ABSTRACT
Objective: Home-based chemotherapy is widely used and offers advantages in terms of patient-centeredness, hospital capacity, and cost-effectiveness. However, in practice, patients experience difficulties with self-management and handling the elastomeric infuser. In this study, we aimed to explore the experiences of patients undergoing home-based chemotherapy based on patients' and nurses' perspectives. Additionally, we aimed to identify patients' unmet needs. Methods: A qualitative descriptive study was conducted in a tertiary hospital in South Korea. Ten patients undergoing home-based chemotherapy and ten nurses with experience in home-based chemotherapy participated. Data were collected by using semi-structured individual interviews and analyzed by using inductive content analysis. Results: Four main categories were identified based on the interviews: (1) ambivalence regarding comfort vs. enduring the discomfort, (2) acceptance of the discomfort as a part of them, (3) the need for more precise, numerical measurements, and (4) the realization that they need similar hands-on care at home as in a hospital. Conclusions: Although patients were satisfied with home-based chemotherapy, they were enduring the difficulties they experienced at home alone. Nurses should make an effort to identify patient needs and devise tailored nursing interventions to improve their safety.
ABSTRACT
Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.
