Studies foresee a dramatic increase in the elderly population of Western Europe over the next decades, putting pressure on healthcare systems. Healthcare robots are developed to facilitate independent living for elderly people. This article aims to provide a qualitative analysis of recent projects in healthcare robotics (2008–2024) and proposes new research directions for healthcare robots for older adults. We provide an overview of current research and a roadmap for upcoming research. Our study began with a literature search using four databases. Searches were performed for articles from research projects containing the words “elderly care,” “assisted aging,” “health monitoring,” or “elderly health.” Additional exclusion criteria were used to focus on elderly healthcare and utilization of commercial robotic systems. Resulting from this endeavor, 20 recent research projects are described and categorized in this article. Then, these projects were analyzed using thematic analysis. Our findings are summarized in common themes: Most projects have a strong bias towards care robots’ functionalities; robots are often seen as outsiders in care settings; there is an emphasis on robots as commercial products; and there is some limited attention to the design and ethical aspects of care robots, but very little attention to their legal aspects. The article concludes with key points representing a roadmap for future research addressing robotics for the elderly.

Striking a balance between utility and privacy holds significant importance in systems that rely on sensor utilization, such as robots. This balance is even more vital in care robots, given the sensitivity of personal data and the necessity for privacy-preserving monitoring to ensure user comfort. This paper presents a comprehensive investigation into the utility-privacy trade-off concerning different vision-based sensors. Specifically, RGB cameras, color and mono-color thermal cameras, and depth sensors are compared, considering technical aspects and users’ perception of privacy. The technical analysis addresses human pose tracking, human presence detection, human vital sign monitoring, and human facial and emotion recognition. The quantitative examination of sensors in real-life scenarios highlights the mono-color thermal camera’s effectiveness for user monitoring. Particularly, this sensor excels in challenging human presence detection scenarios compared to RGB cameras. Furthermore, interview and survey studies, encompassing two different age groups were carried out to compare how sensors are perceived in terms of user privacy. The quantitative and qualitative assessments of users’ feedback in these studies reveal that apart from depth sensors, thermal mono-color, and thermal color sensors are perceived as better at preserving user privacy compared to RGB cameras. The analysis includes the influence of participant age on privacy perception, indicating non-significant effects. Considering both the technical assessment and user preferences, the mono-color thermal camera emerges as the optimal choice for human monitoring purposes.

This paper presents a Nonlinear Model Predictive Control-based Reinforcement Learning (NMPC-based RL) framework for robot manipulators. The controller is developed to address the motion planning problem for robot manipulators in the presence of obstacles. The proposed control scheme includes a parametrized NMPC structure used as an approximator for the RL framework’s value function and action-value function. In the NMPC structure, the cost function, system constraints, and the manipulator’s model are parameterized. The Q-Learning algorithm based on the Temporal Difference method adjusts the parameters of the NMPC to increase the closed-loop performance of the whole control scheme. The controller has been applied to a 6-degrees-of-freedom (DoF) model of a robot manipulator, aimed at moving its end-effector to reach the desired pose when static obstacles are in the robot’s workspace. Numerical simulations demonstrate that the proposed controller can effectively control the end-effector’s pose in such a way as to avoid any collisions between the manipulator and the obstacles. It is shown that the learning capability of the proposed NMPC-based RL framework can enhance the efficiency of the control loop up to 21%.

Introducing robots in healthcare facilities and homes may reduce the workload of healthcare personnel while providing the users with better and more available services. It may also contribute to interactions that are engaging and safe against transmitting contagious diseases for senior adults. A major challenge in this regard is to design and adapt the robot’s behavior based on the requirements and preferences of the different users. In this paper, we report a conducted use study on how people perceive different kinds of robot encounters. We had two groups of target users: one with senior residents at a care center and another with young students at a university, which would be representative for the visitors and care volunteers in the facility. Several common scenarios have been created to evaluate the perception of the robot’s behavior by the participants. Two sets of questionnaires were used to collect feedback on the behavior and the general perception of the users about the robot´s different styles of behavior. An exploratory analysis of the effect of age shows that the age of the targeted user group should be considered as one of the main criteria when designing the social parameters of a care robot, as seniors preferred slower speed and closer distance to the robot. The results can contribute to improving a future robot’s control to better suit users from different generations.

Recognizing human activities from sensor data is one of the key areas of image processing, computer vision, and pattern recognition researches today. The target of human activity recognition (HAR) is usually to detect and analyze distinguished activities from the data acquired via different sensors (e.g. thermal cameras). This work proposes a HAR approach from videos recorded via a thermal camera. Skeletons of human bodies are extracted from thermal frames using an opensource deep convolutional neural network (CNN)-based approach named OpenPose. It is generally applied on videos of typical color cameras. However, this work adopts OpenPose on thermal images to extract useful features so that the HAR system can be deployed in environments with low lights as well. Once skeletons of human silhouettes are obtained from the thermal images, robust spatiotemporal features are extracted followed by discriminant analysis. Finally, the discriminant features are fed into a deep recurrent neural network (RNN) for activity training and recognition. The proposed HAR method can be applied to monitor the users such as elderly in both bright and dark environments to prolong their independent life, unlike other typical color cameras which are generally applied in bright environments.

Recent advances in image processing and computer vision have driven to numerous initiatives to recognize human activities from video data. This work proposes a human activity recognition approach using robust translation and scale-invariant body silhouette features recurrent neural network. First, Human body silhouette is extracted from a depth image after background subtraction. Then, body parts are segmented using random forests to get corresponding body skeleton in the image. Furthermore, scale-invariant skeleton features are extracted by representing the body joints in the spherical coordinate system. Then, the skeleton features are augmented with the motion features of the skeleton in consecutive frames. To combine with the skeleton features, Radon transformation is applied on the depth silhouettes to extract translation and scale-invariant silhouette features. The robust features extracted from the depth image sequences are then applied to a deep recurrent neural network for activity training and recognition. The proposed approach shows the superiority over other approaches by achieving greater than 98 % mean recognition rate on private and public datasets where others can yield around 95%.