Modern Smartphones Research Articles

Smartphone scanning is a reliable and accurate alternative to contemporary residual limb measurement techniques.

Monitoring the volume and shape of residual limbs post-amputation is necessary to achieve optimal socket fit and determine overall limb health, yet contemporary clinical measurement techniques show high variance between measures. Three-dimensional scanning presents an opportunity for improved accuracy and reliability of residual limb measurements, however, three-dimensional scanners remain prohibitively expensive. A cost-effective alternative is the use of software that can utilise the photographs of modern smartphone cameras to create geometrically accurate scans. Whilst several studies have investigated the potential of privately developed photogrammetry algorithms for capturing residual limbs with clinical accuracy, none to the authors knowledge have explored commercially available software to do the same. Three applications were tested, namely Polycam, Luma, and Meshroom, to determine if they could produce clinically acceptable results. Scans of ten residual limbs were created using both smartphone technology and a reference structured-light scanner (Artec EVA), against which the validity and reliability of the resulting limb models were assessed using the Bland-Altman method and Intraclass Correlation Coefficient, respectively. Polycam and Luma achieved both Pearson Coefficients and Intraclass Correlation Coefficients of 0.999, and Coefficients of Variation of 1.1% and 1.4%, respectively. Volume reliability coefficients were 58.3 ml and 70.0 ml respectively for Polycam and Luma, whereas Meshroom failed to meet any of the criteria for clinical suitability, with a repeatability coefficient of 790.3 ml. Both Polycam and Luma exhibit sufficient accuracy and reliability to be considered for clinical volume measurements.

Deep Autoencoder for Real-Time Single-Channel EEG Cleaning and Its Smartphone Implementation Using TensorFlow Lite With Hardware/Software Acceleration.

To remove signal contamination in electroencephalogram (EEG) traces coming from ocular, motion, and muscular artifacts which degrade signal quality. To do this in real-time, with low computational overhead, on a mobile platform in a channel count independent manner to enable portable Brain-Computer Interface (BCI) applications. We propose a Deep AutoEncoder (DAE) neural network for single-channel EEG artifact removal, and implement it on a smartphone via TensorFlow Lite. Delegate based acceleration is employed to allow real-time, low computational resource operation. Artifact removal performance is quantified by comparing corrupted and ground-truth clean EEG data from public datasets for a range of artifact types. The on-phone computational resources required are also measured when processing pre-saved data. DAE cleaned EEG shows high correlations with ground-truth clean EEG, with average Correlation Coefficients of 0.96, 0.85, 0.70 and 0.79 for clean EEG reconstruction, and EOG, motion, and EMG artifact removal respectively. On-smartphone tests show the model processes a 4 s EEG window within 5 ms, substantially outperforming a comparison FastICA artifact removal algorithm. The proposed DAE model shows effectiveness in single-channel EEG artifact removal. This is the first demonstration of a low-computational resource deep learning model for mobile EEG in smartphones with hardware/software acceleration. This work enables portable BCIs which require low latency real-time artifact removal, and potentially operation with a small number of EEG channels for wearability. It makes use of the artificial intelligence accelerators found in modern smartphones to improve computational performance compared to previous artifact removal approaches.

The Photometric Testing of High-Resolution Digital Cameras from Smartphones-A Pilot Study.

Luminance is the fundamental photometric quantity representing the technical meaning of brightness. It is usually measured from a distance using a matrix sensor, which is the basis of the professional instrument. However, specific technical requirements must be fulfilled to achieve accurate results. This paper considers whether modern high-resolution smartphone cameras are suitable for luminance measurements. Three cameras from high-end smartphones were evaluated on a dedicated laboratory stand. The sensors' output characteristics showed relatively good linearity of the individual R, G, and B channels. Unfortunately, the spectral sensitivities were unfavorable, as the minimum error achieved was about 17%. This device is classified outside the generally accepted quality scale for photometric instruments. The presented investigation confirmed that none of the high-resolution smartphone cameras tested was suitable for use as a universal luminance camera. However, one of the test devices can be developmental if restrictively calibrated and used only in a specialistic laboratory stand. Using a smartphone (or only its camera) for luminance measurements requires proper advanced calibration. It is possible, but it limits us to only dedicated applications. The pilot study presented in this paper will help create a suitable test stand for spectacle vision systems, e.g., virtual reality equipment.

