People with dementia (PwD) experience widespread cognitive impairment, leading to challenges in carrying out routine tasks known as activities of daily living (ADLs) and especially more complex instrumental ADLs (IADLs). Without adequate support, PwD becomes highly susceptible to a loss of independence and vulnerability. Various assistive technologies (ATs) have been proposed to assist PwD with mild conditions to complete some IADL tasks independently. However, most existing AT devices provide limited IADL support to PwD with little or no user-specific customization. This paper presents our early development of a new homecare assistive tool for PwD with mild conditions, called CATcare (Cognitive Assistive Technology Care). Our CATcare system can be operated and configured on a smartphone or smart glasses and further customized by a caregiver according to the specific IADL needs of a care recipient. It can provide IADL-specific step-by-step cueing, prompting, and timely feedback to assist PwD in accomplishing the task of interest. Our research leverages the capabilities of transfer learning from AI models in indoor localization, object detection, and Natural Language Processing (NLP) and shows the potential and promise to develop a customizable and personalizable CATcare tool. This CATcare system is intended to improve the life quality of PwD and reduce the burden of their caregivers.

To support indoor scene understanding, room layouts have been recently introduced that define a few typical space configurations according to junctions and boundary lines. In this paper, we study camera pose estimation from eight common room layouts with at least two boundary lines that is cast as a PnL (Perspective-n-Line) problem. Specifically, the intersecting points between image borders and room layout boundaries, named image outer corners (IOCs), are introduced to create additional auxiliary lines for PnL optimization. Therefore, a new PnL-IOC algorithm is proposed which has two implementations according to the room layout types. The first one considers six layouts with more than two boundary lines where 3D correspondence estimation of IOCs creates sufficient line correspondences for camera pose estimation. The second one is an extended version to handle two challenging layouts with only two coplanar boundaries where correspondence estimation of IOCs is ill-posed due to insufficient conditions. Thus the powerful NSGA-II algorithm is embedded in PnL-IOC to estimate the correspondences of IOCs. At the last step, the camera pose is jointly optimized with 3D correspondence refinement of IOCs in the iterative Gauss-Newton algorithm. Experiment results on both simulated and real images show the advantages of the proposed PnL-IOC method on the accuracy and robustness of camera pose estimation from eight different room layouts over the existing PnL methods. The code is available at https://github.com/XiaoweiChenOSU/PnL-IOC.