A Steering Wheel Mounted Grip Sensor: Design, Development and Evaluation

Department of Human Factors EngineeringDriving is a commonplace but safety critical daily activity for billions of people. It remains one of the leading causes of death worldwide, particularly in younger adults. In the last decades, a wide range of technologies, such as intelligent braking or speed regulating systems, have been integrated into vehicles to improve safetyannually decreasing death rates testify to their success. A recent research focus in this area has been in the development of systems that sense human states or activities during driving. This is valuable because human error remains a key reason underlying many vehicle accidents and incidents. Technologies that can intervene in response to information sensed about a driver may be able to detect, predict and ultimately prevent problems before they progress into accidents, thus avoiding the occurrence of critical situations rather than just mitigating their consequences. Commercial examples of this kind of technology include systems that monitor driver alertness or lane holding and prompt drivers who are sleepy or drifting off-lane. More exploratory research in this area has sought to capture emotional state or stress/workload levels via physiological measurements of Heart Rate Variability (HRV), Electrocardiogram (ECG) and Electroencephalogram (EEG), or behavioral measurements of eye gaze or face pose. Other research has monitored explicitly user actions, such as head pose or foot movements to infer intended actions (such as overtaking or lane change) and provide automatic assessments of the safety of these future behaviors ??? for example, providing a timely warning to a driver who is planning to overtake about a vehicle in his or her blind spot. Researchers have also explored how sensing hands on the wheel can be used to infer a driver???s presence, identity or emotional state. This thesis extends this body of work through the design, development and evaluation of a steering wheel sensor platform that can directly detect a driver???s hand pose all around a steering wheel. This thesis argues that full steering hand pose is a potentially rich source of information about a driver???s intended actions. For example, it proposes a link between hand posture on the wheel and subsequent turning or lane change behavior. To explore this idea, this thesis describes the construction of a touch sensor in the form of a steering wheel cover. This cover integrates 32 equidistantly spread touch sensing electrodes (11.250 inter-sensor spacing) in the form of conductive ribbons (0.2" wide and 0.03" thick). Data from each ribbons is captured separately via a set of capacitive touch sensor microcontrollers every 64 ms. We connected this hardware platform to an OpenDS, an open source driving simulator and ran two studies capturing hand pose during a sequential lane change task and a slalom task. We analyzed the data to determine whether hand pose is a useful predictor of future turning behavior. For this we classified a 5-lane road into 4 turn sizes and used machine-learning recognizers to predict the future turn size from the change in hand posture in terms of hand movement properties from the early driving data. Driving task scenario of the first experiment was not appropriately matched with the real life turning task therefore we modified the scenario with more appropriate task in the second experiments. Class-wise prediction of the turn sizes for both experiments didn???t show good accuracy, however prediction accuracy was improved when the classes were reduced into two classes from four classes. In the experiment 2 turn sizes were overlapped between themselves, which made it very difficult to distinguish them. Therefore, we did continuous prediction as well and the prediction accuracy was better than the class-wise prediction system for the both experiments. In summary, this thesis designed, developed and evaluated a combined hardware and software system that senses the steering behavior of a driver by capturing grip pose. We assessed the value of this information via two studies that explored the relationship between wheel grip and future turning behaviors. The ultimate outcome of this study can inform the development of in car sensing systems to support safer driving.ope

Testing the efficacy of vision training for presbyopia: alternating-distance training does not facilitate vision improvement compared to fixed-distance training

Purpose Current evidence demonstrates the effectiveness of vision training for presbyopia. We developed and examined a training program to test the effectiveness of alternating focal distances as a training method. Methods We devised a sharpness discrimination task, in which participants judged whether the stimulus was a sine- or square-wave grating, and tested in two training groups and one control group. In the alternating-distance training group (N = 8, age 49-64), participants had to alternate the fixation between a near- and far-screen. In the fixed-distance training group (N=8, age 47-65), participants fixated on the same-distance target for the whole block. Before and after the 20 training sessions, we measured the near- and far-visual acuity (VA) using the Landolt C and Early Treatment Diabetic Retinopathy Study (ETDRS) tasks and contrast sensitivity using the qCSF procedure. The control group (N=8, age 49-65) participated only in the pre- and post-tests. Results Both training groups showed a significant improvement between the pre- and post-tests in the Landolt C task, and the improvement sizes were not significantly different between the groups. In the ETDRS task, only the fixed-distance training group showed significant improvement, although there was no significant difference between the two groups. Neither group showed improvement in the contrast sensitivity task compared to the control group. Conclusion The novel sharpness discrimination task can be an effective training method for presbyopia to prevent the deterioration of VA; however, contrary to popular belief, the effect of alternating-distance training was comparable to or even weaker than that of fixed-distance training

GlassPass: Tapping Gestures to Unlock Smart Glasses

Wearable technologies such as smart-glasses can sense, store and display sensitive personal contents. In order to protect this data, users need to securely authenticate to their devices. However, current authentication techniques, such as passwords or PINs, are a poor fit for the limited input and output spaces available on wearables. This paper focuses on eyewear and addresses this problem with a novel authentication system that uses an alphabet of simple tapping patterns optimized for rapid and accurate input on the temples (or arms) of glasses. Furthermore, it explores how an eyewear display can support password memorization by privately presenting a visualization of entered symbols. A pair of empirical studies confirm that performance during input of both individual password symbols and full passwords is rapid and accurate. A follow-up session one week after the main study suggests using a private display to show entered password symbols effectively supports memorization