Abstract-The current diagnosis process of dementia is resulting in a high percentage of cases with delayed detection. To address this problem, in this paper, we explore the feasibility of autonomously detecting mild cognitive impairment (MCI) in the older adult population. We implement a signal processing approach equipped with a machine learning paradigm to process and analyze real-world data acquired using home-based unobtrusive sensing technologies. Using the sensor and clinical data pertaining to 97 subjects, acquired over an average period of three years, a number of measures associated with the subjects' walking speed and general activity in the home were calculated. Different time spans of these measures were used to generate feature vectors to train and test two machine learning algorithms namely support vector machines and random forests. We were able to autonomously detect MCI in older adults with an area under the ROC curve of 0.97 and an area under the precision-recall curve of 0.93 using a time window of 24 weeks. This study is of great significance since it can potentially assist in the early detection of cognitive impairment in older adults.
Building on the work by ORCATECH, Akl et al. used a subset of the data to explore the feasibility of autonomously discriminating older adults with MCI from their cognitively intact counterparts using a number of predefined measures associated with the subjects' walking speed and general activity in their homes REF .
Autonomous Unobtrusive Detection of Mild Cognitive Impairment in Older Adults
IEEE Transactions on Biomedical Engineering
IEEE Transactions on Biomedical Engineering
Computer Science Medicine