Smart Eyeglasses Research Articles

A Clustering-Based Approach to Functional and Biomechanical Parameters Recorded with a Pair of Smart Eyeglasses in Older Adults in Order to Determine Physical Performance Groups.

Falls and frailty status are often associated with a decline in physical capacity and multifactorial assessment is highly recommended. Based on the functional and biomechanical parameters measured during clinical tests with an accelerometer integrated into smart eyeglasses, the purpose was to characterize a population of older adults through an unsupervised analysis into different physical performance groups. A total of 84 participants (25 men and 59 women) over the age of sixty-five (age: 74.17 ± 5.80 years; height: 165.70 ± 8.22 cm; body mass: 68.93 ± 13.55 kg) performed a 30 s Sit-to-Stand test, a six-minute walking test (6MWT), and a 3 m Timed Up and Go (TUG) test. The acceleration data measured from the eyeglasses were processed to obtain six parameters: the number of Sit-to-Stands, the maximal vertical acceleration values during Sit-to-Stand movements, step duration and length, and the duration of the TUG test. The total walking distance covered during the 6MWT was also retained. After supervised analyses comparison (i.e., ANOVAs), only one of the parameters (i.e., step length) differed between faller groups and no parameters differed between frail and pre-frail participants. In contrast, unsupervised analysis (i.e., clustering algorithm based on K-means) categorized the population into three distinct physical performance groups (i.e., low, intermediate, and high). All the measured parameters discriminated the low- and high-performance groups. Four of the measured parameters differentiated the three groups. In addition, the low-performance group had a higher proportion of frail participants. These results are promising for monitoring activities in older adults to prevent the decline of physical capacities.

The challenge of measuring physiological parameters during motor imagery engagement in patients after a stroke.

It is suggested that eye movement recordings could be used as an objective evaluation method of motor imagery (MI) engagement. Our investigation aimed to evaluate MI engagement in patients after stroke (PaS) compared with physical execution (PE) of a clinically relevant unilateral upper limb movement task of the patients' affected body side. In total, 21 PaS fulfilled the MI ability evaluation [Kinaesthetic and Visual Imagery Questionnaire (KVIQ-10), body rotation task (BRT), and mental chronometry task (MC)]. During the experiment, PaS moved a cup to distinct fields while wearing smart eyeglasses (SE) with electrooculography electrodes integrated into the nose pads and electrodes for conventional electrooculography (EOG). To verify MI engagement, heart rate (HR) and oxygen saturation (SpO2) were recorded, simultaneously with electroencephalography (EEG). Eye movements were recorded during MI, PE, and rest in two measurement sessions to compare the SE performance between conditions and SE's psychometric properties. MI and PE correlation of SE signals varied between r = 0.12 and r = 0.76. Validity (cross-correlation with EOG signals) was calculated for MI (r = 0.53) and PE (r = 0.57). The SE showed moderate test-retest reliability (intraclass correlation coefficient) with r = 0.51 (95% CI 0.26-0.80) for MI and with r = 0.53 (95% CI 0.29 - 0.76) for PE. Event-related desynchronization and event-related synchronization changes of EEG showed a large variability. HR and SpO2 recordings showed similar values during MI and PE. The linear mixed model to examine HR and SpO2 between conditions (MI, PE, rest) revealed a significant difference in HR between rest and MI, and between rest and PE but not for SpO2. A Pearson correlation between MI ability assessments (KVIQ, BRT, MC) and physiological parameters showed no association between MI ability and HR and SpO2. The objective assessment of MI engagement in PaS remains challenging in clinical settings. However, HR was confirmed as a reliable parameter to assess MI engagement in PaS. Eye movements measured with the SE during MI did not resemble those during PE, which is presumably due to the demanding task. A re-evaluation with task adaptation is suggested.

