6 research outputs found
Personalising Vibrotactile Displays through Perceptual Sensitivity Adjustment
Haptic displays are commonly limited to transmitting a discrete
set of tactile motives. In this paper, we explore the
transmission of real-valued information through vibrotactile
displays. We simulate spatial continuity with three perceptual
models commonly used to create phantom sensations: the linear,
logarithmic and power model. We show that these generic
models lead to limited decoding precision, and propose a
method for model personalization adjusting to idiosyncratic
and spatial variations in perceptual sensitivity. We evaluate
this approach using two haptic display layouts: circular, worn
around the wrist and the upper arm, and straight, worn along
the forearm. Results of a user study measuring continuous
value decoding precision show that users were able to decode
continuous values with relatively high accuracy (4.4% mean
error), circular layouts performed particularly well, and personalisation
through sensitivity adjustment increased decoding
precision
Comparing driving behavior of humans and autonomous driving in a professional racing simulator.
Motorsports have become an excellent playground for testing the limits of technology, machines, and human drivers. This paper presents a study that used a professional racing simulator to compare the behavior of human and autonomous drivers under an aggressive driving scenario. A professional simulator offers a close-to-real emulation of underlying physics and vehicle dynamics, as well as a wealth of clean telemetry data. In the first study, the participants' task was to achieve the fastest lap while keeping the car on the track. We grouped the resulting laps according to the performance (lap-time), defining driving behaviors at various performance levels. An extensive analysis of vehicle control features obtained from telemetry data was performed with the goal of predicting the driving performance and informing an autonomous system. In the second part of the study, a state-of-the-art reinforcement learning (RL) algorithm was trained to control the brake, throttle and steering of the simulated racing car. We investigated how the features used to predict driving performance in humans can be used in autonomous driving. Our study investigates human driving patterns with the goal of finding traces that could improve the performance of RL approaches. Conversely, they can also be applied to training (professional) drivers to improve their racing line
Passive Haptic Learning for Vibrotactile Skin-Reading: Comparison of Teaching Structures
This paper investigates the effects of using passive haptic
learning to train the skill of reading text from vibrotactile
patterns. The vibrotactile method of transmitting messages,
skin-reading, is effective at conveying rich information but its
active training method requires full user attention, is demanding,
time-consuming, and tedious. Passive haptic learning
offers the possibility to learn in the background while performing
another primary task. We present a study investigating the
use of passive haptic learning to train for skin-reading. Additionally,
a word-based learning structure is typically used for
this passive learning method. We expose trends that suggest
this word-based incrimental teaching may not be optimal
Implementation Aspects of Anonymous Credential Systems for Mobile Trusted Platforms
Part 1: Research PapersInternational audienceAnonymity and privacy protection are very important issues for Trusted Computing enabled platforms. Protection mechanisms are required in order to hide activities of the trusted platforms when performing cryptography based transactions over the Internet, which would otherwise compromise the platform’s privacy and with it the users’s anonymity. In order to address this problem, the Trusted Computing Group (TCG) has introduced two concepts addressing the question how the anonymity of Trusted Platform Modules (TPMs) and their enclosing platforms can be protected. The most promising of these two concepts is the Direct Anonymous Attestation (DAA) scheme which eliminates the requirement of a remote authority but includes complex mathematical computations. Moreover, DAA requires a comprehensive infrastructure consisting of various components in order to allow anonymous signatures to be used in real-world scenarios. In this paper, we discuss the results of our analysis of an infrastructure for anonymous credential systems which is focused on the Direct Anonymous Attestation (DAA) scheme as specified by the TCG. For the analysis, we especially focus on mobile trusted platforms and their requirements. We discuss our experiences and experimental results when designing and implementing the infrastructure and give suggestions for improvements and propose concepts and models for - from our point of view - missing components
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data
This paper explores the use of reinforcement learning (RL) models for
autonomous racing. In contrast to passenger cars, where safety is the top
priority, a racing car aims to minimize the lap-time. We frame the problem as a
reinforcement learning task with a multidimensional input consisting of the
vehicle telemetry, and a continuous action space. To find out which RL methods
better solve the problem and whether the obtained models generalize to driving
on unknown tracks, we put 10 variants of deep deterministic policy gradient
(DDPG) to race in two experiments: i)~studying how RL methods learn to drive a
racing car and ii)~studying how the learning scenario influences the capability
of the models to generalize. Our studies show that models trained with RL are
not only able to drive faster than the baseline open source handcrafted bots
but also generalize to unknown tracks
Non-invasive brain–computer interfaces for control of grasp neuroprosthesis: The European MoreGrasp initiative
Restoration of grasping has the highest priority for people with cervical spinal cord injury (SCI). This chapter describes the non-invasive brain–computer interface (BCI)-controlled grasp neuroprosthesis developed within the European Horizon 2020 project MoreGrasp. Based on former projects of the collaborators, several innovative technologies were developed within the MoreGrasp project with the aim to achieve an intuitive thought-controlled restoration of hand function in end users with tetraplegia for supporting activities of daily living. The end users in the focus of this project have been people with sufficiently preserved elbow and shoulder movements, but missing hand and finger functions.
In particular, within MoreGrasp a novel, closed-loop upper limb grasp neuroprosthesis was developed which could be controlled by different multimodal control options, namely user-friendly BCIs based on gel-less electrodes and wireless electroencephalogram (EEG) amplifiers using natural movement attempt strategies, a shoulder joystick and instrumented objects. All these control modalities could be tailored to the end users’ needs and capabilities. Furthermore, a web-based service infrastructure for registration, assessment, and training of end users was developed. It assisted experimenters as well as end users in prototype assessment and operation. Finally, a clinical study involving end users with tetraplegia evaluating the MoreGrasp technology at their homes was initiated. The first results obtained and the lessons learned are provided at the end of the chapter