Stroke is the leading cause of serious, long-term disability in the United States and those that do not exercise or engage in regular activity are at a 30% increased risk of experiencing a recurrent stroke. One-on- one rehabilitation sessions are frequently limited in number due to insurance regulations and therapists (physical and occupational) frequently prescribe home-based exercise programs. These programs historically have low adherence rates and patient report can often be biased, incomplete, or inaccurate. Wearable sensors can track amount of activity but these sensors are limited in scope and cannot discern between various activities. Depth sensors can be used in the home to detect falls and monitor in-home gait patterns of well older adults. Other researchers have used depth sensors to detect and discern activities in laboratories or mock home environments with a population without any disabilities. In this proposal, the Daily Activity Recognition and Assessment System (DARAS) will merge prior ambient depth sensor work with newly developed algorithms to objectively and accurately measure the amount and type of activity of people with stroke living at home. This will be completed in three specific aims. The DARAS algorithms will be developed and refined for recognizing activities of people with stroke in the kitchen environment using the Foresite depth sensor. These algorithms will be trained using real-world data from lab-based testing with individuals with stroke (n =10). We will refine the Convolutional Neural Networks (CNN) based algorithm for accurately segmenting and recognizing activities from untrimmed processing of depth videos. The developed activity recognition system will be deployed in the homes of 10 individuals with stroke over the course of 1 year. To determine the impact on daily life and acceptability of the system and generated data, focus groups will be held with 10 individuals with stroke. The DARAS developed in this proposal will provide a novel outcome assessment for a variety of post-stroke interventions and provide occupational therapists the ability to detect declines in performance early on and intervene.
Stroke is the leading cause of serious, long-term disability in the United States and those that do not engage in regular activity are at a 30% increased risk of experiencing a second stroke. Self-monitoring and self-report of home-based activity and exercise is highly subjective and provides little value to rehabilitation clinicians and their clients with stroke. The system to be developed in this proposal will objectively and accurately measure the amount and type of activity of people with stroke living at home.