Sloth: Key Stretching and Deniable Encryption using Secure Elements on Smartphones

Privacy enhancing technologies must not only protect sensitive data in-transit, but also locally at-rest. For example, anonymity networks hide the sender and/or recipient of a message from network adversaries. However, if a participating device is physically captured, its owner can be pressured to give access to the stored conversations. Therefore, client software should allow the user to plausibly deny the existence of meaningful data. Since biometrics can be collected without consent and server-based authentication leaks metadata, implementations typically rely on memorable passwords for local authentication. Traditional password-based key stretching lacks a strict time guarantee due to the ease of parallelized password guessing by attackers. This paper introduces Sloth, a key stretching method leveraging the Secure Element (SE) commonly found in modern smartphones to provide a strict rate limit on password guessing. While this would be straightforward with full access to the SE, Android and iOS only provide a very limited API. Sloth utilizes the existing developer SE API and novel cryptographic constructions to build an effective rate-limit for password guessing on recent Android and iOS devices. Our approach ensures robust security even for short, randomly-generated, six-character alpha-numeric passwords against adversaries with virtually unlimited computing resources. Our solution is compatible with approximately 96% of iPhones and 45% of Android phones and Sloth seamlessly integrates without device or OS modifications, making it immediately usable by app developers today. We formally define the security of Sloth and evaluate its performance on various devices. Finally, we present HiddenSloth, a plausibly-deniable encryption scheme leveraging Sloth. It provides multi-snapshot resistance against adversaries who can covertly capture its on-disk content multiple times.

Assessing the Impact of Smartphone Addiction among medical and health faculty students at university of Kirkuk

Background: Modern smartphones have become an integral part of daily life, particularly among medical students who experience significant academic pressure and stress This study aimed to assess the extent of students' impact from smartphone addiction and investigate its association with their demographic characteristics, socio-economic status, and associated risk factors. Methods: A descriptive cross-sectional study design was used in this study. 772 students from all stages of study in the medical faculties of Kirkuk University. using Smartphone Addiction Scale (SAS), Nordic questionnaires for the analysis of musculoskeletal symptoms, and the severity of digital vision syndrome (DES) was measured using 12 items, The Data was analyzed through Statistical Package for Social Sciences (SPSS) 22. Results: The result of the current study shows that 55.1% were female and the male were 347 (44.9%). the average age of the students was 21.09 ± 1.75. 46.80% of the sample were overweight and obese. around 343(44.4%) sampled are assigned intermittent sleep. while significant relationships are obtained with each of Socio-Economic Status, specialization, using Smartphone in 24hrs, sleeping hours during a day in at least at P<0.05. an overall through global mean of score for the studied domains was assessed in compact form a moderate level, with percentile ranged responses from 5.65 to 80.61 for the studied sample. Conclusion: Our study substantiated that health and medical students are progressing into a state of addiction. no significant difference between males and females when considering of smartphone addiction. The age group most affected was individuals aged 22-23. while the Individuals with moderate to high SES are more susceptibility to addiction. Smartphone use should be limited. To increase awareness and knowledge about the detrimental impacts of smartphone addiction, workshops, courses, and conferences should be held.

Asymmetric Stereo High Dynamic Range Imaging with Smartphone Cameras.

Stereo high dynamic range imaging (SHDRI) offers a more temporally stable solution to high dynamic range (HDR) imaging from low dynamic range input images compared to bracketing and removes the loss of accuracy that single-image HDR solutions offer. However, few solutions currently exist that take advantage of the different (asymmetric) lenses, commonly found on modern smartphones, to achieve SHDRI. This paper presents a method that achieves single-shot asymmetric HDR fusion via a reference-based deep learning approach. Results demonstrate a system that is more robust to aperture and image signal processing pipeline differences than existing solutions.

