Towards Preventing Gaps in Health Care Systems through Smartphone Use: Analysis of ARKit for Accurate Measurement of Facial Distances in Different Angles.

There is a growing consensus in the global health community that the use of communication technologies will be an essential factor in ensuring universal health coverage of the world's population. New technologies can only be used profitably if their accuracy is sufficient. Therefore, we explore the feasibility of using Apple's ARKit technology to accurately measure the distance from the user's eye to their smartphone screen. We developed an iOS application for measuring eyes-to-phone distances in various angles, using the built-in front-facing-camera and TrueDepth sensor. The actual position of the phone is precisely controlled and recorded, by fixing the head position and placing the phone in a robotic arm. Our results indicate that ARKit is capable of producing accurate measurements, with overall errors ranging between 0.88% and 9.07% from the actual distance, across various head positions. The accuracy of ARKit may be impacted by several factors such as head size, position, device model, and temperature. Our findings suggest that ARKit is a useful tool in the development of applications aimed at preventing eye damage caused by smartphone use.

Interrater reliability in the assessment of physiotherapy students.

BACKGROUND: Reliable and objective assessment of psychomotor skills in physiotherapy students' education is essential for direct feedback and skill improvement. The aim of this study is to determine the interrater reliability in the assessment process of physiotherapy students and to analyse the assessment behaviour of the examiners. METHODS: Physiotherapy teachers from two different schools assessed students from two different schools performing proprioceptive neuromuscular facilitation (PNF) patterns. An evaluation sheet with a 6-point rating scale and 20 evaluation criteria including an overall rating was used for assessment. The interrater reliability was determined calculating an intraclass-correlation coefficient (ICC) and Krippendorff's alpha. The assessment behaviour of the examiners was further analysed calculating the location parameters and showing the item response distribution over item in form of a Likert plot. RESULTS: The ICC estimates were mostly below 0.4, indicating poor interrater reliability. This was confirmed by Krippendorff's alpha. The examiners showed a certain central tendency and intergroup bias. DISCUSSION AND CONCLUSION: The interrater reliability in this assessment format was rather low. No difference between the two physiotherapy schools concerning the interrater reliability could be identified. Despite certain limitations of this study, there is a definite need for improvement of the assessment process in physiotherapy education to provide the students with reliable and objective feedback and ensure a certain level of professional competence in the students. TRIAL REGISTRATION: The study was approved by the ethics committee of the Medical Faculty RWTH Aachen University (EK 340/16).

Tracking of Mental Workload with a Mobile EEG Sensor.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

Recursive neural networks in hospital bed occupancy forecasting.

BACKGROUND: Efficient planning of hospital bed usage is a necessary condition to minimize the hospital costs. In the presented work we deal with the problem of occupancy forecasting in the scale of several months, with a focus on personnel's holiday planning. METHODS: We construct a model based on a set of recursive neural networks, which performs an occupancy prediction using historical admission and release data combined with external factors such as public and school holidays. The model requires no personal information on patients or staff. It is optimized for a 60 days forecast during the summer season (May-September). RESULTS: An average mean absolute percentage error (MAPE) of 6.24% was computed on 8 validation sets. CONCLUSIONS: The proposed machine learning model has shown to be competitive to standard time-series forecasting models and can be recommended for incorporation in medium-size hospitals automatized scheduling and decision making.

Correction: Kutafina, E.; Laukamp, D.; Bettermann, R.; Schroeder, U.; Jonas, S.M. Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training. Sensors 2016, 16, 1221.

The authors wish to make the following erratum to this paper [...].

Wearable Sensors for eLearning of Manual Tasks: Using Forearm EMG in Hand Hygiene Training.

In this paper, we propose a novel approach to eLearning that makes use of smart wearable sensors. Traditional eLearning supports the remote and mobile learning of mostly theoretical knowledge. Here we discuss the possibilities of eLearning to support the training of manual skills. We employ forearm armbands with inertial measurement units and surface electromyography sensors to detect and analyse the user's hand motions and evaluate their performance. Hand hygiene is chosen as the example activity, as it is a highly standardized manual task that is often not properly executed. The World Health Organization guidelines on hand hygiene are taken as a model of the optimal hygiene procedure, due to their algorithmic structure. Gesture recognition procedures based on artificial neural networks and hidden Markov modeling were developed, achieving recognition rates of 98 . 30 % ( ± 1 . 26 % ) for individual gestures. Our approach is shown to be promising for further research and application in the mobile eLearning of manual skills.

TrustNShare: Development of a Blockchain-Based Data Trust Model for Secure and Controlled Health Data Sharing Grounded on Empirical Research.

Ensuring data quality and protecting data are key requirements when working with health-related data. Re-identification risks of feature-rich data sets have led to the dissolution of the hard boundary between data protected by data protection laws (GDPR) and anonymized data sets. To solve this problem, the TrustNShare project is creating a transparent data trust that acts as a trusted intermediary. This allows for secure and controlled data exchange, while offering flexible datasharing options, considering trustworthiness, risk tolerance, and healthcare interoperability. Empirical studies and participatory research will be conducted to develop a trustworthy and effective data trust model.

