AI Medical Compendium Journal:
JMIR mHealth and uHealth

Showing 21 to 30 of 40 articles

Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study.

JMIR mHealth and uHealth
BACKGROUND: There is a growing need for the integration of patient-generated health data (PGHD) into research and clinical care to enable personalized, preventive, and interactive care, but technical and organizational challenges, such as the lack of...

Upper-Limb Motion Recognition Based on Hybrid Feature Selection: Algorithm Development and Validation.

JMIR mHealth and uHealth
BACKGROUND: For rehabilitation training systems, it is essential to automatically record and recognize exercises, especially when more than one type of exercise is performed without a predefined sequence. Most motion recognition methods are based on ...

Comparison of the Validity and Generalizability of Machine Learning Algorithms for the Prediction of Energy Expenditure: Validation Study.

JMIR mHealth and uHealth
BACKGROUND: Accurate solutions for the estimation of physical activity and energy expenditure at scale are needed for a range of medical and health research fields. Machine learning techniques show promise in research-grade accelerometers, and some e...

Acute Exacerbation of a Chronic Obstructive Pulmonary Disease Prediction System Using Wearable Device Data, Machine Learning, and Deep Learning: Development and Cohort Study.

JMIR mHealth and uHealth
BACKGROUND: The World Health Organization has projected that by 2030, chronic obstructive pulmonary disease (COPD) will be the third-leading cause of mortality and the seventh-leading cause of morbidity worldwide. Acute exacerbations of chronic obstr...

Pain Assessment Tool With Electrodermal Activity for Postoperative Patients: Method Validation Study.

JMIR mHealth and uHealth
BACKGROUND: Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hosp...

Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study.

JMIR mHealth and uHealth
BACKGROUND: In recent years, mobile-based interventions have received more attention as an alternative to on-site obesity management. Despite increased mobile interventions for obesity, there are lost opportunities to achieve better outcomes due to t...

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach.

JMIR mHealth and uHealth
BACKGROUND: Mental health disorders affect multiple aspects of patients' lives, including mood, cognition, and behavior. eHealth and mobile health (mHealth) technologies enable rich sets of information to be collected noninvasively, representing a pr...

Deep Learning-Based Multimodal Data Fusion: Case Study in Food Intake Episodes Detection Using Wearable Sensors.

JMIR mHealth and uHealth
BACKGROUND: Multimodal wearable technologies have brought forward wide possibilities in human activity recognition, and more specifically personalized monitoring of eating habits. The emerging challenge now is the selection of most discriminative inf...

Using Machine Learning and Smartphone and Smartwatch Data to Detect Emotional States and Transitions: Exploratory Study.

JMIR mHealth and uHealth
BACKGROUND: Emotional state in everyday life is an essential indicator of health and well-being. However, daily assessment of emotional states largely depends on active self-reports, which are often inconvenient and prone to incomplete information. A...

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks.

JMIR mHealth and uHealth
BACKGROUND: Schizophrenia spectrum disorders (SSDs) are chronic conditions, but the severity of symptomatic experiences and functional impairments vacillate over the course of illness. Developing unobtrusive remote monitoring systems to detect early ...