DETERMINING THE DENSITY AND MOLAR MASS OF AIR IN A HOME EXPERIMENT

Formulation of the problem. Experimental research is an integral part of physics education. During distance learning, students can carry out hands-on experiments only at home. Modern smartphones, equipped with various sensors, offer significant capabilities for this purpose. The literature offers quite extensive descriptions of experiments aimed at determining mechanical, acoustic, and optical quantities using smartphones. At the same time, insufficient attention has been paid to determining gas parameters that can be measured using the pressure sensor embedded in smartphones. Therefore, the relevant task is to develop a methodology for experimenting to determine the density of air and its molar mass at home using a pressure sensor. Materials and methods. To achieve the objective of the study, we used the analysis of the curriculum of the course "General Physics for Bachelor of Engineering", a review of the methodological instructions for performing laboratory work in the section "Molecular Physics and Thermodynamics" of the physics course of technical universities, a review of the literature on the topic of the study, and an analysis of the results of student research on the dependence of air pressure on altitude. We also surveyed students about the possibility of conducting the research at home and their interest in conducting other experiments using a smartphone. Results. The methodology for determining the density and molar mass of air was developed based on the results of a study of the pressure-height dependence. It is shown that it is necessary to perform statistical processing of experimental data to estimate the sought quantities. The experimental results allowed us to obtain values of density and molar mass of air that show a good correlation with the tabulated values. Conclusions. Studying the pressure-altitude relationship using a smartphone and the PhyPhox application allows for fairly accurate calculations of air density and molar mass. According to the survey results, students responded positively to conducting home experiments using smartphones.

A skeleton-based method for exercise recognition based on 3D coordinates of human joints

<p>The aim of the work is to develop the method of identification and comparison of poses and exercises performed by a person that will have a low sensitivity to data errors. This method uses their formal descriptions in the form of conjunctions of logical statements and should work regardless of the shooting angle at which the video was taken and the proportions of the person on it. Each statement describes the position of the joints relative to each other along one of the axes. The joint coordinates are corrected by taking into account the length of the bones that connect them that eliminates the necessity to process outliers and it also improves the accuracy of joints positioning. Removal of errors out of the data using the method of averaging the graph along each axis at every step. In order to do this, consecutive points are grouped so that the difference between the maximum and the minimum does not exceed the error. The groups are then filtered to leave only those in which both are smaller or both are larger. The proposed method of identification requires just a modern smartphone and has no restrictions on how to take video of exercises.</p>

Indoor AR Navigation Framework Based on Geofencing and Image-Tracking with Accumulated Error Correction

This study presents a novel framework for improving indoor augmented reality (AR) navigation with modern smartphone technology, which is achieved by addressing two major challenges: managing large absolute coordinate spaces and reducing error accumulation in camera-based spatial tracking. Our contribution is significant in two ways. First, we integrate geofencing with indoor navigation by considering spatial tracking errors, timing for audio guidance, and dynamic 3D arrow visualization for effective local-to-global spatial coordinate transformation. This method achieves precise local positioning and seamlessly integrates with larger spatial contexts, overcoming the limitations of current AR systems. Second, we introduce a periodic image-based calibration approach to minimize the inherent error accumulation in camera-based tracking, enhancing accuracy over longer distances. Unlike prior studies focusing on individual technologies, our work explores the software architecture of indoor AR navigation by providing a comprehensive framework for its design and practical use. The practicality of our approach is validated through the implementation of a smartphone application at the Mineral Industry Museum of Akita University, highlighting the limitations of component technologies and demonstrating our framework’s effectiveness.

Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests.

To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury. We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google's MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2.5ms. Custom software translated the results from both frameworks to the same format. We then extracted relevant knee angles at key moments of the jump and compared them, using the Kinect V2 as the ground truth. The results show relatively small angle differences between the two solutions in the coronal plane and moderate angle differences on the sagittal plane. Overall, the MediaPipe framework results seem to underestimate both knee valgus angles and knee sagittal angles compared to the Kinect V2. This preliminary study demonstrates the potential for Google's MediaPipe framework to be used for calculating lower limb kinematics during athletic stress test motions, which can run on most modern smartphones, as it produces similar results to the Kinect V2. A smartphone application similar to the one developed could potentially be used for low cost and widespread ACL injury prevention.

Design of a new 3D printed all-in-one magnetic smartphone adapter for fundus and anterior segment imaging.

