Classification of Drivers' Workload Using Physiological Signals in Conditional Automation

The use of automation in cars is increasing. In future vehicles, drivers will no longer be in charge of the main driving task and may be allowed to perform a secondary task. However, they might be requested to regain control of the car if a hazardous situation occurs (i.e., conditionally automated driving). Performing a secondary task might increase drivers' mental workload and consequently decrease the takeover performance if the workload level exceeds a certain threshold. Knowledge about the driver's mental state might hence be useful for increasing safety in conditionally automated vehicles. Measuring drivers' workload continuously is essential to support the driver and hence limit the number of accidents in takeover situations. This goal can be achieved using machine learning techniques to evaluate and classify the drivers' workload in real-time. To evaluate the usefulness of physiological data as an indicator for workload in conditionally automated driving, three physiological signals from 90 subjects were collected during 25 min of automated driving in a fixed-base simulator. Half of the participants performed a verbal cognitive task to induce mental workload while the other half only had to monitor the environment of the car. Three classifiers, sensor fusion and levels of data segmentation were compared. Results show that the best model was able to successfully classify the condition of the driver with an accuracy of 95%. In some cases, the model benefited from sensors' fusion. Increasing the segmentation level (e.g., size of the time window to compute physiological indicators) increased the performance of the model for windows smaller than 4 min, but decreased for windows larger than 4 min. In conclusion, the study showed that a high level of drivers' mental workload can be accurately detected while driving in conditional automation based on 4-min recordings of respiration and skin conductance

Modeling and simulation of climbing robot

Abstract This project is a part of a bigger project aiming to develop bio-inspired climbing robots for space exploration. My goal is to provide a tool to simulate the physics of those robots, design them and test their controllers. Therefore my project is divided into three main steps. The first step consists in developing a physical plugin for Webots in order to be able to simulate the bio-inspired adhesion mechanism that is developed in the research laboratory. The second step aims to create 3D models of two different robots in Webots. These models will reflect as accurately as possible the true mechanics of the ones developed in the Mechanical Engineering Laboratory of Dr. Carlo Menon at Simon Fraser University. The first robot will consists in a six-legged spider-like robot with 6 DOFs on each leg. The second one will consist in a simple four-legged gecko-like robot. The third step will study the walking mechanisms of the geckos and design a robust walking pattern using CPG's with feedback for the gecko robot previously modeled. More precisely, the study will focus on the mechanism at the level of a legl; how to optimize the attachment of the leg through adhesion and the reflexes occurring at this level. This page has been left blank on purpos

Bike usage forecasting for optimal rebalancing operations in bike-sharing systems

This article presents the first step of a project focusing on enhancing the management of bike-sharing systems. The objective of the project is to optimize the daily rebalancing operations that need to be performed by operators of bike-sharing systems using machine-learning algorithms and constraint programming. This study presents an evaluation of machine learning algorithms developed for forecasting the availability of bikes on three Swiss bike-sharing networks. The results demonstrate the superiority of the Multi-Layer Perceptron algorithm for forecasting available bikes at station-level for different prediction horizons and its applicability for real-time prediction generation

Predictive modeling for optimization of field operations in bike-sharing systems

This article presents a framework to facilitate and optimize the management of field operations for bike-sharing companies. The study focuses on two modules based on artificial intelligence: the prediction module forecasts bikes availability at station-level using machine-learning and the rebalancing module provides optimal rebalancing operations and routes using constraint programming. The evaluation on 9 months of data collected from a real bike-sharing network notably highlighted the superior forecasting accuracy of the Multilayer Perceptron algorithm

Gesture recognition corpora and tools ::a scripted ground truthing method

This article presents a framework supporting rapid prototyping of multimodal applications, the creation and management of datasets and the quantitative evaluation of classification algorithms for the specific context of gesture recognition. A review of the available corpora for gesture recognition highlights their main features and characteristics. The central part of the article describes a novel method that facilitates the cumbersome task of corpora creation. The developed method supports automatic ground truthing of the data during the acquisition of subjects by enabling automatic labeling and temporal segmentation of gestures through scripted scenarios. The temporal errors generated by the proposed method are quantified and their impact on the performances of recognition algorithm are evaluated and discussed. The proposed solution offers an efficient approach to reduce the time required to ground truth corpora for natural gestures in the context of close human–computer interaction

A Survey of datasets for human gesture recognition

This paper presents a survey on datasets created for the field of gesture recognition. The main characteristics of the datasets are presented on two tables to provide researchers a clear and rapid access to the information. This paper also provides a comprehensive description of the datasets and discusses their general strengths and limitations. Guidelines for creation and selection of datasets for gesture recognition are proposed. This survey should be a key-access point for researchers looking to create or use datasets in the field of human gesture recognition

Real-time usage forecasting for bike-sharing systems ::a study on random forest and convolutional neural network applicability

In this paper, we present a system that has been developed to facilitate the collection and use of Bike-Sharing Systems data for research, notably to develop and compare bike usage forecasting algorithms. We collected internal and external data for six different European cities and developed a system providing short and long-term predictions of bikes and slots availabilities for bike-sharing stations in real-time. In order to provide the best predictions, we developed and compared the performances of two types of algorithm; the first one is based on the state-of-the-art Random Forest algorithm and the second one is based on Convolutional Neural Networks. Our study demonstrates their applicability, showing better accuracy for short-term predictions with the Random Forest algorithm and better long-term prediction accuracy with the Convolutional Neural Networks algorithm

Etude exploratoire d’un assistant digital d’aide au montage basé sur la projection en réalité augmentée

This article presents PMF-Vision, a digital assistant for manual assembly based on the concept of augmented reality information projected on the workspace. This digital assistant aims at assisting operators with mild mental deficiencies in their assembly tasks by tracking their actions in real-time. In this research, we analyzed hand tracking algorithms based on the latest deep learning techniques and highlighted the best candidate for our context in terms of accuracy and processing time. We present different interaction concepts for the system and propose to evaluate two visualisation concepts to assist the operator during component placement. The results of the exploratory study (N=6) highlighted the excellent usability of the system and the interest, expressed by the target population, to use such tools in their daily tasks.Cet article présente PMF-Vision, un assistant digital d’aide au montage manuel basé sur le concept de projection d’informations en réalité augmentée sur l’espace de travail. Cet assistant digital vise à assister des opérateurs en situation de handicap mental dans leurs tâches de montage en suivant leurs actions en temps-réel. Dans le cadre de cette recherche, nous avons analysé les algorithmes de suivi des mains basés sur les dernières techniques d’apprentissage profond et mis en valeur le meilleur candidat pour notre contexte en termes de précision et de temps de traitement. Nous présentons différents concepts d’interaction pour le système et proposons d’évaluer deux concepts de visualisation pour assister l’opérateur lors du placement de composants. Les résultats de l’étude exploratoire (N=6) ont permis de mettre en valeur l’excellente utilisabilité du prototype et l’intérêt, exprimé par la population cible, d’utiliser de tels outils dans leurs tâches quotidiennes