The number of people with dementia is increasing worldwide. At the same time, family and professional caregivers’ resources are limited. A promising approach to relieve these carers’ burden and assist people with dementia is assistive technology. In order to be useful and accepted, such technologies need to respect the values and needs of their intended users. We applied the value sensitive design approach to identify values and needs of patients with dementia and family and professional caregivers in respect to assistive (...) technologies to assist people with dementia in institutionalized care. Based on semi-structured interviews of residents/patients with cognitive impairment, relatives, and healthcare professionals (10 each), we identified 44 values summarized by 18 core values. From these values, we created a values’ network to demonstrate the interplay between the values. The core of this network was caring and empathy as most strongly interacting value. Furthermore, we found 36 needs for assistance belonging to the four action fields of activity, care, management/administration, and nursing. Based on these values and needs for assistance, we created possible use cases for assistive technologies in each of the identified four action fields. All these use cases already are technologically feasible today but are not currently being used in healthcare facilities. This underlines the need for development of value-based technologies to ensure not only technological feasibility but also acceptance and implementation of assistive technologies. Our results help balance conflicting values and provide concrete suggestions for how engineers and designers can incorporate values into assistive technologies. (shrink)

ObjectiveTo determine whether gait and accelerometric features can predict disorientation events in young and older adults.MethodsCognitively healthy younger and older participants navigated on a treadmill through a virtual representation of the city of Rostock featured within the Gait Real-Time Analysis Interactive Lab system. We conducted Bayesian Poisson regression to determine the association of navigation performance with domain-specific cognitive functions. We determined associations of gait and accelerometric features with disorientation events in real-time data using Bayesian generalized mixed effect models. The accuracy (...) of gait and accelerometric features to predict disorientation events was determined using cross-validated support vector machines and Hidden Markov models.ResultsBayesian analysis revealed strong evidence for the effect of gait and accelerometric features on disorientation. The evidence supported a relationship between executive functions but not visuospatial abilities and perspective taking with navigation performance. Despite these effects, the cross-validated percentage of correctly assigned instances of disorientation was only 72% in the SVM and 63% in the HMM analysis using gait and accelerometric features as predictors.ConclusionDisorientation is reflected in spatiotemporal gait features and the accelerometric signal as a potentially more easily accessible surrogate for gait features. At the same time, such measurements probably need to be enriched with other parameters to be sufficiently accurate for individual prediction of disorientation events. (shrink)