Quantitative evaluation of blinking in blepharospasm using electrooculogram-integrated smart eyeglasses

Smart eyeglasses with an integrated electrooculogram (EOG) device (JINS MEME ES_R®, JINS Inc.) were evaluated as a quantitative diagnostic tool for blepharospasm. Participants without blepharospasm (n = 21) and patients with blepharospasm (n = 19) undertook two voluntary blinking tests (light and fast) while wearing the smart eyeglasses. Vertical (Vv) and horizontal (Vh) components were extracted from time-series voltage waveforms recorded during 30 s of the blinking tests. Two parameters, the ratio between the maximum and minimum values in the power spectrum (peak-bottom ratio, Fourier transform analysis) and the mean amplitude of the EOG waveform (peak amplitude analysis) were calculated. The mean amplitude of Vh from light and fast blinking was significantly higher in the blepharospasm group than in the control group (P < 0.05 and P < 0.05). Similarly, the peak-bottom ratio of Vv from light and fast blinking was significantly lower in the blepharospasm group than in the control group (P < 0.05 and P < 0.05). The mean amplitude of Vh and peak-bottom ratio of Vv correlated with the scores determined using the Jankovic rating scale (P < 0.05 and P < 0.01). Therefore, these parameters are sufficiently accurate for objective blepharospasm classification and diagnosis.

Applying the UTAUT2 Model to Smart Eyeglasses to Detect and Prevent Falls Among Older Adults and Examination of Associations With Fall-Related Functional Physical Capacities: Survey Study.

As people age, their physical capacities (eg, walking and balance) decline and the risk of falling rises. Yet, classic fall detection devices are poorly accepted by older adults. Because they often wear eyeglasses as they go about their daily activities, daily monitoring to detect and prevent falls with smart eyeglasses might be more easily accepted. On the basis of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), this study evaluated (1) the acceptability of smart eyeglasses for the detection and prevention of falls by older adults and (2) the associations with selected fall-related functional physical capacities. A total of 142 volunteer older adults (mean age 74.9 years, SD 6.5 years) completed the UTAUT2 questionnaire adapted for smart eyeglasses and then performed several physical tests: a unipodal balance test with eyes open and closed, a 10-m walk test, and a 6-minute walk test. An unsupervised analysis classified the participants into physical performance groups. Multivariate ANOVAs were performed to identify differences in acceptability constructs according to the performance group. The UTAUT2 questionnaire adapted for eyeglasses presented good psychometric properties. Performance expectancy (β=.21, P=.005), social influence (β=.18, P=.007), facilitating conditions (β=.17, P=.04), and habit (β=.40, P<.001) were significant contributors to the behavioral intention to use smart eyeglasses (R²=0.73). The unsupervised analysis based on fall-related functional physical capacities created 3 groups of physical performance: low, intermediate, and high. Effort expectancy in the low performance group (mean 3.99, SD 1.46) was lower than that in the other 2 groups (ie, intermediate: mean 4.68, SD 1.23; high: mean 5.09, SD 1.41). Facilitating conditions in the high performance group (mean 5.39, SD 1.39) were higher than those in the other 2 groups (ie, low: mean 4.31, SD 1.68; intermediate: mean 4.66, SD 1.51). To our knowledge, this study is the first to examine the acceptability of smart eyeglasses in the context of fall detection and prevention in older adults and to associate acceptability with fall-related functional physical capacities. The older adults with higher physical performances, and possibly lower risks of falling, reported greater acceptability of smart eyeglasses for fall prevention and detection than their counterparts exhibiting low physical performances.

Dynamic correction of astigmatism.

For the correction of defocus and astigmatism, mechanical approaches are well known, but there is a need for a non-mechanical, electrically tunable optical system that could provide both focus and astigmatism power correction with an adjustable axis. The optical system presented here is composed of three liquid-crystal-based tunable cylindrical lenses that are simple, low cost, and having a compact structure. Potential applications of the concept device include smart eyeglasses, virtual reality (VR)/ augmented reality (AR)head-mounted displays (HMDs), and optical systems subject to thermal or mechanical distortion. Details of the concept, design method, numerical computer simulations of the proposed device, as well as characterization of a prototype, are provided in this work.

Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait.

The study aims to determine the validity and reproducibility of step duration and step length parameters measured during walking in healthy participants using an accelerometer embedded in smart eyeglasses. Twenty young volunteers participated in two identical sessions comprising a 30 s gait assessment performed at three different treadmill speeds under two conditions (i.e., with and without a cervical collar). Spatiotemporal parameters (i.e., step duration and step length normalized by the lower limb length) were obtained with both the accelerometer embedded in smart eyeglasses and an optoelectronic system. The relative intra- and inter-session reliability of step duration and step length computed from the vertical acceleration data were excellent for all experimental conditions. An excellent absolute reliability was observed for the eyeglasses for all conditions and concurrent validity between systems was observed. An accelerometer incorporated in smart eyeglasses is accurate to measure step duration and step length during gait.

