Abnormal Behavioral Patterns Detection from Activity Records of Institutionalized Older Adults

The automatic detection of behavioral changes in older adults living in geriatric centers is relevant for physicians and caregivers. These changes could indicate an incipient symptom of a disease or a steep decline in the health of the person. Abnormal pattern discovery has been studied in the context of an array of wearable sensors i.e. accelerometers, infrared, cameras, etc. dedicated to monitor the older adult. In this work we explore the use of manually annotated records, the type of records maintained by caregivers in a daily log. These annotations have low semantic value, and consist in a sequence of keywords about the activity being carried out by the older adult. This information is often overseen because it could be noisy, incomplete and redundant. We tested a data-driven approach to identify patterns from daily activity records, which were collected over six months from a group of older adults in a geriatric center. The results show that through simple data processing techniques it is posible to identify abnormal patterns in daily activities associated with behavioral changes over time.