In this paper, we demonstrate the first attempt of encapsulating a flexible micro battery into a contact lens to implement an eye-tracker. The paper discusses how to scale the battery to power various circuits embedded in the contact lens, such as ASIC, photodiodes, etc., as well as how to combine the battery with external harvested energy sources. The fabricated ring battery has a surface area of 0.75 cm2 yielding an areal capacity of 43 µAh·cm−2 at 20C. Based on simulated 0.35-µm CMOS ASIC power consumption, this value is large enough to allow powering the ASIC for 3 minutes. The functioning of the micro battery is demonstrated by powering an orange LED.
We present the design, fabrication, and test of a multipurpose integrated circuit (Application Specific Integrated Circuit) in AMS 0.35 µm Complementary Metal Oxide Semiconductor technology. This circuit is embedded in a scleral contact lens, combined with photodiodes enabling the gaze direction detection when illuminated and wirelessly powered by an eyewear. The gaze direction is determined by means of a centroid computation from the measured photocurrents. The ASIC is used simultaneously to detect specific eye blinking sequences to validate target designations, for instance. Experimental measurements and validation are performed on a scleral contact lens prototype integrating four infrared photodiodes, mounted on a mock-up eyeball, and combined with an artificial eyelid. The eye-tracker has an accuracy of 0.2°, i.e., 2.5 times better than current mobile video-based eye-trackers, and is robust with respect to process variations, operating time, and supply voltage. Variations of the computed gaze direction transmitted to the eyewear, when the eyelid moves, are detected and can be interpreted as commands based on blink duration or using blinks alternation on both eyes.
Liquid crystal (LC) tunable lenses have been extensively studied and used in various applications, however, most of them have been evaluated regardless of their optical imaging quality, in particular, concerning their intrinsic diffuse scattering. In this paper, we investigate the impact of such impairments when LC lenses are used as tunable elements in a depth-from-focus algorithm (DfF). We attempt to analyze these effects in order to design LC lenses that mitigate their impact on the imaging quality. For this purpose, we designed various lenses to evaluate several parameters such as optical, electrical, manufacturing, etc., according to their implementations in a near-pixel DfF architecture.
Recording a video scene as seen by an observer, materializing where is focused his visual attention and allowing an external person to point at a given object in this scene, could be beneficial for various applications such as medical education or remote training. Such a versatile device, although tested at the experimental laboratory demonstrator stage, has never been integrated in a compact and portable way in a real environment. In this context, we built a low-cost, light-weight, head-mounted device integrating a miniature camera and a laser pointer that can be remotely controlled or servo-controlled by an eye tracker. Two motorizations were implemented and tested (pan/tilt and Rilsey-prisms-based). The video was both recorded locally and transmitted wirelessly. Risley prisms allowed finer remote control of camera or laser pointer orientation (0.1° vs. 0.35°), but data processing and Wi-Fi transmission incur significant latency (~0.5 s) limiting the servo-controlling by eye movements. The laser beam was spatially shaped by a Diffractive Optical Element to facilitate object illumination or recognition. With this first proof-of-concept prototype, the data stream needs to be optimized to make full use of the eye tracker, but this versatile device can find various applications in education, healthcare or research.
Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field.
Augmented reality (AR) is expected to play an increasing role in numerous professional fields, such as medicine, aviation, and especially in industry. Given its potential to improve training in assembly task, many studies have tried to assess the effectiveness of AR using various objective or subjective criteria. The aim of this article is to review what is currently known about the use of AR for assembly training. The first part of this paper presents a definition of AR and the different AR devices that are currently proposed. The second part briefly describes the different applications of AR and its interest for industrial assembly processes. In the third part, we propose a literature review of studies published over the last 20 years, on the effectiveness of presenting assembly instructions using AR. Our analysis suggests that effectiveness is mainly evaluated using three criteria: time to complete the assembly, number of assembly errors and mental workload of the user. Data from these studies show that the presentation of assembly instructions in AR can reduce the number of assembly errors, but is not systematically effective in shortening the time of the overall assembly task or in reducing the mental workload of the user. Thus, there is currently no consensual conclusions about the effectiveness of AR for training. In the last part of this article, we suggest that this lack of conclusive data could be explained by a lack of unified and comparable methodology between studies. We identify and discuss four factors that differ between the reviewed studies, such as heterogeneity in the population studied, the type of assembly task used, the AR systems tested but also evaluate AR for guidance or learning purpose. In addition, most of the studies evaluated test situations that are not representative enough of an industrial context. In conclusion, we emphasise that further studies on the impact of AR integration for training directly in industry are needed. Without more representative data on AR, its integration must be done with caution and could not completely replace a human instructor.