Activity Recognition and Activity Level Estimation for Context-Based Prompting System of Mild Cognitive Impairment Patients

Abstract

The number of elderly people, who are unable to live independently and need assistance due to cognitive impairment, will rise rapidly in the aging society. To assist the independent living of these individuals and decrease the caregiver burden have become an important public health concern in the future. Mild Cognitive Impairment (MCI) is an intermediate state between normal cognitive function and dementia. The symptoms of MCI include difficulty remembering recent events or recently acquired information, depression and anxiety. MCI also increases the fall risk and affects patients’ social function and behavior. Sufficient physical activities can improve health of brain and reduce the risk of MCI. Since the context-aware computing technologies for assisting living have gained great popularity. We proposed a context-based activity prompting system to improve quality of life for MCI patients. The proposed system utilizes a smart phone as a sensor device to transmit sensing data to cloud server for activity recognition and activity level estimation, and uses context-based technique to provide activity prompting message to MCI patients. The activity prompting service supplies the activities self-management for MCI patients and helps them living independently. The system also provides real time fall detection mechanism to shorten the rescue time when accident happened. The experimental results have demonstrated that the proposed system achieves high accuracy on activity recognition and activity level estimation.