A New Prototype of Smart Wearable Monitoring System Solution for Alzheimer's Patients

A systematic review of reminder and guidance systems for Alzheimer's Disease and Related Dementias patients: context, barriers and facilitators

PURPOSE

To present the latest available research assessing the actual impact of reminder and guidance technologies for daily activities in Alzheimer's Disease and Related Dementia's patients, outlining design implications for these technologies.

MATERIALS AND METHODS

The search was conducted in the ACM Digital Library, IEEExplore, ScienceDirect, PubMed, Cochrane Library, Sage Journal, ResearchGate, and SemanticScholar. An iteratively-developed Boolean search string was built including up to 18 AND/OR terms across Four categories (Memory Aids, Technology, Daily Activities, Memory Impairment). We qualitatively analyzed the findings and discussions of the findings reported in 40 studies in our corpus to determine common barriers to, and facilitators of, effective intervention implementation and adoption.

RESULTS

Forty studies fulfilled the inclusion and exclusion criteria. In existing studies, individuals with Alzheimer's Disease and Related Dementias understand the usefulness of different functions that can be provided within an app to assist with everyday tasks. There was a high level of heterogeneity regarding the studies' location, duration, and evaluation methodology.

CONCLUSIONS

There is a need for assistive reminder and guidance technologies to be tailored towards autonomy, identity and personalization. Future work should include motivating features to aid during mood changes and feelings of insecurity.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.