Evaluation of Deep Clustering for Diarization of Aphasic Speech.

Speaker attribution and labeling of single channel, multi speaker audio files is an area of active research, since the underlying problems have not been solved satisfactorily yet. This especially holds true for non-standard voices and speech, such as children and impaired speakers. Being able to perform speaker labelling of pathological speech would potentially enable the development of computer assisted diagnosis and treatment systems and is thus a desirable research goal. In this manuscript we investigate on the applicability of embeddings of audio signals, in the form of time and frequency-band based segments, into arbitrary vector spaces on diarization of pathological speech. We focus on modifying an existing embedding estimator such that it can be used for diarization. This is mainly done via clustering the time and frequency band dependant vectors and subsequently performing a majority vote procedure on all frequency dependent vectors of the same time segment to assign a speaker label. The result is evaluated on recordings of interviews of aphasia patients and language therapists. We demonstrate general applicability, with error rates that are close to what has been previously achieved in diarizing children's speech. Additionally, we propose to enhance the processing pipelines with smoothing and a more sophisticated, energy based, voting scheme.

Robust Comparison of Simultaneous EEG Recordings Using Kalman Filters and Gaussian Mixture Models.

In this manuscript we propose a novel method to compare simultaneously recorded electroencephalography (EEG) signals from different devices. Although standard methods like correlation and spectral analysis give quantitative answers to this question, these methods often penalize certain artifacts such as eye blinking too strongly. In our analysis we instead utilize an unsupervised labeling technique to evaluate the matching of two signals by comparing their label sequences. The proposed method was successfully tested on artificial data, where it showed a reduced deviation from the ground truth compared to the correlation coefficient. Furthermore, the method was applied on a real use-case to assess the quality of a low-cost EEG device compared to a clinical one. Here it showed more consistent results than the correlation coefficient, while it also did not rely on outlier removal prior to the analysis. However, the proposed method still suffers from accidental matches of labels, so that unrelated data sets may be assigned an unexpectedly high matching score. This paper suggests extensions to the proposed method, which could improve this issue.

Multi-modal and multiscale imaging approaches reveal novel cardiovascular pathophysiology in Drosophila melanogaster.

Establishing connections between changes in linear DNA sequences and complex downstream mesoscopic pathology remains a major challenge in biology. Herein, we report a novel, multi-modal and multiscale imaging approach for comprehensive assessment of cardiovascular physiology in Drosophila melanogaster We employed high-speed angiography, optical coherence tomography (OCT) and confocal microscopy to reveal functional and structural abnormalities in the hdp2 mutant, pre-pupal heart tube and aorta relative to controls. hdp2 harbor a mutation in wupA, which encodes an ortholog of human troponin I (TNNI3). TNNI3 variants frequently engender cardiomyopathy. We demonstrate that the hdp2 aortic and cardiac muscle walls are disrupted and that shorter sarcomeres are associated with smaller, stiffer aortas, which consequently result in increased flow and pulse wave velocities. The mutant hearts also displayed diastolic and latent systolic dysfunction. We conclude that hdp2 pre-pupal hearts are exposed to increased afterload due to aortic hypoplasia. This may in turn contribute to diastolic and subtle systolic dysfunction via vascular-heart tube interaction, which describes the effect of the arterial loading system on cardiac function. Ultimately, the cardiovascular pathophysiology caused by a point mutation in a sarcomeric protein demonstrates that complex and dynamic micro- and mesoscopic phenotypes can be mechanistically explained in a gene sequence- and molecular-specific manner.

Automatic Recognition of Epileptiform EEG Abnormalities.

Long term EEG examinations, for example during epilepsy diagnosis, can be performed more efficiently with support of automated abnormality detection. Currently, these methods are usually developed based on one specific database, which limits the possibilities of generalizations. Here, we present a machine learning solution for detection of interictal abnormal EEG segments optimized on the publically available TUH Abnormal EEG Corpus. The classifier is further re-trained and tested on several combinations of publicly available data sets. The results achieved internally on the datasets are comparable to the known state of the art, while training and testing on different sources produced accuracy in the range of 67.51% to 99.50%. Lower accuracy is achieved when the training data set is highly preprocessed and relatively small.

Automated Error Detection in Physiotherapy Training.

BACKGROUND: Manual skills teaching, such as physiotherapy education, requires immediate teacher feedback for the students during the learning process, which to date can only be performed by expert trainers. OBJECTIVES: A machine-learning system trained only on correct performances to classify and score performed movements, to identify sources of errors in the movement and give feedback to the learner. METHODS: We acquire IMU and sEMG sensor data from a commercial-grade wearable device and construct an HMM-based model for gesture classification, scoring and feedback giving. We evaluate the model on publicly available and self-generated data of an exemplary movement pattern executions. RESULTS: The model achieves an overall accuracy of 90.71% on the public dataset and 98.9% on our dataset. An AUC of 0.99 for the ROC of the scoring method could be achieved to discriminate between correct and untrained incorrect executions. CONCLUSION: The proposed system demonstrated its suitability for scoring and feedback in manual skills training.

