Table 2 Overview of the six DCs

DC1

26.10.16

Explore the participants’ needs.

Understand the needs of the participants and especially identify their reported challenges in daily life.

Enable the researchers to propose solutions to the participants reported needs.

Presentation of cartoon illustrations of Helmer and Nora in three examples of challenging situations to encourage discussions around needs and challenges in everyday life:

i) an ordinary day at home

ii) out and about

iii) an ordinary night at home.

The research team developed the illustrations of Helmer and Nora and performed a dry-run café.

Written instructions were developed.

DC2

14.12.16

Dissemination of the potential of future solutions via examples.

Show examples on how automation and smart-home technology can be helpful in daily life.

Discuss four examples of technology to get feedback from the participants on the usefulness and acceptability of these solutions.

Four cartoons were presented:

i. the bathroom and bedside light switch on automatically when Helmer gets out of bed at night.

ii. An alarm is activated when Nora falls down in the kitchen. Her daughter gets a message, and they can have a talk via loudspeakers while waiting for assistance to come. The camera can also be turned on (See Fig. 1).

iii. A voice message reminds Nora not to forget her wallet when she is about to leave her apartment without it.

iv. Helmer in his apartment can get information on his tablet as to who is in the cafeteria so that he can consider to joining them.

The needs were discussed in the team, and several

technology solutions that were assessed as relevant for the project

as well as possible to realize within the project’s duration,

were mapped to these needs.

A possible smart-home sensor system was proposed to the project team and evaluated.

Dry-run.

DC3

6.4.17

Test and evaluate low-tech existing solutions.

Understand the participants views aimed at an aquaintance with available low-technology assistive solutions by hands-on experiences with prototypes.

Demonstration of and testing simple available technological solutions:

i. Helmer has a portable light switch that turns on/off the light that shows the way to the bathroom. The light can also be controlled by a tablet.

ii. Nora pushes a button on the wall or tablet before leaving the apartment and a voice tells her if the terrace door is open or the fridge is open or the stove is on.

iii. Nora demonstrates that by pressing one button on the tablet her favorite TV channel is switched on.

Map available low-tech solutions to a selection of needs.

Make mock-up versions og solutions.

Dry-run.

DC4

31.5.17

To get feedback on a fall detection system.

Recruitment of participants for a field trial of existing technology.

To get feedback on the usability and applicability of a fall detecting module and service based on Microsoft Kinect.

Invitation to test simple technology.

Fall detection:

- demonstration of a fall detection system using Microsoft Kinect.

- presentation of the fall detection and alert cartoon with Nora (reuse of cartoon illustration in DC2).

Proposed system to install:

i. physical switch by the bedside to turn the bathroom lamp on and off remotely, when standing up from bed at night.

ii. vocal message when leaving the apartment if the stove or coffee machine is on, or the balcony door or fridge is open.

The residents were invited to try these solutions at home for four weeks and give their opinion on usability and acceptability, as well as on how we could improve the solutions. Eight residents consented to take part in the trial, and one of them, Hilda, consented to participate in this feasibility study. The residents could choose which functionalities they wanted installed in their home.

Define solutions that are expected to have value for the participants based on the info from the previous cafés. In order to both get feedback on user experience, and in exchange for their providing data for further studies.

Dry-run.

DC5

11.1.18

Recruitment of participants for a field trial to provide data for Machine Learning studies

Explain machine learning and demonstrate RoomMate as fall detection system and alert service.

To get feedback on three examples of future functions that could be provided using machine learning.

Invitation to participate in a trial of the RoomMate as fall detection and alert service as well as allow data collection for the development of future support functions.

Four cartoons were presented on future functions:

i. Fall risk assessment: using RoomMate it is possible to deduce Helmer’s motion pattern and whether he is unsteady or his balance has deteriorated. The system can notify Helmer himself, the host, or Helmer’s family.

ii. Hazardous situation alert: Using computer vision it is possible to register that Nora is in bed and has forgotten to switch off the stove. A ‘smart’ system should be able to adapt to Nora’s routines and turn the stove off if it is forgotten and there is a danger for overheating.

iii. Comfort automation: the light in the kitchen and the coffee machine are automatically switched off when Nora goes to bed at night. The lights in the apartment are automatically controlled to give her better light when she gets up at night: The bedside lamp turns on when she gets up at night and the bathroom lights switch on if she intends to go to the bathroom; and turn off sufficient time after she has left the bathroom. The bedside lamp turns off when she finishes reading and turn round to sleep..

iv. Assistance – switching-on the TV: using computer vision based on the data from RoomMate, the system understands that Nora struggles to switch on the TV and switches it on automatically at the channel Nora usually watches on this day and time.

The residents were recruited to get installed a smart-home system with 17 sensors, as well as two RoomMate fall detectors, one in the living room and one in the bedroom.

Define solutions that are expected to have value for the participants based on the info from the previous cafés. In order to both get feedback on user experience, and in exchange for their providing data for further studies.

Meeting between researchers and one resident for discussing how to present machine learning in an understandable way.

Dry-run.

DC6

Closing the project

Thank all the participants for their participation.

Discussed their opinions on their participating in the project.