Background
New technologies, like socially assistive robots (SARs), may have the potential to support caregivers at home. Still, the evidence for people with dementia in home care is unclear because a lot of studies are performed in a laboratory or institutional setting, and mainly use robots in prototype stages.
Objective
This study aims to explore the effects of the refined, commercially-available, humanoid SAR Pepper combined with a tablet PC–based dementia training program (Coach Pepper) versus an exclusively tablet PC–based dementia training program on psychosocial and physical outcomes of people with dementia living at home, including caregivers and dementia trainers. We hypothesize that Coach Pepper has a more positive effect on the primary outcome motivation (stable or decreased apathy) of people with dementia.
Methods
A mixed methods study will be performed, including a randomized controlled, parallel, 2-arm study with a complementary qualitative part. This sample includes 40 PWD living at home and 40 relatives, each complemented with five professional caregivers and dementia trainers. The intervention group will receive Coach Pepper (a SAR connected with a tablet PC–based dementia training program), and the control group will receive exclusively tablet PC–based training without the SAR. The duration of the intervention will be three weeks per household. Data will be collected at baseline and during and after the intervention by standardized questionnaires, sensor data of the robot, and tablet PC, as well as semistructured interviews, focus groups, and observation.
Results
To date, no results are available for this study protocol. The study intervention started in May 2019 and will end in Spring 2020.
Conclusions
The intervention of this study can be seen as a nonpharmacological intervention, including cognitive and physical training by a robot. This study will help to further refine SAR for the specific needs of people with dementia living at home.
International Registered Report Identifier (IRRID)
DERR1-10.2196/14927
We introduce the discipline of Acoustic Geo-Sensing (AGS) that deals with the connection of acoustics and geoposition, i. e., 'local audio' -focussing on spatial rather than on temporal aspects. We motivate this field of research, and give an example by automatic determination of a cyclist's route between determined start and endpoints, the direction she advances on this route, and the progress made from cell-phone audio. The Graz Cell-phone Cycle Corpus of 16 hours audio is introduced to this end. A standardised acoustic feature set ensures reproducibility throughout extensive experimentation aiming to reveal maximal spatiotemporal resolution. In the result, principle feasibility is shown by unsupervised clustering and all presented tasks can be solved at high accuracies and correlation within Random Forest classification and Additive Regression.
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