evaluating-usability-aspects-mixed-reality-solution-for-immersive

Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to ...

Otto von Guericke University Magdeburg

Predicting the evolution of individuals is a rather new mining task with applications in medicine. Medical researchers are interested in the progression of a disease and/or how do patients evolve or recover when they are subjected to some treatment. In this study, we investigate the problem of patients' evolution on the basis of medical tests before and after treatment after brain trauma: we want to understand to what extend a patient can become similar to a healthy participant. We face two challenges. First, we have less information on healthy participants than on the patients. Second, the values of the medical tests for patients, even after treatment started, remain well-separated from those of healthy people; this is typical for neurodegenerative diseases, but also for further brain impairments. Our approach encompasses methods for modelling patient evolution and for predicting the health improvement of different patients' subpopulations, i.e. prediction of label if they recovered or not. We test our approach on a cohort of patients treated after brain trauma and a corresponding cohort of controls.

Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI).

Only few previous studies used ecological momentary assessments to explore the time-of-day-dependence of tinnitus. The present study used data from the mobile application "TrackYourTinnitus" to explore whether tinnitus loudness and tinnitus distress fluctuate within a 24-h interval. Multilevel models were performed to account for the nested structure of assessments (level 1: 17,209 daily life assessments) nested within days (level 2: 3,570 days with at least three completed assessments), and days nested within participants (level 3: 350 participants). Results revealed a time-of-day-dependence of tinnitus. In particular, tinnitus was perceived as louder and more distressing during the night and early morning hours (from 12 a.m. to 8 a.m.) than during the upcoming day. Since previous studies suggested that stress (and stress-associated hormones) show a circadian rhythm and this might influence the time-of-day-dependence of tinnitus, we evaluated whether the described results change when statistically controlling for subjectively reported stress-levels. Correcting for subjective stress-levels, however, did not change the result that tinnitus (loudness and distress) was most severe at night and early morning. These results show that time-of-day contributes to the level of both tinnitus loudness and tinnitus distress. Possible implications of our results for the clinical management of tinnitus are that tailoring the timing of therapeutic interventions to the circadian rhythm of individual patients (chronotherapy) might be promising.

Does Tinnitus Depend on Time-of-Day? An Ecological Momentary Assessment Study with the "TrackYourTinnitus" Application.

In the recent years, there has been an increasing interest in the potential of internet- and smartphone-based technologies for the support of tinnitus patients. A broad spectrum of relevant approaches, some in the form of studies, others in the form of market products, have been mentioned in literature. They include auditory treatments, internet-based Cognitive Behavioral Therapy (iCBT), serious games, and questionnaires for tinnitus monitoring. The goal of this study is to highlight the role of existing internet-based and smart technologies for the advancement of tinnitus clinical practice: we consider contributions that refer to treatments and diagnostics, and we include contributions refering to self-help measures. We elaborate on the potential and challenges of such solutions and identify constraints associated to their deployment, such as the demand for familiarity with internet-based services and the need to re-design interactive services so that they fit on the small surface of a smartwatch.

Review of Smart Services for Tinnitus Self-Help, Diagnostics and Treatments.

Editorial: Towards an Understanding of Tinnitus Heterogeneity.

Visual analytics are becoming increasingly important in the light of big data and related scenarios. Along this trend, the field of immersive analytics has been variously furthered as it is able to provide sophisticated visual data analytics on one hand, while preserving user-friendliness on the other. Furthermore, recent hardware developments such as smart glasses, as well as achievements in virtual-reality applications, have fanned immersive analytic solutions. Notably, such solutions can be very effective when they are applied to high-dimensional datasets. Taking this advantage into account, the work at hand applies immersive analytics to a high-dimensional production dataset to improve the digital support of daily work tasks. More specifically, a mixed-reality implementation is presented that will support manufacturers as well as data scientists to comprehensively analyze machine data. As a particular goal, the prototype will simplify the analysis of manufacturing data through the usage of dimensionality reduction effects. Therefore, five aspects are mainly reported in this paper. First, it is shown how dimensionality reduction effects can be represented by clusters. Second, it is presented how the resulting information loss of the reduction is addressed. Third, the graphical interface of the developed prototype is illustrated as it provides (1) a correlation coefficient graph, (2) a plot for the information loss, and (3) a 3D particle system. In addition, an implemented voice recognition feature of the prototype is shown, which was considered to be being promising to select or deselect data variables users are interested in when analyzing the data. Fourth, based on a machine learning library, it is shown how the prototype reduces computational resources using smart glasses. The main idea is based on a recommendation approach as well as the use of subspace clustering. Fifth, results from a practical setting are presented, in which the prototype was shown to domain experts. The latter reported that such a tool is actually helpful to analyze machine data daily. Moreover, it was reported that such a system can be used to educate machine operators more properly. As a general outcome of this work, the presented approach may constitute a helpful solution for the industry as well as other domains such as medicine.

Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data.

The mobile phone app, TrackYourStress (TYS), is a new crowdsensing mobile health platform for ecological momentary assessments of perceived stress levels.

Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessment Pilot Study.

Chronic tinnitus is a complex condition that can be associated with considerable distress. Whilst cognitive-behavioral treatment (CBT) approaches have been shown to be effective, not all patients benefit from psychological or psychologically anchored multimodal therapies. Determinants of tinnitus-related distress thus provide valuable information about tinnitus characterization and therapy planning.

Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables.

Tinnitus is a complex condition that is associated with major psychological and economic impairments - partly through various comorbidities such as depression. Understanding the interaction between tinnitus and depression may thus improve either symptom cluster's prevention, diagnosis and treatment. In this study, we developed and validated a machine learning model to predict depression severity after outpatient therapy (T1) based on variables obtained before therapy (T0). 1,490 patients with chronic tinnitus (comorbid major depressive disorder: 52.2%) who completed a 7-day multimodal treatment encompassing tinnitus-specific components, cognitive behavioural therapy, physiotherapy and informational counselling were included. 185 variables were extracted from self-report questionnaires and socio-demographic data acquired at T0. We used 11 classification methods to train models that reliably separate between subclinical and clinical depression at T1 as measured by the general depression questionnaire. To ensure highly predictive and robust classifiers, we tuned algorithm hyperparameters in a 10-fold cross-validation scheme. To reduce model complexity and improve interpretability, we wrapped model training around an incremental feature selection mechanism that retained features that contributed to model prediction. We identified a LASSO model that included all 185 features to yield highest predictive performance (AUC = 0.87 ± 0.04). Through our feature selection wrapper, we identified a LASSO model with good trade-off between predictive performance and interpretability that used only 6 features (AUC = 0.85 ± 0.05). Thus, predictive machine learning models can lead to a better understanding of depression in tinnitus patients, and contribute to the selection of suitable therapeutic strategies and concise and valid questionnaire design for patients with chronic tinnitus with or without comorbid major depressive disorder.

Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics.

Human learning is one of the main topics in psychology and cognitive neuroscience. The analysis of experimental data, e.g. from category learning experiments, is a major challenge due to confounding factors related to perceptual processing, feedback value, response selection, as well as inter-individual differences in learning progress due to differing strategies or skills. We use machine learning to investigate (Q1) how participants of an auditory category-learning experiment evolve towards learning, (Q2) how participant performance saturates and (Q3) how early we can differentiate whether a participant has learned the categories or not. We found that a Gaussian Mixture Model describes well the evolution of participant performance and serves as basis for identifying influencing factors of task configuration (Q1). We found early saturation trends (Q2) and that CatBoost, an advanced classification algorithm, can separate between participants who learned the categories and those who did not, well before the end of the learning session, without much degradation of separation quality (Q3). Our results show that machine learning can model participant dynamics, identify influencing factors of task design and performance trends. This will help to improve computational models of auditory category learning and define suitable time points for interventions into learning, e.g. by tutorial systems.

Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment.

Microcirculatory defects in diabetes are linked with neuropathy and the onset of diabetic foot syndrome. In this study we quantify pressure- and posture-dependent changes of plantar temperatures as a surrogate of tissue perfusion in healthy volunteers versus diabetes patients diagnosed with neuropathy in the absence of macroangiopathy.

Plantar temperatures in stance position: A comparative study with healthy volunteers and diabetes patients diagnosed with sensoric neuropathy.

