Dimensional emotion recognition from camera-based PRV features.

Heart rate variability (HRV) is an important indicator of autonomic nervous system activity and can be used for the identification of affective states. The development of remote Photoplethysmography (rPPG) technology has made it possible to measure pulse rate variability (PRV) using a camera without any sensor-skin contact, which is highly correlated to HRV, thus, enabling contactless assessment of emotional states. In this study, we employed ten machine learning techniques to identify emotions using camera-based PRV features. Our experimental results show that the best classification model achieved a coordination correlation coefficient of 0.34 for value recognition and 0.36 for arousal recognition. The rPPG-based measurement has demonstrated promising results in detecting HAHV (high-arousal high-valence) emotions with high accuracy. Furthermore, for emotions with less noticeable variations, such as sadness, the rPPG-based measure outperformed the baseline deep network for facial expression analysis.

Compensation of lens manufacturing errors and inhomogeneities by filtering view images in three-dimensional lenticular displays.

For a multiview autostereoscopic three-dimensional display based on the lenticular lens, the manufacturing errors and inhomogeneities of the lenticular sheet can deteriorate the view image quality. A calibration method is proposed by filtering the pixel mapping of all view images. Each view is first sequentially turned on to show a white test image, and the intensity distribution of every view will be analyzed. Then individual filter functions are derived and applied to the corresponding view images to correct the subpixel value. Experimental results show that the proposed method can solve view image loss and significantly improve the quality of stereoscopic images.

Influence of laser coherence on reference-matched laser Doppler velocimetry.

The probe length is investigated under the influence of the coherence length of Gaussian and Lorentzian spectra for the case that the focal point and the point of highest interference are matched in a strongly focused laser Doppler velocimetry setup (LDV). Isosurfaces of a -3 dB drop of the intensity maximum are estimated and suggested as an alternative, comprehensible way to define probe volumes. In the end, the equations are applied for an exemplary lidar setup to show the reduction of requirements for the coherence length of the laser source in comparison to unmatched cases.

A prediction model for ocular damage - Experimental validation.

With the increasing number of laser applications in medicine and technology, accidental as well as intentional exposure of the human eye to laser sources has become a major concern. Therefore, a prediction model for ocular damage (PMOD) is presented within this work and validated for long-term exposure. This model is a combination of a raytracing model with a thermodynamical model of the human and an application which determines the thermal damage by the implementation of the Arrhenius integral. The model is based on our earlier work and is here validated against temperature measurements taken with porcine eye samples. For this validation, three different powers were used: 50mW, 100mW and 200mW with a spot size of 1.9mm. Also, the measurements were taken with two different sensing systems, an infrared camera and a fibre optic probe placed within the tissue. The temperatures were measured up to 60s and then compared against simulations. The measured temperatures were found to be in good agreement with the values predicted by the PMOD-model. To our best knowledge, this is the first model which is validated for both short-term and long-term irradiations in terms of temperature and thus demonstrates that temperatures can be accurately predicted within the thermal damage regime.

Large aperture at low cost three-dimensional time-of-flight range sensor using scanning micromirrors and synchronous detector switching.

In this article the problem of achieving fast scanning of a time-of-flight range sensor with a large optical receiver aperture at low system cost is targeted. The presented approach to solve this problem consists of a micromirror-based transmitter unit and a receiver unit consisting of a large aperture lens system with a small field of view and a detector array. A concept, which is called synchronous detector switching, is applied to the detector array. Thereby electronic steering of the small receiver field of view is possible. The overall approach is compared to alternative approaches, and the underlying concept of synchronous detector switching is demonstrated experimentally in an implementation of a three-dimensional time-of-flight range sensor. It is theoretically shown that the presented concept is potentially cheaper than the alternative approaches for applications with a field of view of less than 60×60°. After a discussion of the strengths and limitations of the approach, its effect on broader scientific issues is outlined.

[Benefits of participatory involvement of patients in the development of a dermatological treatment app-A report from the practice]. / Nutzen der partizipatorischen Mitwirkung von PatientInnen an der Entwicklung einer dermatologischen Therapie-App ­ ein Bericht aus der Praxis.

Approximately 2% of the German population suffer from psoriasis. HybridVITA has developed a mobile application (app) that enables psoriasis patients to independently document the progression of the disease and the current psychological stress at home. The HybridVITA app was created in close collaboration with user groups to ensure optimal adaptation to their needs. Two interactive workshops were held with the user groups and the technical developers of the app as a core element of the developmental process. The workshops identified the needs and suggestions for improvement of the various user groups and formulated user stories for the further development of the app using the Scrum method. The participatory approach of the workshop enabled the project team to gather valuable practical knowledge at an early stage of development. The team's awareness of potential obstacles during the early stages of the project enabled them to proactively identify and address these issues prior to implementing the app in dermatological care. We are confident that a patient-centered and participatory approach to health app development can provide valuable insights for developers.

Nanosecond multipulse retinal damage thresholds of elongated irradiance profiles in explant measurements and simulations.