Development smart eyeglasses for visually impaired people based on you only look once

Visually impaired people are facing many problems in their life. One of these problems is how they can find the objects in their indoor environment. This research was presented to assists visually impaired people in finding the objects in office. Object detection is a method used to detect the objects in images and videos. Many algorithms used for object detection such as convolutional neural network (CNN) and you only look once (YOLO). The proposed method was YOLO which outperforms the other algorithms such as CNN. In CNN the algorithm splits the image into regions. These regions sequentially enters the neural network for object detection and recognition so CNN does not deal with all the regions at the same time but YOLO looks the entire image then it produces the bounding boxes with convolutional network and the probabilities of these boxes, this makes YOLO faster than other algorithms. Open source computer vision (OpenCV) used to capture frames by using camera. Then YOLO used to detect and recognize the objects in each frame. Finally, the sound in Arabic language was generated to tell the visually impaired people about the objects. The proposed system can detect 6 objects and achieve an accuracy of 99%.

Context-Adaptive Sub-Nyquist Sampling for Low-Power Wearable Sensing Systems

This paper investigates a context-adaptive sample acquisition strategy at sub-Nyquist sampling rate for wearable embedded sensor devices. Our approach can be applied to compressive sensing frameworks to minimise sampling and transmission costs. We consider a context estimate to represent the local signal structure and a feed-forward response model to continuously tune signal acquisition of an online sampling and transmission system. To evaluate our approach, we analysed the performance in different pattern recognition scenarios. We report three case studies here: (1) eating monitoring based on electromyography measurements in smart eyeglasses, (2) human activity recognition based on waist-worn inertial sensor data, and (3) heartbeat detection and arrhythmia classification based on single-lead electrocardiogram readings. Compared to conventional sub-Nyquist sampling, our context-adaptive approach saves between 13 to 22 percent of energy, while achieving similar pattern recognition performance and reconstruction error.

Sit-To-Stand Movement Evaluated Using an Inertial Measurement Unit Embedded in Smart Glasses-A Validation Study.

Wearable sensors have recently been used to evaluate biomechanical parameters of everyday movements, but few have been located at the head level. This study investigated the relative and absolute reliability (intra- and inter-session) and concurrent validity of an inertial measurement unit (IMU) embedded in smart eyeglasses during sit-to-stand (STS) movements for the measurement of maximal acceleration of the head. Reliability and concurrent validity were investigated in nineteen young and healthy participants by comparing the acceleration values of the glasses’ IMU to an optoelectronic system. Sit-to-stand movements were performed in laboratory conditions using standardized tests. Participants wore the smart glasses and completed two testing sessions with STS movements performed at two speeds (slow and comfortable) under two different conditions (with and without a cervical collar). Both the vertical and anteroposterior acceleration values were collected and analyzed. The use of the cervical collar did not significantly influence the results obtained. The relative reliability intra- and inter-session was good to excellent (i.e., intraclass correlation coefficients were between 0.78 and 0.91) and excellent absolute reliability (i.e., standard error of the measurement lower than 10% of the average test or retest value) was observed for the glasses, especially for the vertical axis. Whatever the testing sessions in all conditions, significant correlations (p < 0.001) were found for the acceleration values recorded either in the vertical axis and in the anteroposterior axis between the glasses and the optoelectronic system. Concurrent validity between the glasses and the optoelectronic system was observed. Our observations indicate that the IMU embedded in smart glasses is accurate to measure vertical acceleration during STS movements. Further studies should investigate the use of these smart glasses to assess the STS movement in unstandardized settings (i.e., clinical and/or home) and to report vertical acceleration values in an elderly population of fallers and non-fallers.