Whilst some studies have identified gender-specific differences, there is no consensus about gender-specific determinants for prevalence rates or concomitant symptoms of chronic tinnitus such as depression or anxiety. However, gender-associated differences in psychological response profiles and coping strategies may differentially affect tinnitus chronification and treatment success rates. Thus, understanding gender-associated differences may facilitate a more detailed identification of symptom profiles, heighten treatment response rates, and help to create access for vulnerable populations that are potentially less visible in clinical settings. Our research questions are: RQ1: how do male and female tinnitus patients differ regarding tinnitus-related distress, depression severity, and treatment response, RQ2: to what extent are answers to questionnaires administered at baseline associated with gender, and RQ3: which baseline questionnaire items are associated with tinnitus distress, depression, and treatment response, while relating to one gender only? In this work, we present a data analysis workflow to investigate gender-specific differences in  = 1,628 patients with chronic tinnitus (828 female, 800 male) who completed a 7-day multimodal treatment encompassing cognitive behavioral therapy (CBT), physiotherapy, auditory attention training, and information counseling components. For this purpose, we extracted 181 variables from 7 self-report questionnaires on socio-demographics, tinnitus-related distress, tinnitus frequency, loudness, localization, and quality as well as physical and mental health status. Our workflow comprises (i) training machine learning models, (ii) a comprehensive evaluation including hyperparameter optimization, and (iii) post-learning steps to identify predictive variables. We found that female patients reported higher levels of tinnitus-related distress, depression and response to treatment (RQ1). Female patients indicated higher levels of tension, stress, and psychological coping strategies rates. By contrast, male patients reported higher levels of bodily pain associated with chronic tinnitus whilst judging their overall health as better (RQ2). Variables measuring depression, sleep problems, tinnitus frequency, and loudness were associated with tinnitus-related distress in both genders and indicators of mental health and subjective stress were found to be associated with depression in both genders (RQ3). Our results suggest that gender-associated differences in symptomatology and treatment response profiles suggest clinical and conceptual needs for differential diagnostics, case conceptualization and treatment pathways.

Gender-Specific Differences in Patients With Chronic Tinnitus-Baseline Characteristics and Treatment Effects.

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform.

Chronic tinnitus is a complex, multi-factorial symptom that requires careful assessment and management. Evidence-based therapeutic approaches involve audiological and psychological treatment components. However, not everyone benefits from treatment. The identification and characterisation of patient subgroups (or "phenotypes") may provide clinically relevant information. Due to the large number of assessment tools, data-driven methods appear to be promising. The acceptance of these empirical results can be further strengthened by a comprehensive visualisation. In this study, we used cluster analysis to identify distinct tinnitus phenotypes based on self-report questionnaire data and implemented a visualisation tool to explore phenotype idiosyncrasies. 1228 patients with chronic tinnitus from the Charité Tinnitus Center in Berlin were included. At baseline, each participant completed 14 questionnaires measuring tinnitus distress, -loudness, frequency and location, depressivity, perceived stress, quality of life, physical and mental health, pain perception, somatic symptom expression and coping attitudes. Four distinct patient phenotypes emerged from clustering: avoidant group (56.8%), psychosomatic group (14.1%), somatic group (15.2%), and distress group (13.9%). Radial bar- and line charts allowed for visual inspection and juxtaposition of major phenotype characteristics. The phenotypes differed in terms of clinical information including psychological symptoms, quality of life, coping attitudes, stress, tinnitus-related distress and pain, as well as socio-demographics. Our findings suggest that identifiable patient subgroups and their visualisation may allow for stratified treatment strategies and research designs.

Article	Year
Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain informatics\| PubMed ID: 27747503	2015
Does Tinnitus Depend on Time-of-Day? An Ecological Momentary Assessment Study with the "TrackYourTinnitus" Application. Frontiers in aging neuroscience\| PubMed ID: 28824415	2017
Review of Smart Services for Tinnitus Self-Help, Diagnostics and Treatments. Frontiers in neuroscience\| PubMed ID: 30177869	2018
Editorial: Towards an Understanding of Tinnitus Heterogeneity. Frontiers in aging neuroscience\| PubMed ID: 30941029	2019
Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data. Sensors (Basel, Switzerland)\| PubMed ID: 31510064	2019
Exploring the Time Trend of Stress Levels While Using the Crowdsensing Mobile Health Platform, TrackYourStress, and the Influence of Perceived Stress Reactivity: Ecological Momentary Assessm... JMIR mHealth and uHealth\| PubMed ID: 31670692	2019
Tinnitus-related distress after multimodal treatment can be characterized using a key subset of baseline variables. PloS one\| PubMed ID: 31999776	2020
Development and internal validation of a depression severity prediction model for tinnitus patients based on questionnaire responses and socio-demographics. Scientific reports\| PubMed ID: 32170136	2020
Machine learning identifies the dynamics and influencing factors in an auditory category learning experiment. Scientific reports\| PubMed ID: 32300111	2020
Plantar temperatures in stance position: A comparative study with healthy volunteers and diabetes patients diagnosed with sensoric neuropathy. EBioMedicine\| PubMed ID: 32304997	2020
Gender-Specific Differences in Patients With Chronic Tinnitus-Baseline Characteristics and Treatment Effects. Frontiers in neuroscience\| PubMed ID: 32523506	2020
Efficient Processing of Geospatial mHealth Data Using a Scalable Crowdsensing Platform. Sensors (Basel, Switzerland)\| PubMed ID: 32570953	2020
Phenotyping chronic tinnitus patients using self-report questionnaire data: cluster analysis and visual comparison. Scientific reports\| PubMed ID: 33009468	2020

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