To describe and validate a 3D-printed adapter tool which could be used with either a slit lamp or a condensing lens, interchangeable between devices through magnetic fastening, in order to provide physicians a quick, easy and effective method of obtaining clinical photos. Three specialists, with at least 4-year experience in ophthalmology, gave a rate of image quality obtained by our device and the diagnostic confidence grade. The 3 specialists conducted each 13 or 14 examinations with the smartphone and magnetic adapter. At the end of evaluation, they rated with the Likert scale the ease of use of the device in obtaining clinical images of the anterior segment and ocular fundus respectively. Data of quality perception and confidence demonstrated high values not dissimilar to the "de visu" eye examination. Moreover the instrument we designed turned out to be very user friendly. Our adapter coupled with a modern smartphone was able to obtain 4k images and videos of anterior segment, central and peripheral fundus, in an easy and inexpensive way.

Comprehensive Analysis of Advancements and Challenges in Smartphone Semiconductor Technology

The unprecedented proliferation of smartphones globally has revolutionized communication, information access, and daily life activities. This remarkable growth is largely attributed to rapid advancements in semiconductor technology, which is at the heart of these devices. Semiconductor technology, encompassing processors, memory, and sensors, has been pivotal in enhancing the performance, efficiency, and functionality of smartphones. This paper presents a comprehensive analysis of semiconductor technology in modern smartphones, addressing the principles and advancements of core components such as processors, memory, and sensors. It highlights how miniaturization of transistors, advanced storage solutions, and innovative sensor technologies enhance smartphone performance, efficiency, and functionality. The challenges faced by semiconductor technology, including physical limitations, energy efficiency, heat management, and environmental sustainability, are also explored. Additionally, the paper discusses future prospects, emphasizing potential breakthroughs with extreme ultraviolet lithography, two-dimensional materials like graphene, and intelligent manufacturing techniques. These advancements promise to overcome current limitations, offering faster computational speeds, lower energy consumption, and reduced environmental impact. The study underscores the pivotal role of semiconductor technology in driving smartphone innovation while acknowledging the critical challenges that need addressing for future development.

Development and Factor Structure of Problematic Multidimensional Smartphone Use Scale

AbstractLiving in an intensifying technological and digital environment makes people more engaged with smartphones and related internet platforms. Alongside its highly debated advantages and disadvantages, modern smartphone use is a global phenomenon which has been mainly debated within the context of cultural and technology relationships which also influence cross-cultural activities. Focusing on one of the modern factors (i.e., smartphone use) that impact human behavior, this article presents the development of a measure of the Multidimensional Smartphone Use Scale (MSUS) and tests its factor structure to offer a scale that covers smartphone-related problems from a broader perspective. Using an online survey, a total of 514 participants took part in the current study. The scale was developed to measure a multidimensional conception of smartphone use-related problems and behavioral disorders including nomophobia, ringxiety, textiety, and phubbing which were supported with exploratory and confirmatory factors analyses. The MSUS indicated good internal consistency reliability ranging from α = .75 to α = .89. This article presents the preliminary evidence regarding the reliability and validity of the MSUS which can be used in a wide range of settings to measure maladaptive modes of smartphone use and related behaviors.

Exploiting RPMB authentication in a closed source TEE implementation

Embedded Multimedia Cards (eMMCs) provide a protected memory area called the Replay Protected Memory Block (RPMB). eMMCs are commonly used as storage media in modern smartphones. In order to protect these devices from unauthorized access, important data is stored in the RPMB area in an authenticated manner. Modification of the RPMB data requires a pre-shared authentication key. An unauthorized user cannot change the stored data.On modern devices, this pre-shared key is generated and used exclusively within a Trusted Execution Environment (TEE) preventing attackers from access. In this paper, we investigate how the authentication key for RPMB is programmed on the eMMC. We found that this key can be extracted directly from the target memory chip. Once obtained, the authentication key can be used to manipulate stored data. In addition, poor implementation of certain security features, aimed at preventing replay attacks using RPMB on the host system can be broken by an attacker. We show how the authentication key can be extracted and how it can be used to break the anti-rollback protection to enable data restoration even after a data wipe operation has been completed.Our findings show that non-secure RPMB implementations can enable forensic investigators to break security features implemented on modern smartphones.

