Utilizing finger movement data to cluster patients with everyday action impairment

Abstract

Difficulty in performing the activities of daily living is a key clinical feature of early cognitive decline in older adults and has also been associated with the early stage of dementia in mild cognitive impairment (MCI). As the number of individuals with dementia and the development of technology rise, an immersive virtual environment or virtual reality has been used in therapy for memory problems in the area of MCI. This study evaluated the use of finger movement data obtained from the virtual-reality-based application and its ability to cluster patients with everyday action impairment. Here, as a pilot study, nine healthy adults completed lunch box packing as an everyday action task in the designated virtual reality called the Virtual Kitchen (VK), equipped with a leap motion controller to record their finger movement. We converted the finger movements to acceleration data and then employed a time series clustering algorithm to create several clusters based on the data set. In addition, to comprehensively review the clustering result, we assessed performance-based measures for the experiment using the Naturalistic Action Test (NAT). The final results indicate that the clusters formed by using the acceleration data seem reasonably analogous to the performance measures (i.e., the type and number of errors that occurred).