Potential of Smart Glasses in Manual Order Picking Systems

Full automation of order picking “man-to-goods” workplaces still will not be rational in the near future. Humans will stay central actors and as such, they will determine system’s effectiveness and efficiency. Order picking activities are labour intensive and time-consuming. Smart glasses are one of the possibilities to relieve order-picker’s hands, resulting in reduced time consumption. In the paper, we review scientific literature on use of smart glasses in manual order picking system and outline the opportunities for future research. Available scientific papers on smart glasses topic deal with unique environments characterized by layout, equipment, process, specific combination of technologies etc. Most authors agree that their findings cannot be generalized. There are no studies in the literature on the effects of multiple-hour use of smart eyeglasses on the eyes. Warehouse systems are mostly not identical, even employees have different characteristics and attentiveness to technology. It is proposed to test each system before implementation and try to combine technologies for best results. Different systems tends to different goals, for example fast service or zero errors. The production system’s outputs are also very likely influenced by software and user interface design, which indicates the need for additional research. The growing number of studies certainly have a positive impact on clarifying the unexplored. The area of smart glasses currently represents a great research potential.

An Empirical Approach to Phishing Countermeasures Through Smart Glasses and Validation Agents

Phishing attacks have been persistent for more than two decades despite mitigation efforts from academia and industry. We believe that users fall victim to attacks not only because of lack of knowledge and awareness, but also because they are not attentive enough to security indicators and visual abnormalities on the webpages they visit. This is also probably why smart device users, who have more limited screen size and device capabilities compared to desktop users, are three times more likely to fall victim to phishing attacks. To assert our claim, we first investigated general phishing awareness among different groups of smartphone users. We then used smart eyeglasses (electro-oculographic) to experimentally measure the mental effort and vigilance exhibited by users while surfing a website and while playing an Android phishing game that we developed. The results showed that knowledge and awareness about phishing do not seem to have a significant impact on security behaviours, as knowledgeable participants exhibited insecure behaviours such as opening email attachments from unfamiliar senders. However, attentiveness was important as even participants with low cybersecurity knowledge could effectively identify attacks if they were reasonably attentive. Based on these results, we asserted that users are more likely to continue falling victim to phishing attacks due to insecure behaviours, unless tools to lessen the identification burden are provided. We thus recommended implementing a lightweight algorithm into a custom Android browser for detecting phishing sites deceptively without a user interaction. We used fake login credentials as validation agents and monitor the destination server HTTP responses to determine the authenticity of a webpage. We also presented initial evaluation results of this algorithm.

A Low-Profile Digital Eye-Tracking Oculometer for Smart Eyeglasses.

Wearable eye tracking devices have broad uses in medicine, psychology, augmented & virtual reality and consumer market research. Most mobile eye trackers available today utilize infrared imaging of the pupil and corneal reflections with video cameras. This tracking method requires sophisticated real-time processing of video signals consuming substantial electrical power. This method is thus unsuitable for light weight wearables such as adaptive smart eyeglasses for correction of presbyopia. In this paper we present a low-profile, low-power (7.7 mJ/sample) digital eye tracker oculometer based on infrared sclera tracking. The system is implemented using eight, 24-bit infrared proximity sensors and synchronous infrared LEDs. The pupil location is determined from 32 reflected pulsed light measurements independent of ambient illumination. The digital oculometer is 3.1 mm thick and weighs ~3 g. The tracker mounts adjacent to the tunable lenses in the smart eyeglasses frame. The eye tracker showed a pointing error of 1.3 degrees rms over a vertical and horizontal range of 30 degrees when tested by an observer.

Sixth Sense Technology

This paper discusses recent and unique inventions in Human Computer Interaction (HCI). To that end, firstly the authors discuss the Sixth Sense Technology. This technology allows users to interact with virtual objects in the real world in a unique manner. It has a number of applications which are further discussed. Then the opportunities and challenges are discussed. Most importantly, a list of inventions in fields of Augmented Reality (AR) and Virtual Reality (VR) in the recent years are discussed, grouped and compared. These include the smart eye glasses, VR headsets, smart watches, and more. Future implications of all those technologies are brought into light considering the new advancements in software and hardware designs. Recommendations are highlighted for future inventions.