Significance: The database for multipulse retinal damage thresholds for the laser safety standard (IEC 60825-1:2014) is confined, especially for elongated irradiation profiles. To ensure eye safety, retinal damage thresholds (ED50 values) need to be determined. Aim: This study aims to examine nanosecond multipulse scenarios. Approach: To determine ED50 values in ex vivo measurements, an optical laser setup is presented. Porcine explant tissue is irradiated with rectangular top-hat profiles. Thermal simulations are carried out on a validated computer model and retinal injury thresholds are obtained. Results: The measurements resulted in ED50 values from 8.46 to 42.72 µJ with a slope from 1.15 to 1.4. A thermal damage in the measurements can be excluded due to the level value in combination with a different type of declining behavior for increasing pulses compared to the simulations. A dependence with increasing elongation or area of the retinal image emerges in the simulations but could not be confirmed in the measurements due to the influencing factors (biological variability, focusing, and measuring procedure). Conclusions: Using slit apertures for beam shaping, variable rectangular spot geometries are realized without changing elements in the setup. For further evaluation of the behavior of elongated irradiation profiles, additional measurements to improve the measurement accuracy are necessary.

Model-Driven Dementia Prevention and Intervention Platform.

Most types of dementia, including Alzheimer's disease, are not curable. However, there are risk factors, such as obesity or hypertension, that can promote the development of dementia. Holistic treatment of these risk factors can prevent the onset of dementia or delay it in its early stages. To support individualized treatment of risk factors in dementia, this paper presents a model-driven digital platform. It enables monitoring of biomarkers using smart devices from the internet of medical things (IoMT) for the target group. The collected data from such devices can be used to optimize and adjust treatment in a patient in the loop manner. To this end, providers such as Google Fit and Withings have been connected to the platform as example data sources. To achieve treatment and monitoring data interoperability with existing medical systems, internationally accepted standards such as FHIR are used. The configuration and control of the personalized treatment processes are achieved using a self-developed domain-specific language. For this language, an associated diagram editor was implemented, which allows the management of the treatment processes through graphical models. This graphical representation should help treatment providers to understand and manage these processes more easily. To investigate this hypothesis, a usability study was conducted with twelve participants. We were able to show that such graphical representations provide advantages in clarity in reviewing the system, but lack in easy set-up (compared to wizard-style systems).

Explainable Artificial Intelligence in Ambulatory Digital Dementia Screenings.

Recently, digital apps have entered the market to enable the early diagnosis of dementia by offering digital dementia screenings. Some of these apps use Machine Learning (ML) to predict cognitive impairment. The aim of this work is to find explanations for the predictions of such a mobile application called DemPredict using methods from the field of Explainable Artificial Intelligence (XAI). In order to evaluate which method is best suited, different XAI approaches are used and compared. However, the comparability of the results is a key challenge. By evaluating the trustworthiness, stability, and computation time of the methods, it is possible to identify the optimal XAI approaches for the respective algorithms.

Digital Health Apps in the Context of Dementia: Questionnaire Study to Assess the Likelihood of Use Among Physicians.

BACKGROUND: Age-related diseases such as dementia are playing an increasingly important role in global population development. Thus, prevention, diagnostics, and interventions require more accessibility, which can be realized through digital health apps. With the app on prescription, Germany made history by being the first country worldwide to offer physicians the possibility to prescribe and reimburse digital health apps as of the end of the year 2020. OBJECTIVE: Considering the lack of knowledge about correlations with the likelihood of use among physicians, this study aimed to address the question of what makes the use of a digital health app by physicians more likely. METHODS: We developed and validated a novel measurement tool-the Digital Health Compliance Questionnaire (DHCQ)-in an interdisciplinary collaboration of experts to assess the role of proposed factors in the likelihood of using a health app. Therefore, a web-based survey was conducted to evaluate the likelihood of using a digital app called DemPredict to screen for Alzheimer dementia. Within this survey, 5 latent dimensions (acceptance, attitude toward technology, technology experience, payment for time of use, and effort of collection), the dependent variable likelihood of use, and answers to exploratory questions were recorded and tested within directed correlations. Following a non-probability-sampling strategy, the study was completed by 331 physicians from Germany in the German language, of whom 301 (90.9%) fulfilled the study criteria (eg, being in regular contact with patients with dementia). These data were analyzed using a range of statistical methods to validate the dimensions of the DHCQ. RESULTS: The DHCQ revealed good test theoretical measures-it showed excellent fit indexes (Tucker-Lewis index=0.98; comparative fit index=0.982; standardized root mean square residual=0.073; root mean square error of approximation=0.037), good internal consistency (Cronbach α=.83), and signs of moderate to large correlations between the DHCQ dimensions and the dependent variable. The correlations between the variables acceptance, attitude toward technology, technology experience, and payment for the time of use and the dependent variable likelihood of use ranged from 0.29 to 0.79, and the correlation between effort of the collection and likelihood of use was -0.80. In addition, we found high levels of skepticism regarding data protection, and the age of the participants was found to be negatively related to their technical experience and attitude toward technology. CONCLUSIONS: In the context of the results, increased communication between the medical and technology sectors and significantly more awareness raising are recommended to make the use of digital health apps more attractive to physicians as they can be adjusted to their everyday needs. Further research could explore the connection between areas such as adherence on the patient side and its impact on the likelihood of use by physicians.