How Older Drivers Perceive Warning Alerts? Insights for the Design of Driver-Car Interaction.

The automotive industry is working toward driving automation and driver-assistance technology is becoming a norm in modern cars. Warning alert systems support the driver-car interaction and inform drivers about automation system status, upcoming obstacles, or dangers ahead. However, older drivers' needs are not always addressed in research studies, although they make up a large segment of drivers. Therefore, we conducted a qualitative three-round formative evaluation of a warning alert system using video prototypes in lab and remote settings. The goal was to evaluate visual-, sound-, and speech-based alerts based on: (a) their efficiency in informing drivers about the road situation ahead, and (b) participants' subjective opinions. We evaluated the system's efficiency using self-reported data measuring participants' cognitive load, usability, UX, and ease of use. Also, we conducted interviews to collect subjective feedback about proposed prototypes. In this article, we describe the design of warning alerts and report on their evaluation results. Our results show that speech-based warnings, especially when coupled with visual warnings, are efficient and accepted well by the participants. This article illustrates older drivers' attitude toward the use of different warning modalities in the driving context.

Feature extraction and selection for objective gait analysis and fall risk assessment by accelerometry.

BACKGROUND: Falls in the elderly is nowadays a major concern because of their consequences on elderly general health and moral states. Moreover, the aging of the population and the increasing life expectancy make the prediction of falls more and more important. The analysis presented in this article makes a first step in this direction providing a way to analyze gait and classify hospitalized elderly fallers and non-faller. This tool, based on an accelerometer network and signal processing, gives objective informations about the gait and does not need any special gait laboratory as optical analysis do. The tool is also simple to use by a non expert and can therefore be widely used on a large set of patients. METHOD: A population of 20 hospitalized elderlies was asked to execute several classical clinical tests evaluating their risk of falling. They were also asked if they experienced any fall in the last 12 months. The accelerations of the limbs were recorded during the clinical tests with an accelerometer network distributed on the body. A total of 67 features were extracted from the accelerometric signal recorded during a simple 25 m walking test at comfort speed. A feature selection algorithm was used to select those able to classify subjects at risk and not at risk for several classification algorithms types. RESULTS: The results showed that several classification algorithms were able to discriminate people from the two groups of interest: fallers and non-fallers hospitalized elderlies. The classification performances of the used algorithms were compared. Moreover a subset of the 67 features was considered to be significantly different between the two groups using a t-test. CONCLUSIONS: This study gives a method to classify a population of hospitalized elderlies in two groups: at risk of falling or not at risk based on accelerometric data. This is a first step to design a risk of falling assessment system that could be used to provide the right treatment as soon as possible before the fall and its consequences. This tool could also be used to evaluate the risk several times during the revalidation procedure.

What individuals believe they can and cannot do: explorations of realistic and unrealistic control beliefs.

We examined construct validity and implications of scales that assess perceived control over controllable events (realistic control belief) and perceived control over uncontrollable events (unrealistic control belief). We found that people high in unrealistic control belief are less likely to discriminate between controllable and uncontrollable situations and to distort task outcomes so as to make them appear more favorable (more under control). We also found that people high in realistic control belief are more likely to assume responsibility for past mistakes. In additional studies on coping and health risk behavior, higher realistic control belief was related to direct engagement with the stressor and to less risky sexual behavior; higher unrealistic control belief was related to less emotional processing and to more risky sexual behavior. The results demonstrate a need to discriminate between the two types of control beliefs.

Testing discrepancy effects: a critique, a suggestion, and an illustration.

To assess the effects of discrepancy between two independent variables, investigators sometimes compute difference scores and correlate such scores with a criterion variable. However, the correlation of the difference with the criterion is accounted for by the correlations of the difference constituents with the criterion and the constituents' variances. It follows that when investigators are testing a prediction that is not captured by the difference constituents' main effects, using the difference correlation analysis may be misleading. Under these circumstances, the effects of a discrepancy between two independent variables can be assessed by a test of their interaction. The problems inherent in using difference scores and the advantage of testing the interaction are illustrated in relation to research programs on two separate topics in social psychology.