744,160 research outputs found
Movement perceived as chores or a source of joy : a phenomenological-hermeneutic study of physical activity and health
Physical activity has become the most documented and acknowledged health advice in relation to both staying healthy and regaining health both physically and mentally. Thus, physical activity in relation to spinal cord injury, low back pain and heart disease is respectively means to regain bodily function, avoid or reduce pain and early death. A second analysis of three studies with a phenomenological-hermeneutic approach building on Ricoeurâs philosophy on how people understand themselves and their world through narrative configurations revealed that physical activity had different meanings to people. This revealed that the meanings of physical activity could range from movements being unpleasant, maybe even painful to movements being a source of joy. This caused participants (1) to engage in movement as a source of joy, (2) to overcome the bodily struggle to do their chores, and maybe feel better as a result or (3) to minimize bodily functions equivalent to a functional daily life. Illustrated by 10 different approaches this provides knowledge about driving forces for health professional support. As joy and passion are the strongest driving forces to physical activity, this highlights the importance of supporting people to find a kind of physical activity that they like. Keywords: Low back pain, heart disease, narration, rehabilitation, physical activity, phenomology-hermeneutic, Ricoeur, spinal cord injurypublishedVersio
Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach
The dynamic nature of driving environments and the presence of diverse road
users pose significant challenges for decision-making in autonomous driving.
Deep reinforcement learning (DRL) has emerged as a popular approach to tackle
this problem. However, the application of existing DRL solutions is mainly
confined to simulated environments due to safety concerns, impeding their
deployment in real-world. To overcome this limitation, this paper introduces a
novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logics
(DRLSL) that combines the strengths of DRL (learning from experience) and
symbolic first-order logics knowledge-driven reasoning) to enable safe learning
in real-time interactions of autonomous driving within real environments. This
innovative approach provides a means to learn autonomous driving policies by
actively engaging with the physical environment while ensuring safety. We have
implemented the DRLSL framework in autonomous driving using the highD dataset
and demonstrated that our method successfully avoids unsafe actions during both
the training and testing phases. Furthermore, our results indicate that DRLSL
achieves faster convergence during training and exhibits better
generalizability to new driving scenarios compared to traditional DRL methods.Comment: 15 pages, 9 figures, 1 table, 1 algorithm. Under review as a journal
paper at IEEE transactions on Intelligent Transportation System
Experimental Security Analysis of DNN-based Adaptive Cruise Control under Context-Aware Perception Attacks
Adaptive Cruise Control (ACC) is a widely used driver assistance feature for
maintaining desired speed and safe distance to the leading vehicles. This paper
evaluates the security of the deep neural network (DNN) based ACC systems under
stealthy perception attacks that strategically inject perturbations into camera
data to cause forward collisions. We present a combined
knowledge-and-data-driven approach to design a context-aware strategy for the
selection of the most critical times for triggering the attacks and a novel
optimization-based method for the adaptive generation of image perturbations at
run-time. We evaluate the effectiveness of the proposed attack using an actual
driving dataset and a realistic simulation platform with the control software
from a production ACC system and a physical-world driving simulator while
considering interventions by the driver and safety features such as Automatic
Emergency Braking (AEB) and Forward Collision Warning (FCW). Experimental
results show that the proposed attack achieves 142.9x higher success rate in
causing accidents than random attacks and is mitigated 89.6% less by the safety
features while being stealthy and robust to real-world factors and dynamic
changes in the environment. This study provides insights into the role of human
operators and basic safety interventions in preventing attacks.Comment: 18 pages, 14 figures, 8 table
Detecting Moments of Stress from Measurements of Wearable Physiological Sensors
There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participantâs environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a âgold standardâ of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participantâs perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans
A proposed psychological model of driving automation
This paper considers psychological variables pertinent to driver automation. It is anticipated that driving with automated systems is likely to have a major impact on the drivers and a multiplicity of factors needs to be taken into account. A systems analysis of the driver, vehicle and automation served as the basis for eliciting psychological factors. The main variables to be considered were: feed-back, locus of control, mental workload, driver stress, situational awareness and mental representations. It is expected that anticipating the effects on the driver brought about by vehicle automation could lead to improved design strategies. Based on research evidence in the literature, the psychological factors were assembled into a model for further investigation
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