Increasing numbers of patients with disabilities or elderly people with mobility issues often suffer from a pressure ul-cer. The affected areas need regular checks, but they have a difficulty in accessing a hospital. Some remote diagnosis systems are being used for them, but there are limitations in checking a patient’s status regularly. In this paper, we pre-sent a remote medical assistant that can help pressure ulcer management with image processing techniques. The pro-posed system includes a mobile application with a deep learning model for wound segmentation and analysis. As there are not enough data to train the deep learning model, we make use of a pre-trained model from a relevant domain and data augmentation that is appropriate for this task. First of all, an image pre-processing method using bilinear inter-polation is used to resize images and normalize the images. Second, for data augmentation, we use rotation, reflection, and a watershed algorithm. Third, we use a pre-trained deep learning model generated from skin wound images similar to pressure ulcer images. Finally, we added an attention module that can provide hints on the pressure ulcer image features. The resulting model provides an accuracy of 99.0%, an intersection over union (IoU) of 99.99%, and a dice similarity coefficient (DSC) of 93.4% for pressure ul-cer segmentation, which is better than existing results.