A Cloud-native Approach for Processing of Crowdsourced GNSS Observations and Machine Learning at Scale: A Case Study from the CAMALIOT Project

The era of modern smartphones, running on Android version 7.0 and higher, facilitates nowadays acquisition of raw dual-frequency multi-constellation GNSS observations. This paves the way for GNSS community data to be potentially exploited for precise positioning, GNSS reflectometry or geoscience applications at large. The continuously expanding global GNSS infrastructure along with the enormous volume of prospective GNSS community data bring, however, major challenges related to data acquisition, its storage, and subsequent processing for deriving various parameters of interest. In addition, such large datasets cannot be managed manually anymore, leading thus to the need for fully automated and sophisticated data processing pipelines. Application of Machine Learning Technology for GNSS IoT data fusion (CAMALIOT) was an ESA NAVISP Element 1 project (NAVISP-EL1-038.2) with activities aiming to address the aforementioned points related to GNSS community data and their exploitation for scientific applications with the use of Machine Learning (ML). This contribution provides an overview of the CAMALIOT project with information on the designed and implemented cloud-native software for GNSS processing and ML at scale, developed Android application for retrieving GNSS observations from the modern generation of smartphones through dedicated crowdsourcing campaigns, related data ingestion and processing, and GNSS analysis concerning both conventional and smartphone GNSS observations. With the use of the developed GNSS engine employing an Extended Kalman Filter, example processing results related to the Zenith Total Delay (ZTD) and Slant Total Electron Content (STEC) are provided based on the analysis of observations collected with geodetic-grade GNSS receivers and from local measurement sessions involving Xiaomi Mi 8 that collected GNSS observations using the developed Android application. For smartphone observations, ZTD is derived in a differential manner based on a single-frequency double-difference approach employing GPS and Galileo observations, whereas satellite-specific STEC time series are obtained through carrier-to-code leveling based on the geometry-free linear combination of observations from both GPS and Galileo constellations. Although the ZTD and STEC time series from smartphones were derived on a demonstration basis, a rather good level of consistency of such estimates with respect to the reference time series was found. For the considered periods, the RMS of differences between the derived smartphone-based time series of differential zenith wet delay and reference values were below 3.1 mm. In terms of satellite-specific STEC time series expressed with respect to the reference STEC time series, RMS of the offset-reduced differences below 1.2 TECU was found. Smartphone-based observations require special attention including additional processing steps and a dedicated parameterization in order to be able to acquire reliable atmospheric estimates. Although with lower measurement quality compared to traditional sources of GNSS data, an augmentation of ground-based networks of fixed high-end GNSS receivers with GNSS-capable smartphones would however, form an interesting source of complementary information for various studies relying on GNSS observations.

Armed with Faster Crypto: Optimizing Elliptic Curve Cryptography for ARM Processors

Elliptic curve cryptography is a widely deployed technology for securing digital communication. It is the basis of many cryptographic primitives such as key agreement protocols, digital signatures, and zero-knowledge proofs. Fast elliptic curve cryptography relies on heavily optimised modular arithmetic operations, which are often tailored to specific micro-architectures. In this article, we study and evaluate optimisations of the popular elliptic curve Curve25519 for ARM processors. We specifically target the ARM NEON single instruction, multiple data (SIMD) architecture, which is a popular architecture for modern smartphones. We introduce a novel representation for 128-bit NEON SIMD vectors, optimised for SIMD parallelisation, to accelerate elliptic curve operations significantly. Leveraging this representation, we implement an extended twisted Edwards curve Curve25519 back-end within the popular Rust library “curve25519-dalek”. We extensively evaluate our implementation across multiple ARM devices using both cryptographic benchmarks and the benchmark suite available for the Signal protocol. Our findings demonstrate a substantial back-end speed-up of at least 20% for ARM NEON, along with a noteworthy speed improvement of at least 15% for benchmarked Signal functions.

