Guiding Assistive-Technology Adaptations Through Intelligent Stream Mining of Patient Data

Introduction

One in 4 U.S. adults (61 million Americans) have a disability that impacts major life activities (Okoro et al., 2017). More than one million adults in the U.S. are diagnosed each year with cognitive impairments (CI) due to neurological disease or trauma. Currently, there are between 13.3 to 16.1 million Americans living with chronic brain disorders and associated CI (Alliance, 2001). In addition, approximately 4 million Americans have developmental disabilities that impact cognitive functioning (Services, 2002). Cognitive impairments prevent this large and growing segment of our society from fully integrating into society (McColl et al., 1998).

Assistive technology (AT) should help. However, studies have found that AT systems are abandoned by CI users at shockingly high rates (de Joode et al., 2010a; LoPresti et al., 2004; Wilson et al., 2001a; Wright et al., 2001). One major cause of abandonment is an eventual misalignment between: (1) user goals and abilities, and (2) the functionality delivered by the system. Research described in this chapter supports the monitoring of this relationship between user goals and their satisfaction by the system. In a case study, users are given an email system to aid in their activities of daily living (ADLs). At appropriate times, the system is adapted to meet the changing needs of the user. By monitoring a user’s event stream, post-clinic care teams and researchers can notice changes in user behavior that indicate that the system should be adapted. This work has mitigated this misalignment for dozens of clients engaged in cognitive rehabilitation. This technology is applicable to personalized user monitoring and generalizes to monitoring voluminous streams of event data.