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I och med den fjärde industriella revolutionen – Industri 4.0 – kommer en nygeneration av teknik att finnas tillgänglig. Det förutspås att robotik och virtualreality kommer att transformera inte bara arbetsplatser utan även utvecklaandra domäner, såsom smarta städer och möjligheten till livsstils- och hälsomonitoreringhemma.Antalet tillgängliga konsument- och medicintekniska produkter ökar irask takt. Hälso- och sjukvårdssystemet ställs inför utmaningar, såsom behovetav att utveckla verktyg för att hantera ny teknologi men också att förändraarbetsprocesser och anpassa organisationen för att kunna hantera teknologin.

Det här kapitlet ger en översikt över kommande teknologier, förslag på hurteknologi kan användas i hemmiljöer, en översikt över hur sådan teknik utvärderatssamt inte minst en reflektion kring hur dessa teknologier kan harmoniseramed nuvarande organisatoriska processer.

This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

This paper examines whether social cues, such as facial expressions, can be used to adapt and tailor a robot-assisted training in order to maximize performance and comfort. Specifically, this paper serves as a basis in determining whether key facial signals, including emotions and facial actions, are common among participants during a physical and cognitive training scenario. In the experiment, participants performed basic arm exercises with a social robot as a guide. We extracted facial features from video recordings of participants and applied a recursive feature elimination algorithm to select a subset of discriminating facial features. These features are correlated with the performance of the user and the level of difficulty of the exercises. The long-term aim of this work, building upon the work presented here, is to develop an algorithm that can eventually be used in robot-assisted training to allow a robot to tailor a training program based on the physical capabilities as well as the social cues of the users.

The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

This article reports on the EU project ExCITE with specific focus on the technical development of the telepresence platform over a period of 42 months. The aim of the project was to assess the robustness and validity of the mobile robotic telepresence (MRP) system Giraff as a means to support elderly people and to foster their social interaction and participation. Embracing the idea of user-centered product refinement, the robot was tested over long periods of time in real homes. As such, the system development was driven by a strong involvement of elderly people and their caregivers but also by technical challenges associated with deploying the robot in real-world contexts. The results of the 42-months’ long evaluation is a system suitable for use in homes rather than a generic system suitable, for example, in office environments.

Auditory immersion plays a significant role in generating a good feeling of presence for users driving a telepresence robot. In this paper, one of the key characteristics of auditory immersion - sound source localization (SSL) - is studied from the perspective of those who operate telepresence robots from remote locations. A prototype which is capable of delivering soundscape to the user through Interaural Time Difference (ITD) and Interaural Level Difference (ILD) using the ORTF stereo recording technique was developed. The prototype was evaluated in an experiment and the results suggest that the developed method is sufficient for sound source localization tasks.

This paper presents an image-based cooperative driving system for telepresence robot, which allows safe operation in indoor environments and is meant to minimize the burden on novice users operating the robot. The paper focuses on one emerging telepresence robot, namely, mobile remote presence systems for social interaction. Such systems brings new opportunities for applications in healthcare and elderly care by allowing caregivers to communicate with patients and elderly from remote locations. However, using such systems can be a difficult task particularly for caregivers without proper training. The paper presents a first implementation of a vision-based cooperative driving enhancement to a telepresence robot. A preliminary evaluation in the laboratory environment is presented.

Mobile robotic telepresence systems used for social interaction scenarios require that users steer robots in a remote environment. As a consequence, a heavy workload can be put on users if they are unfamiliar with using robotic telepresence units. One way to lessen this workload is to automate certain operations performed during a telepresence session in order to assist remote drivers in navigating the robot in new environments. Such operations include autonomous robot localization and navigation to certain points in the home and automatic docking of the robot to the charging station. In this paper we describe the implementation of such autonomous features along with user evaluation study. The evaluation scenario is focused on the first experience on using the system by novice users. Importantly, that the scenario taken in this study assumed that participants have as little as possible prior information about the system. Four different use-cases were identified from the user behaviour analysis.

Many new technologies claiming to support independent living and prolonged possibilities of aging in place have been developed. To support independent living and increase the sense of safety and security both for the elderly themselves and for their relatives, the technologies have to be easily adaptable to match the divergent user’s personal expectations and needs. The study reported in this paper was conducted as seven case studies where a sensor network was deployed in homes of people with a self-perceived memory decline. We describe problems related to adaptive personalization of such technology in real settings and discuss what consequences these problems may have for the elderly people's and their relatives willingness to use the technology. Our results indicate that a lack of sufficient possibilities to adaptive personalization of the system makes it difficult to address individual user's expectations and needs. This, in turn, leads to a decreased trustworthiness of the technology and a risk of unwillingness to use the technology.