Bed-Exit Prediction Applying Neural Network Combining Bed Position Detection and Patient Posture Estimation.

The work of a nurse involves tasks that can lead to serious accidents with a single mistake or miss, and thus nurses are exposed to high stress. In particular, injections, pre-medications, tube connections, and falling are factors that lead to serious accidents and are considered a major part of the load of nursing work. To reduce the burden of nursing work, we are working on developing a sensing system to prevent fall accidents. Because fall accidents tend to occur when elderly people, whose lower limb muscle strength has declined, go to the toilet, we use a camera image to detect the end position, which is the initial posture of the patient's landing movement. In this study, we detected the sitting position of the patient by combining the detection result of the skeletal position of the patient and the detection result of the bed position. A simulation environment was constructed and the estimation accuracy of the end sitting position of the patient was evaluated using the image captured at the scene where the patient and the nurse are active.

Vision-based Bed Detection for Hospital Patient Monitoring System.

In recent years, as a way to prevent patient fall-down, studies have been conducted using patient room cameras to detect the patient behavior of leaving the bed. It is very important to specify the patient bed location in the process of detecting patient behavior using camera images. In this study, we propose a method to specify the patient bed location using a monocular camera. In this proposal, we convert a camera image viewpoint into a bird's-eye view image as a preprocessing step. By using planer perspective transformation, it is possible to display the bed as a rectangular shape with a fixed ratio, even if the bed location or camera position is changed. Therefore, it is possible to detect the bed location with a high degree of accuracy by means of machine learning. The simulation experiment results confirm that the average error and standard deviation of the bed coordinates are 7.9 and 5.0 pixels, respectively; in the practical scene, we confirm that the average error and standard deviation of the bed coordinates are 12.1 and 8.2 pixels, respectively.

Quantitative analysis of facial palsy using a three-dimensional facial motion measurement system.

OBJECTIVE: The prognosis for facial nerve palsy (FNP) depends on its severity. Currently, many clinicians use the Yanagihara, House-Brackmann, and/or Sunnybrook grading systems to assess FNP. Although these assessments are performed by experts, inter- and intra-observer disagreements have been demonstrated. The quantitative and objective analyses of the degree of FNP would be preferred to monitor functional changes and to plan and evaluate therapeutic interventions in patients with FNP. Numerous two-dimensional (2-D) assessments have been proposed, however, the limitations of 2-D assessment have been reported. The purpose of this study was to introduce a three-dimensional (3-D) image generation system for the analysis of facial nerve palsy (FNP) and to show the correlation between the severity of FNP assessed by this method and two conventional systems. METHODS: Five independent facial motions, resting, eyebrow raise, gentle eye closure, full smile with lips open and whistling were recorded with our system and the images were then analyzed using our software. The regional and gross facial symmetries were analyzed. The predicted scores were calculated and compared to the Yanagihara and H-B grading scores. We analyzed 15 normal volunteers and 42 patients with FNP. RESULTS: The results showed that 3-D analysis could measure mouth movement in the anteroposterior direction, whereas two-dimensional analysis could not. The system results showed good correlation with the clinical results from the Yanagihara (r(2)=0.86) and House-Brackmann (r(2)=0.81) grading scales. CONCLUSION: This objective method can produce consistent results that align with two conventional systems. Therefore, this method is ideally suited for use in a routine clinical setting.