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Older adults relatives, playing the role of primary caregivers, face an emotional burden as elderly dependence increments. Interventions to help older adults to maintain activities of daily living, such as managing their medications, may alleviate family caregivers’ burden. Medication adherence supported by ambient computing technologies has been previously explored for improving older adults’ medication compliance. However, their impact has not been evaluated on family caregivers. In this study we assessed the utilization feasibility and acceptance of a Medication Ambient Display for supporting elderly to autonomously medicate. The system was placed in the homes of two seniors-caregivers dyads during 4 weeks. The attitudes and preferences of these volunteers were assessed through survey instruments. Overall experiences were consistently positive even though some usability issues were also reported. Responses from our participants indicated that they appreciated the potential of this technology to make older adults more responsible for taking their medications, which provided caregivers with peace of mind.

Exergames are currently used as a new tool for medical purposes. In this context, this paper presents an overview of the approaches used to gather evidence about the use and impact of exergames-based interventions on elderly. In total, 2306 abstracts were returned from a database search, yielding 52 relevant papers. Our analysis found a group of papers mostly published in engineering forums with emphasis on evaluating novel technologies and an evaluation providing low-evidence, another group of studies, published mostly in medical journals, use more conventional technologies, but conduct more comprehensive evaluations from which stronger evidence is obtained.

User indoor positioning has been under constant improvement especially with the availability of new sensors integrated into the modern mobile devices. These sensory devices allow us to exploit not only infrastructures made for every day use, such as Wi-Fi, but also natural infrastructure, as is the case of natural magnetic fields. From our experience working with mobile devices and Magnetic-Field based location systems, we identify some issues that should be addressed to improve the performance of a Magnetic-Field based system, such as a reduction of the data to be analyzed to estimate an individual location. In this paper we propose a feature extraction process that uses magnetic-field temporal and spectral features to acquire a classification model using the capabilities of mobile phones. Finally, we present a comparison against well known spectral classification algorithms with the aim to ensure the reliability of the feature extraction process.