Preliminary Investigation of Activity Prediction in Nursing Homes using Activity History with Erroneous Time Stamps

Systems for recording nursing care activities using smartphones are becoming widespread in the nursing care field. As a result, it is becoming possible to predict future activities of care recipients based on the caregiving record history recorded in the system. Prediction of future activities will enable the development of various caregiving applications, such as preparation support for future caregiving activities, detection of missing entries in caregiving records, and sensor-based real-time activity recognition using the predictions as prior information. However, the caregivers are inherently busy and cannot record entries in real-time due to other tasks, resulting in many errors in time stamps of the entries, i.e., shift in occurrence time of an activity. When data with the many timing errors are used as training data in activity prediction methods, the performance of the prediction methods will deteriorate. In this paper, we propose an activity prediction method that is robust against the time errors. The proposed model has a module for correcting time errors and estimating whether or not a certain activity will occur from the current time to one hour later using the activity records of the past T hours. We evaluated the effectiveness of the proposed method using three datasets.