Mobile eHealth Platform for Home Monitoring of Bipolar Disorder

Abstract

People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur. MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.

Personal Health Metrics Data Management Using Symmetric 5G Data Channels

“…The algorithmic apparatus that is described in [28] has been considered during the specification of the integrated data management system's data processing components [29,30]. Nevertheless, the comprehensive validity assessment that we conducted [31] showed that even the optimized homomorphic encryption models are not feasible [32] for the timely processing of the acquired medical data on the mobile client devices [33]. Moreover, it is significant to mention the relevant contributions that pertain to the realm of ubiquitous systems.…”

An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors

“…[ 36 ] may be used during the design of data processing components that are part of relevant integrated data management systems [ 37 ]. It is important to note that the comprehensive validity evaluation that we conducted [ 34 ] proves that even certain improved homomorphic encryption approaches are not adequate [ 51 , 52 ] relative to the timely processing of the collected medical data on the client side [ 35 , 53 ]. Furthermore, it is important to note the papers [ 54 , 55 ] that are connected to the full scope of ubiquitous systems.…”

Acoustic and prosodic information for home monitoring of bipolar disorder

“…Instead, the NYMPHA-MD project 11 defined a framework for continuous patient monitoring to identify early warning signs of deviations in mood and attitudes suggesting the onset of a depressive or maniac episode, which, in turn, will allow for early intervention. 12 PULSO Ediciones S.L. won the NYMPHA-MD Pre-Commercial Procurement bid with the MoodRecord project, where the Universitat Pompeu Fabra (Barcelona) developed the speech module analysis.…”