| Summary: | Fall is one of the main factors causing the extension of the patient’s hospital stay. In some cases, fall leads the elderly patients to bone fracture, bedridden, and care state. Also in other cases, fall leads to fear, withdraw, and bedridden. However, the medical staff can’t keep eye on all the patients at all the time. Moreover, patient's condition and their environment change from moment to moment. Therefore, the primary screening is need to find the high risk patients and to focus attention on them. Furthermore, real-time risk assessment is need to prevent the patients from falling. In this paper, we propose and develop an automatic real-time fall risk assessment using deep learning as a primary screening of the patients in the hospital. By using our method, we construct a part of the nursing system to reduce the fall incidents and accidents of the patients, and the caregiving load of the medical staff. At the reception, proposed method recognizes patient’s appearance and estimates risks by classifying the fall risk levels according to a hospital’s clinical safety standard, and it stores those safety related information in the electric medical records of patients. While the patients stay in the hospital, medical staffs see the potential risks in the condition of the patients and their environment online through the wearable devices such as smart glasses, mobile terminals, and assistive mobile robots. We prepared a datasets including 16,000 images which collected from public datasets such as MIT places, educational video, actual incident location, and annotated them by ourselves for the clinical safety. The result show a fundamental experiment comparing with manual method by medical staffs and our proposed method surpasses real-time performance and coverage. These automatic fall risk assessment method using deep learning is novel to in clinical safety field.
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