Background: Loneliness and social isolation in older age are considered major public health concerns and research on technology-based solutions is growing rapidly. This scoping review of reviews aims to summarize the communication technologies (CTs) (review question RQ1), theoretical frameworks (RQ2), study designs (RQ3), and positive effects of technology use (RQ4) present in the research field. Methods: A comprehensive multi-disciplinary, multi-database literature search was conducted. Identified reviews were analyzed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. A total of N = 28 research reviews that cover 248 primary studies spanning 50 years were included. Results: The majority of the included reviews addressed general internet and computer use (82% each) (RQ1). Of the 28 reviews, only one (4%) worked with a theoretical framework (RQ2) and 26 (93%) covered primary studies with quantitative-experimental designs (RQ3). The positive effects of technology use were shown in 55% of the outcome measures for loneliness and 44% of the outcome measures for social isolation (RQ4). Conclusion: While research reviews show that CTs can reduce loneliness and social isolation in older people, causal evidence is limited and insights on innovative technologies such as augmented reality systems are scarce.

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments.

We present an important aspect of our human-robot communication interface which is being developed in the context of our long-term research framework PERSES dealing with highly interactive mobile companion robots. Based on a multi-modal people detection and tracking system, we present a hierarchical neural architecture that estimates a target point at the floor indicated by a pointing pose, thus enabling a user to navigate a mobile robot to a specific target position in his local surroundings by means of pointing. In this context, we were especially interested in determining whether it is possible to accomplish such a target point estimator using only monocular images of low-cost cameras. The estimator has been implemented and experimentally investigated on our mobile robotic assistant HOROS. Although only monocular image data of relatively poor quality were utilized, the estimator accomplishes a good estimation performance, achieving an accuracy better than that of a human viewer on the same data. The achieved recognition results demonstrate that it is in fact possible to realize a user-independent pointing direction estimation using monocular images only, but further efforts are necessary to improve the robustness of this approach for everyday application.

This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human–robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper–plastic chessboard calibration target with an internal active light source (near-infrared and visible light). By brief heating, the calibration target could be simultaneously and legibly captured by all cameras. Based on the multimodal dataset captured by our sensor system, PointNet, PointNet++, and RandLA-Net are utilized to verify the effectiveness of applying multimodal point cloud data for hand–object segmentation. These networks were trained on various data modes (XYZ, XYZ-T, XYZ-RGB, and XYZ-RGB-T). The experimental results show a significant improvement in the segmentation performance of XYZ-RGB-T (mean Intersection over Union: $82.8\%$ by RandLA-Net) compared with the other three modes ($77.3\%$ by XYZ-RGB, $35.7\%$ by XYZ-T, $35.7\%$ by XYZ), in which it is worth mentioning that the Intersection over Union for the single class of hand achieves $92.6\%$.