Computer Screen Use Detection Using Smart Eyeglasses

Screen use can influence the circadian phase and cause eye strain. Smart eyeglasses with an integrated colour light sensor can detect screen use. We present a screen use detection approach based on a light sensor embedded into the bridge of smart eyeglasses. By calculating the light intensity at the user’s eyes for different screens and content types we found only computer screens to have significant impact on the circadian phase. Our screen use detection is based on ratios between colour channels and used a linear support vector machine to detect screen use. We validated our detection approach in three studies. A Test bench was built to detect screen use under different ambient light sources and intensities in a controlled environment. In a Lab study, we evaluated recognition performance for different ambient light intensities. Using participant-independent models we achieved a ROC AUC above 0.9 for ambient light intensities below 200 lux. In a study of typical ADLs screen use was detected with an average ROC AUC of 0.83 assuming screen use for 30 % of the time.

Monitoring Chewing and Eating in Free-Living Using Smart Eyeglasses.

We propose to 3-D-print personal fitted regular-look smart eyeglasses frames equipped with bilateral electromyography recording to monitor temporalis muscles' activity for automatic dietary monitoring. Personal fitting supported electrode-skin contacts are at temple ear bend and temple end positions. We evaluated the smart monitoring eyeglasses during in-lab and free-living studies of food chewing and eating event detection with ten participants. The in-lab study was designed to explore three natural food hardness levels and determine parameters of an energy-based chewing cycle detection. Our free-living study investigated whether chewing monitoring and eating event detection using smart eyeglasses is feasible in free-living. An eating event detection algorithm was developed to determine intake activities based on the estimated chewing rate. Results showed an average food hardness classification accuracy of 94% and chewing cycle detection precision and recall above 90% for the in-lab study and above 77% for the free-living study covering 122hours of recordings. Eating detection revealed the 44 eating events with an average accuracy above 95%. We conclude that smart eyeglasses are suitable for monitoring chewing and eating events in free-living and even could provide further insights into the wearer's natural chewing patterns.

Smart contact lens and smart eye glasses

Event Abstract Back to Event Smart contact lens and smart eye glasses Sei Kwang Hahn1 and Do Hee Keum1* 1 Pohang University of Science and Technology, Materials Science and Engineering, Korea Introduction: Contact lens has been widely investigated for biosensing and drug delivery applications to ocular diseases. Here, we developed a smart contact lens containing biosensor and drug delivery system, and a smart eye glasses for the treatment of diabetes as a model disease. Materials and Methods: For the diagnosis of diabetes, tear glucose content was measured as a non-invasive alternative for the blood glucose content monitoring. The Ti electrode of biosensor was immobilized with glucose oxidase. When glucose binds to glucose oxidase, hydrogen peroxide is produced and oxidized on the electrode generating electrical current. For the drug delivery system, we fabricated Au membrane coated drug reservoirs on contact lens. Each reservoir was sealed at one end with a thin membrane of gold anode. When we applied electrical current, Au membrane was dissolved into AuCl4- in NaCl solution. This flexible, biocompatible and miniaturized device was wirelessly powered by the WiTricity system of transmitter coils in smart eyeglasses and receiver coils in smart contact lens. Results: The electrical current from the glucose sensor increased from 10 microA to 200 microA with increasing tear glucose level ranging from 0.05 mM to 0.8 mM. Glucose sensor could be repeatedly used for 3 weeks in tear solution without lens corrosions and big current decrease. The thin gold anode was dissolved immediately in PBS solution by applying electrical voltage, triggering the release of drug from the reservoir. The transmitter circuit generated sine wave signals and MOSFET driver in SOIC8 package drived coils up to 60 V peaks on sine wave. For the power density calculation, the measured absolute value of the optical power was divided to the detector active area (1 cm2). A proper power was transferred to the receiver and sufficient energy was available to power the glucose sensor, drug delivery system, and communication circuitry. Discussion: Biosensor can transmit the detected ocular signals to the smart eyeglasses for future processing and diagnostics applications. A miniaturized ocular drug-delivery system was also automatically controlled using the smart eyeglasses by user voice command. The efficient wireless power transfer system and the miniaturized microelectronic integrated circuit using CMOS and NEMS technologies will introduce a new alternative platform for future diagnostic and therapeutic applications, and human-machine interface healthcare applications. This novel smart contact lens can be further developed as a platform for the futuristic nano clinic systems. Conclusions: We successfully developed smart contact lens which is composed of glucose biosensor, on-demand drug delivery MEMS system, and remote power systems. Biosensor could measure tear glucose level in real-time for the diagnosis of diabetes, electrically controllable pulsatile DDS was applicable to deliver the drugs into the eyes, and miniaturized electrical power system made possible to drive flexible portable devices. This smart contact lens can be further developed for various theranostic applications.

