In the Digital Era, the authors witness a change in the relationship between the patient and the care-giver or Health Maintenance Organization's providing the health services. Another fact is the use of various technologies to increase the effectiveness and quality of health services across all primary and secondary users. These technologies range from telemedicine systems, decision making tools, online and self-services applications and virtual agents; all providing information and assistance. The common thread between all these digital implementations, is they all require human machine interfaces. These interfaces must be interactive, user friendly and inviting, to create user involvement and cooperation incentives. The challenge is to design interfaces which will best fit the target users and enable smooth interaction especially, for the elderly users. Avatars and Virtual Agents are one of the interfaces used for both home care monitoring and companionship. They are also inherently multimodal in nature and allow an intimate relation between the elderly users and the Avatar. This study discusses the need and nature of these relationship models, the challenges of designing for the elderly. The study proposes key features for the design and evaluation in the area of assistive applications using Avatar and Virtual agents for the elderly users.

The design and implementation of a framework that facilitates the development of Mobile Health applications to manage the communications with biomedical sensors in compliance with the CEN ISO/IEEE 11073 standard family are presented. The framework includes a set of functional modules that are responsible, among other tasks, of the communication of sensors and the processing and storage of data. The mobile terminal acts as an intermediary or hub, collecting and presenting the data received in a standardised way, regardless of the sensor type used. In this context, as proof of concept it is presented a mobile app built on the top of the framework to manage the communications with a smart fall detector.

This study focused on the development and usability evaluation of EnCare diagnostics (ECD) and the brain fit plan (BFP) in healthy older adults, cognitively impaired and physically impaired individuals. ECD is proposed as a novel solution to cognitive assessment based on colour selection. BFP is a novel solution to personalised cognitive stimulation. The study consisted of two trials designed to evaluate the usability of the apps. Trial 1 involved 11 healthy older adults and four older adults with physical impairments who undertook ECD and mini-mental state examination (MMSE) once per month for 4 months with only those with physical impairments also completing the BFP daily. Trial 2 involved eight older adults diagnosed with early stage dementia who completed MMSE and ECD once per month for 6 months. In Trial 1, 10 out of 11 participants enjoyed the trial and managed the usability of the app easily. A 75% drop out was observed in response to the BFP with issues of dexterity and lack of understanding on how to use the technology being the main reasons for lack of compliance. Four out of eight participants completed Trial 2 with most of the participants having no usability issues. This usability study demonstrated that ECD is highly acceptable in both healthy older adults and those with early stage dementia when given the shorter versions to accommodate their diagnosis. The BFP was not suited to this population of participants.

Adult day services (ADS) are receiving increased attention due to shifts in health and social policies encouraging home and community-based services for an aging population. However, some issues hinder ADS effectiveness such as the need of diversification of interventions focused on elderly people (with different needs and dependency levels) or the undertrained staff. This work presents an analysis of current ADS gaps from interviews to Spanish geriatric experts and scientific literature. An innovative user-based technology implementation is approached to address the most relevant missing gaps identified: formal assessment of user's skills and status before joining the ADS, establishment of personalised interventions for each user, user classification on groups according to characteristics of user members etc. This approach allows to assess elderly over time to generate knowledge about his/her psychological and physical conditions and about the general factors that accelerate/delay deterioration caused by age.

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Functional magnetic resonance imaging (fMRI) can generate brain images that show neuronal activity due to sensory, cognitive or motor tasks. Haemodynamic response function (HRF) may be considered as a biomarker to discriminate the Alzheimer disease (AD) from healthy ageing. As blood-oxygenation-level-dependent fMRI signal is much weak and noisy, particularly for the elderly subjects, a robust method is necessary for HRF estimation to efficiently differentiate the AD. After applying minimum description length wavelet as an extra denoising step, deconvolution algorithm is here employed for HRF estimation, substituting the averaging method used in the previous works. The HRF amplitude peaks are compared for three groups HRF of young, non-demented and demented elderly groups for both vision and motor regions. Prior works often reported significant differences in the HRF peak amplitude between the young and the elderly. The authors’ experimentations show that the HRF peaks are not significantly different comparing the young adults with the elderly (either demented or non-demented). It is here demonstrated that the contradictory findings of the previous studies on the HRF peaks for the elderly compared with the young are originated from the noise contribution in fMRI data.