Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception.

Background: Over 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception.

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings.

Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia.

Blood glucose forecasting from temporal and static information in children with T1D.

Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level.

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study.

Background: Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study.

Objective: The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy.

Fetal Re-Identification in Multiple Pregnancy Ultrasound Images Using Deep Learning.

Ultrasound examinations during pregnancy can detect abnormal fetal development, which is a leading cause of perinatal mortality. In multiple pregnancies, the position of the fetuses may change between examinations. The individual fetus cannot be clearly identified.

WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography.

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps.

Prevalence and course of pregnancy symptoms using self-reported pregnancy app symptom tracker data.

During pregnancy, almost all women experience pregnancy-related symptoms. The relationship between symptoms and their association with pregnancy outcomes is not well understood. Many pregnancy apps allow pregnant women to track their symptoms.

Evaluating the Effectiveness of Mobile Health in Breast Cancer Care: A Systematic Review.

Breast cancer is affecting millions of people worldwide. If not appropriately handled, the side effects of different modalities of cancer treatment can negatively impact patients' quality of life and cause treatment interruptions. In recent years, mobile health (mHealth) interventions have shown promising opportunities to support breast cancer care.

Examining the Association Between Self-Reported Estimates of Function and Objective Measures of Gait and Physical Capacity in Lumbar Stenosis.

Objective: To evaluate the association of self-reported physical function with subjective and objective measures as well as temporospatial gait features in lumbar spinal stenosis (LSS).

Design: Cross-sectional pilot study.

Setting: Outpatient multispecialty clinic.

Gait features for discriminating between mobility-limiting musculoskeletal disorders: Lumbar spinal stenosis and knee osteoarthritis.

Background: Functional ambulation limitations are features of lumbar spinal stenosis (LSS) and knee osteoarthritis (OA). With numerous validated walking assessment protocols and a vast number of spatiotemporal gait parameters available from sensor-based assessment, there is a critical need for selection of appropriate test protocols and variables for research and clinical applications.

Research Question: In patients with knee OA and LSS, what are the best sensor-derived gait parameters and the most suitable clinical walking test to discriminate between these patient populations and controls?

Methods: We collected foot-mounted inertial measurement unit (IMU) data during three walking tests (fast-paced walk test-FPWT, 6-min walk test- 6MWT, self-paced walk test - SPWT) for subjects with LSS, knee OA and matched controls (N = 10 for each group).

Self-Powered Multiparameter Health Sensor.

Wearable health sensors are about to change our health system. While several technological improvements have been presented to enhance performance and energy-efficiency, battery runtime is still a critical concern for practical use of wearable biomedical sensor systems. The runtime limitation is directly related to the battery size, which is another concern regarding practicality and customer acceptance.

Reference-Free Adjustment of Respiratory Inductance Plethysmography for Measurements during Physical Exercise.

Objective: Respiratory inductance plethysmography (RIP) provides an unobtrusive method for measuring breathing characteristics. Accurately adjusted RIP provides reliable measurements of ventilation during rest and exercise if data are acquired via two elastic measuring bands surrounding the rib cage (RC) and abdomen (AB). Disadvantageously, the most accurate reported adjusted model for RIP in literature-least squares regression-requires simultaneous RIP and flowmeter (FM) data acquisition.

Unobtrusive heart rate estimation during physical exercise using photoplethysmographic and acceleration data.

Photoplethysmography (PPG) is a non-invasive, inexpensive and unobtrusive method to achieve heart rate monitoring during physical exercises. Motion artifacts during exercise challenge the heart rate estimation from wrist-type PPG signals. This paper presents a methodology to overcome these limitation by incorporating acceleration information.

Wearable real-time ecg monitoring with emergency alert system for scuba diving.

Medical diagnosis is the first level for recognition and treatment of diseases. To realize fast diagnosis, we propose a concept of a basic framework for the underwater monitoring of a diver's ECG signal, including an alert system that warns the diver of predefined medical emergency situations. The framework contains QRS detection, heart rate calculation and an alert system.

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy.

Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days.

Temporal correction of detected R-peaks in ECG signals: A crucial step to improve QRS detection algorithms.

In the last decade the interest for heart rate variability analysis has increased tremendously. Related algorithms depend on accurate temporal localization of the heartbeat, e.g.

Real-Time Respiratory Motion Analysis Using 4-D Shape Priors.

Respiratory motion analysis based on range imaging (RI) has emerged as a popular means of generating respiration surrogates to guide motion management strategies in computer-assisted interventions. However, existing approaches employ heuristics, require substantial manual interaction, or yield highly redundant information. In this paper, we propose a framework that uses preprocedurally obtained 4-D shape priors from patient-specific breathing patterns to drive intraprocedural RI-based real-time respiratory motion analysis.

Comparison of a priori calibration models for respiratory inductance plethysmography during running.

Respiratory inductive plethysmography (RIP) has been introduced as an alternative for measuring ventilation by means of body surface displacement (diameter changes in rib cage and abdomen). Using a posteriori calibration, it has been shown that RIP may provide accurate measurements for ventilatory tidal volume under exercise conditions. Methods for a priori calibration would facilitate the application of RIP.

ICA-based reduction of electromyogenic artifacts in EEG data: comparison with and without EMG data.

Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA).

Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal.

Hierarchical, multi-sensor based classification of daily life activities: comparison with state-of-the-art algorithms using a benchmark dataset.

Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity.

Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data.

Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment.

Somnography using unobtrusive motion sensors and Android-based mobile phones.

Sleep plays a fundamental role in the life of every human. The prevalence of sleep disorders has increased significantly, now affecting up to 50% of the general population. Sleep is usually analyzed by extracting a hypnogram containing sleep stages.