For accurate indoor positioning, a moving activity recognition (AR) method has been developed that is based on a smartphone's sensor data. Prior methods can only recognize moving activities if the smartphone is held in a predefined place. We propose a method that works in various holding places to increase the usability. An SVM classifier is chosen because of its strength in utilizing features. New features are added such as percentiles of acceleration, air pressure, and acceleration magnitude. We have achieved 94.3% overall accuracy in various holding places: in users' hands, belt pouches, pant back pockets, and pant side pockets.

With the rapid increase in the number of COVID-19 patients in Japan, the number of patients receiving oxygen at home has also increased rapidly, and some of these patients have died. An efficient approach to identify high-risk patients with slowly progressing and rapidly worsening COVID-19, and to avoid missing the timing of therapeutic intervention will improve patient prognosis and prevent medical complications. Patients admitted to medical institutions in Japan from November 14, 2020 to April 11, 2021 and registered in the COVID-19 Registry Japan were included. Risk factors for patients with High Flow Nasal Cannula invasive respiratory management or higher were comprehensively explored using machine learning. Age-specific cohorts were created, and severity prediction was performed for the patient surge period. We were able to obtain a model that was able to predict severe disease with a sensitivity of 57% when the specificity was set at 90% for those aged 40–59 years, and with a specificity of 50% and 43% when the sensitivity was set at 90% for those aged 60–79 years and 80 years and older, respectively. We were able to identify lactate dehydrogenase level (LDH) as an important factor in predicting the severity of illness in all age groups. Using machine learning, we were able to identify risk factors with high accuracy, and predict the severity of the disease. We plan to develop a tool that will be useful in determining the indications for hospitalisation for patients undergoing home care and early hospitalisation.