The Effect of Electromagnetic Interference Produced by Smartphones Using 5G Network on Patients With Permanent Pacemakers (EMS5G‐PPM Study)

Background: The safety of new‐generation mobile phones using 5G networks in patients with modern‐generation pacemakers has not been studied.Objectives: This study aimed to compare the risk of electromagnetic interference (EMI) generated by a new‐generation mobile phone with 5G networks when positioned at the pacemaker pocket or the contralateral ear and assess the incidence of EMI with telemetry interrogation in patients with permanent pacemakers.Methods: We enrolled 489 patients with pacemakers from three different manufacturers. The pacemaker mode was programmed for overdrive pacing and sensing mode if an intrinsic rhythm was present. A smartphone (Samsung S21 + 5G) was placed directly over the pulse generator and right ear. The phone was tested under standby mode, 5G internet connection, and incoming and outgoing calls for each location. Real‐time electrocardiography (ECG) monitoring and patient symptoms were recorded to determine the occurrence of EMI. The possibility of EMI with interrogation telemetry was also investigated.Results: A total of 4824 tests were performed on 489 patients. Most pacemakers were dual‐chamber (82%) or magnetic resonance imaging (MRI)‐compatible systems (83%). EMI was not detected with both mobile phone positions. Interference with telemetry was demonstrated in 11.5% of patients. Almost all incidences of interference (98.2%) occurred during incoming calls. Single‐chamber pacemakers, non‐MRI‐compatible systems, older pulse generators, older leads, and unipolar settings were significantly related to a higher incidence of interference with interrogation telemetry.Conclusions: The risk of EMI between modern smartphones with 5G networks and pacemakers is low. Nevertheless, interference with the interrogation telemetry may still occur.

CalAid – A mobile fitness application using machine learning for tracking energy expenditure

This paper presents the design, implementation and validation of a novel fitness mobile application called CalAid, aimed at promoting an active lifestyle, in the context of ever growing sedentarism in population of all ages. The application uses sensors present in most modern smartphones such as motion sensors, the 3-axial accelerometer, the gyroscope and the step counter in order to track energy expenditure and to obtain a more precise approximation of consumed calories. By using machine learning we can recognize the activities performed by the application client and, using metabolic equivalents, find the energy expenditure.

Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations.

In this paper, we consider two challenging issues in reference-based super-resolution (RefSR) for smartphone, (i) how to choose a proper reference image, and (ii) how to learn RefSR in a self-supervised manner. Particularly, we propose a novel self-supervised learning approach for real-world RefSR from observations at dual and multiple camera zooms. Firstly, considering the popularity of multiple cameras in modern smartphones, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the super-resolution (SR) of the lesser zoomed (ultra-wide) image, which gives us a chance to learn a deep network that performs SR from the dual zoomed observations (DZSR). Secondly, for self-supervised learning of DZSR, we take the telephoto image instead of an additional high-resolution image as the supervision information, and select a center patch from it as the reference to super-resolve the corresponding ultra-wide image patch. To mitigate the effect of the misalignment between ultra-wide low-resolution (LR) patch and telephoto ground-truth (GT) image during training, we first adopt patch-based optical flow alignment to obtain the warped LR, then further design an auxiliary-LR to guide the deforming of the warped LR features. To generate visually pleasing results, we present local overlapped sliced Wasserstein loss to better represent the perceptual difference between GT and output in the feature space. During testing, DZSR can be directly deployed to super-solve the whole ultra-wide image with the reference of the telephoto image. In addition, we further take multiple zoomed observations to explore self-supervised RefSR, and present a progressive fusion scheme for the effective utilization of reference images. Experiments show that our methods achieve better quantitative and qualitative performance against state-of-the-arts. The code and pre-trained models will be publicly available.

Narzędzia mobilne w planowaniu i monitoringu aktywności fizycznej i w promocji zdrowia

Mobile devices in planning and monitoring physical activity and in promoting health The vast majority of Poles have modern smartphones or smartwatches. These devices are equipped with a number of sensors that can provide a lot of information interesting from a biomedical point of view. According to the concept of Marc Lalond, the most important factor affecting human health is their lifestyle. The purpose of this article is to present widely available and easy-to-use mobile tools that can be utilised to monitor health and the level of physical activity. Dedicated applications tracking physical activity, diet and the calorie intake, sleep patterns or helping to control or stop using stimulants should be an important element of health promotion. The authors postulate the deliberate use of devices owned by majority of the society to improve habits, the general condition of the body and their self-awareness.