Coarse-to-Fine Activity Annotation and Recognition Algorithm for Solitary Older Adults

Abstract

Older adults want to remain independent with dignity for as long as possible, especially the solitary older adults. Activity recognition plays an essential role in elderly care and rehabilitation by detecting activity shifts among the elderly population. Despite over a decade of research and development in activity recognition, accurate and reliable systems for older adults in use are few. We propose an automatic data collecting and labeling system by addressing the annotation issue, and a novel coarse-to-fine activities of daily living(ADLs) recognition algorithm for older adults, by combining supervised and unsupervised machine learning methods. The automatic data collecting and labeling system targets at the annotation issue caused by the diversity of ADLs in free-living situations. Multiple sensors fusion strategy is employed to interpret and annotate the ADLs. Leveraging supervised and unsupervised machine learning methods, we can discover and recognize ambulatory and trivial ADLS for older adults. The performance of the automatic data collecting and labeling system is double-checked in a four days long test. With the reliable ground truth, we evaluate the coarse-to-fine ADLs recognition algorithm. The performance of our algorithm is promising, the recognition accuracy is larger than 91%.