There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
Principal Investigator | |
Principal Investigator's Name: | Matthew Harper |
Institution: | Liverpool John Moores University |
Department: | Computer Science |
Country: | |
Proposed Analysis: | Technology can provide a means of remotely and constantly monitoring people with dementia in home settings to detect dementia-related difficulties in IADL completion, however many current technological approaches to do this suffer from an inability to comprehensively identify these difficulties and distinguish them from difficulties or abnormalities in activities daily life not caused by dementia. The aim of this project is to develop a technology-based method for identifying these difficulties and distinguishing them from non-dementia related difficulties or abnormalities in activities. The data in the ADNI datasets that are relevant to this project are the cognitive assessments, such as functional assessment questionnaire responses and ECog assessments. These will be analyzed to gain an understanding of exactly what difficulties are experienced by people with dementia in their daily lives. This is helpful as COVID-19 has made data collection experiments nigh on impossible, so another way of finding the activities performed in the daily life of a person with dementia, and the difficulties the person faces in completing those activities must be found. The understanding of the activities performed in the daily life of a person with dementia, and the difficulties the person faces in completing those activities, will allow us to mock up the scenarios using publicly available (non-sensitive) data. |
Additional Investigators | |
Investigator's Name: | Fawaz Ghali |
Proposed Analysis: | Technology can provide a means of remotely and constantly monitoring people with dementia in home settings to detect dementia-related difficulties in IADL completion, however many current technological approaches to do this suffer from an inability to comprehensively identify these difficulties and distinguish them from difficulties or abnormalities in activities daily life not caused by dementia. The aim of this project is to develop a technology-based method for identifying these difficulties and distinguishing them from non-dementia related difficulties or abnormalities in activities. The data in the ADNI datasets that are relevant to this project are the cognitive assessments, such as functional assessment questionnaire responses and ECog assessments. These will be analysed to gain an understanding of exactly what difficulties are experienced by people with dementia in their daily lives. This is helpful as COVID-19 has made data collection experiments nigh on impossible, so another way of finding the activities performed in the daily life of a person with dementia, and the difficulties the person faces in completing those activities must be found. The understanding of the activities performed in the daily life of a person with dementia, and the difficulties the person faces in completing those activities, will allow us to mock up the scenarios using publicly available (non-sensitive) data. |