Development of the System to Support Tourists’ Excursion Behavior using Augmented Reality

The purpose of this study is to develop an information system (AR recommended GIS) to support tourists’ excursion behavior by making the accumulating, sharing, and recommending of information concerning urban tourist spots possible. The conclusion of this study can be summarized into three points as shown below. (1) In order to support tourists’ excursion behaviors by integrating SNS, Twitter, Web-GIS, recommendation system, and Smart Eyeglass, in addition to making the accumulating, sharing, and recommending of information regarding urban tourist spots possible, the AR recommended GIS was designed and developed. (2) 91% were between the age of 20-40 among the 91 users, and the ultimate number of submitted information was 161. In addition, concerning the operation using Smart Eyeglass, which was conducted with tourists in the Minato Mirai area, the total number of users were 34, age of users were spread out, and all users had no experience in using Smart Eyeglasses. (3) From the results of the Web questionnaire survey, the system is compatible for the collection method of tourist spot information for users, and is mainly used to collect tourist spot information using the viewing and recommendation functions. From the results of the access analysis using the log data form during the operation, the utilization method of the system with PCs and mobile information terminals were very similar. Additionally, as the system using AR Smart Eyeglass was rated extremely highly, it was evident that it is possible to support tourists’ excursion behavior using PCs, mobile information terminals, and AR Smart Eyeglasses are possible.

Making Regular Eyeglasses Smart

The authors discuss the vast application potential of multipurpose smart eyeglasses that integrate into the form factor of traditional spectacles and provide frequently needed sensing and interaction. In combination with software apps running on smart eyeglasses, the authors develop universal assistance systems that remain unobtrusive and thus can support wearers throughout their daily life. They describe a blueprint of the embedded architecture of smart eyeglasses and identify various software app clusters. They discuss findings from using smart eyeglasses prototypes in three case studies: to recognize cognitive workload, quantify reading habits, and monitor light exposure to estimate the circadian phase. This article is part of a special issue on digitally enhanced reality.

WISEglass: Smart eyeglasses recognising context

We investigated how regular eyeglasses could be extended with multi-modal sensing and processing functions to support context-awareness applications. Our aim was to leverage eyeglasses as a platform for acquiring and processing context information according to the wearer’s needs. The WISEglass architecture consists of inertial motion, environmental light, and pulse sensors, processing and wireless data transmission functionality, besides a rechargeable battery. We implemented prototypes of WISEglass and evaluated them in three application scenarios: daily activity recognition, screen-use detection, and heart rate estimation. We conducted a daily activity study with nine participants, each wearing WISEglass and recording for one day. When evaluating daily activity recognition, we obtained 77 % average accuracy for continuous recognition using Gaussian Mixture Models and classifier reject to ignore null class data. Using the light sensor for detecting screen-use, yielded 80 % accuracy. Against a chest-worn ECG reference, our heart rate estimation showed an difference below 10 beats for stationary activities across the full recording day. We concluded that smart eyeglasses provide information from a single measurement spot that is relevant in various context recognition applications.

Materials On Display

By simultaneously exploiting a material’s electronic, optical, and structural properties, researchers in the U.K. have taken a first step toward developing a new type of electronic display technology. The advance may lead to low-cost, flexible, color display devices that outperform today’s technology in terms of resolution, power consumption, and response time (Nature 2014, DOI: 10.1038/nature13487). Future applications may include smart eyeglasses and contact-lens-type displays. At the heart of the study lies a well-known phase-change material, an alloy of germanium, antimony, and tellurium—Ge2Sb2Te5 (GST). Phase-change materials respond to stimuli such as heat or an electric pulse by switching between two solid states—amorphous and crystalline. This type of structural change, which has been studied in detail, underpins data storage in rewritable CDs, DVDs, and other types of commercial data storage devices. The change in these materials’ lattice structure is accompanied by other changes—for example, in ...