. A major impediment to classifier-manoeuvre recognition is the need for training data, specifically collecting enough data and generating an accurate ground truth. To address this problem, a novel training process is proposed to train the classifier using only simulation data. Training on simulation data bypasses these two issues as data can be cheaply generated and the ground truth is known. In this thesis, a driving simulator is developed using a Markov Decision Process (MDP) to generate simulated data for classifier training. Following training data generation, feature selection is performed using simple features such as velocity and angular velocity. A manoeuvre segmentation classifier is trained using multi-class SVMs. Validation was performed using data collected from driving sessions. A grid search was employed for parameter tuning. The classifier was found to have a 0.8158 average precision rate and a 0.8279 average recall rate across all manoeuvres resulting in an average F1 score of 0.8194 on the dataset

A proposal to improve wearables development time and performance : software and hardware approaches.

Programa de P?s-Gradua??o em Ci?ncia da Computa??o. Departamento de Ci?ncia da Computa??o, Instituto de Ci?ncias Exatas e Biol?gicas, Universidade Federal de Ouro Preto.Wearable devices are a trending topic in both commercial and academic areas. Increasing demand for innovation has raised the number of research and products, addressing brandnew challenges, and creating profitable opportunities. Current wearable devices can be employed in solving problems in a wide variety of areas. Such coverage generates a relevant number of requirements and variables that influences solutions performance. It is common to build specific wearable versions to fit each targeting application niche, what requires time and resources. Currently, the related literature does not present ways to treat the hardware/software in a generic way enough to allow both parts reuse. This manuscript presents the proposal of two components focused on hardware/software, respectively, allowing the reuse of di?erent parts of a wearable solution. A platform for wearables development is outlined as a viable way to recycle an existing organization and architecture. The platform use was proven through the creation of a wearable device that was enabled to be used in di?erent contexts of the mining industry. In the software side, a development and customization tool for specific operating systems is demonstrated. This tool aims not only to reuse standard software components but also to provide improved performance simultaneously. A real prototype was designed and created as a manner to validate the concepts. In the results, the comparison between the operating system generated by the tool versus a conventional operating system allows quantifying the improvement rate. The former operating system showed approximate performance gains of 100% in processing tasks, 150% in memory consumption and I/O operations, and approximately 20% of reduction in energy consumption. In the end, performance analysis allows inferring that the proposals presented here contribute to this area, easing the development and reuse of wearable solutions as a whole

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen