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Member Research and Reports

Member Research and Reports

NYU Affiliated Associate Professor is Published in the JMIR Medical Informatics

A new study by Dr. Winslow Burleson, affiliated associate professor with New York University College of Global Public Health was published in the JMIR Medical Informatics titled “An Assistive Technology System that Provides Personalized Dressing Support for People Living with Dementia: Capability Study.”

Individuals living with advancing stages of dementia (“persons with dementia” or PWDs) or other cognitive disorders do not have the luxury of remembering how to perform basic day-to-day activities, which in turn makes them increasingly dependent on the assistance of caregivers. Dressing is one of the most common and stressful activities provided by caregivers because of its complexity and privacy challenges posed during the process. In preparation for in-home trials with PWDs, the aim of this study was to develop and evaluate a prototype intelligent system, the DRESS prototype, to assess its ability to provide automated assistance with dressing that can afford independence and privacy to individual PWDs and potentially provide additional freedom to their caregivers (family members and professionals).

A set of expected detections for correct dressing was prepared via video analysis of all participants’ dressing behaviors. In the initial phases of donning either shirts or pants, the DRESS prototype missed only four out of 388 expected detections. The prototype’s ability to recognize other missing detections varied across conditions. There were also some unexpected detections such as detection of the inside of a shirt as it was being put on. Throughout the study, detection of dressing events was adversely affected by the relatively smaller effective size of the markers at greater distances. Although the DRESS prototype incorrectly identified 10 of 22 cases for shirts, the prototype preformed significantly better for pants, incorrectly identifying only five of 22 cases. Further analyses identified opportunities to improve the DRESS prototype’s reliability, including increasing the size of markers, minimizing garment folding or occlusions, and optimal positioning of participants with respect to the DRESS prototype.

This study demonstrates the ability to detect clothing orientation and position and infer current state of dressing using a combination of sensors, intelligent software, and barcode tracking. With improvements identified by this study, the DRESS prototype has the potential to provide a viable option to provide automated dressing support to assist PWDs in maintaining their independence and privacy, while potentially providing their caregivers with the much-needed respite.

Read and download Dr. Burleson’s paper