The mobile sleep lab app: An open-source framework for mobile sleep assessment based on consumer-grade wearable devices.

BACKGROUND: Sleep disorders have a prevalence of up to 50% and are commonly diagnosed using polysomnography. However, polysomnography requires trained staff and specific equipment in a laboratory setting, which are expensive and limited resources are available. Mobile and wearable devices such as fitness wristbands can perform limited sleep monitoring but are not evaluated well. Here, the development and evaluation of a mobile application to record and synchronize data from consumer-grade sensors suitable for sleep monitoring is presented and evaluated for data collection capability in a clinical trial. METHODS: Wearable and ambient consumer-grade sensors were selected to mimic the functionalities of clinical sleep laboratories. Then, a modular application was developed for recording, processing and visualizing the sensor data. A validation was performed in three phases: (1) sensor functionalities were evaluated, (2) self-experiments were performed in full-night experiments, and (3) the application was tested for usability in a clinical trial on primary snoring. RESULTS: The evaluation of the sensors indicated their suitability for assessing basic sleep characteristics. Additionally, the application successfully recorded full-night sleep. The collected data was of sufficient quality to detect and measure body movements, cardiac activity, snoring and brightness. The ongoing clinical trial phase showed the successful deployment of the application by medical professionals. CONCLUSION: The proposed software demonstrated a strong potential for medical usage. With low costs, it can be proposed for screening, long-term monitoring or in resource-austere environments. However, further validations are needed, in particular the comparison to a clinical sleep laboratory.

Deterioration of R-Wave Detection in Pathology and Noise: A Comprehensive Analysis Using Simultaneous Truth and Performance Level Estimation.

OBJECTIVE: For long-term electrocardiography (ECG) recordings, accurate R-wave detection is essential. Several algorithms have been proposed but not yet compared on large, noisy, or pathological data, since manual ground-truth establishment is impossible on such large data. METHODS: We apply the simultaneous truth and performance level estimation (STAPLE) method to ECG signals comparing nine R-wave detectors: Pan and Tompkins (1985), Chernenko (2007), Arzeno et al. (2008), Manikandan et al. (2012), Lentini et al. (2013), Sartor et al. (2014), Liu et al. (2014), Arteaga-Falconi et al. (2015), and Khamis et al. (2016). Experiments are performed on the MIT-BIH database, TELE database, PTB database, and 24/7 Holter recordings of 60 multimorbid subjects. RESULTS: Existing approaches on R-wave detection perform excellently on healthy subjects (F-measure above 99% for most methods), but performance drops to a range of F = 90.10% (Khamis et al.) to F = 30.10% (Chernenko) when analyzing the 37 million R-waves of multimorbid subjects. STAPLE improves existing approaches (ΔF = 0.04 for the MIT-BIH database and ΔF = 0.95 for the TELE database) and yields a relative (not absolute) scale to compare algorithms' performances. CONCLUSION: More robust R-wave detection methods or flexible combinations are required to analyze continuous data captured from pathological subjects or that is recorded with dropouts and noise. SIGNIFICANCE: STAPLE algorithm has been adopted from image to signal analysis to compare algorithms on large, incomplete, and noisy data without manual ground truth. Existing approaches on R-wave detection weakly perform on such data.

Low-Cost Wearable for Fatigue and Back-Stress Measurement in Nursing.

In recent years the need for informal home care in European countries is growing quickly due to increased life expectancy and demographic change. Informal caregivers have to overcome many obstacles ranging from a lack of adequate training to misjudging their physical and psychological abilities. The aim of this project is to create a low cost wearable device, which unobtrusively measures the physical stress load on caregivers. Two parameters with the most impact on performance and well-being of the caregiver have been identified: (i) fatigue and (ii) back-stress. Based on the measurements, an alert is issued if caregivers are not performing a task correctly, or if they are overexerting themselves. This paper discusses the design of such device and description of an initial prototype, its advantages and possible further development and applications.

Wearable Sensors in Medical Education: Supporting Hand Hygiene Training with a Forearm EMG.

Lack of proper hand hygiene is a common source of hospital acquired infections. Training and evaluating efficiency in hand washing is therefore an important part of medical education. Here, we propose to use the Myo wearable armband to measure correctness of hand washing for mobile learning. Myo's sensors are designed in order to recognize the activity of the forearm, palm and fingers. Using signal processing and machine learning, the quality of the hand washing process can be estimated and used as evaluation in medical teaching. The project is in its initial phase, thus we present preliminary results and a vision of future development.