Prediction of Freezing of Gait in Parkinson’s from Physiological Wearables: An Exploratory Study
Freezing of gait (FoG) is a common and severe gait impairment among patients with advanced Parkinson’s disease. FoG is associated with falls and negatively impacts the patient’s quality of life. We analyzed wearable collected electrocardiography (ECG) and skin- conductance response (SCR) data from 11 subjects who experience FoG in daily-life, and found statistically significant changes in ECG and SCR data just before the FoG episodes, compared to normal walking.
Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques
This study uses smart homes and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. Analysis reveals that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Permutation-based testing confirms that the sensor-based differences between these groups are statistically significant, which offers insights for automatic detection of the behavior impacts of Parkinson disease.
An Emerging Era in the Management of Parkinson’s Disease: Wearable Technologies and the Internet of Things
Wearable technologies connected through the Internet of Things (IoT) platform are revolutionizing patient care delivery. This new technology offers a bridge for the lateralization of the current healthcare system, incorporating patients as important actors in disease management, reducing costs and improving diagnostics and treatment outcomes. Characterized by large individual variability in clinical progression and treatment needs, Parkinson’s disease presents as an excellent model for this paradigm shift.
Special Section: Sensor Informatics and Quantified Self
Wearable sensors, combined with signal processing, machine learning, and the ability to collect large sets of human data comfortably 24/7, are advancing new ways to learn about human wellbeing. Measurements that used to be confined to short-term sampling in a lab or medical facility are now able to be conducted continuously while at home, work, sleep, or play.
This special issues highlights some of cutting edge research focusing on increased sensitivity, accuracy, and individualized calibration, as well as on the identification of human relevant and human readable patterns from unannotated data, is likely to drive a wide range of new applications whose reach extends far beyond conventional health and medical research…
Special Section: Machine Learning and Data Mining in Medical Imaging
Machine learning plays an essential role in medical imaging such as in computer-assisted diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. The aim of this special issue is to help advance the scientific research within the broad field of machine learning and data mining in medical imaging. This special issue was planned in conjunction with the MICCAI Workshop on Machine Learning in Medical Imaging (MLMI) 2014…
Identifying Physical Activity Profiles in COPD Patients Using Topic Models
With the growing amount of physical activity (PA) measures, the need for methods and algorithms that automatically analyse and interpret unannotated data increases. In this paper PA is seen as a combination of multi-modal constructs that can co-occur in different ways and proportions during the day…