Recognition of Activities of Daily Living in natural "at home" scenario for assessment of Alzheimer's disease patients

In this paper we tackle the problem of Instrumental Activities of Daily Living (IADLs) recognition from wearable videos in a Home Clinical scenario. The aim of this research is to provide an accessible and yet detailed video-based navigation interface of patients with dementia/Alzheimer disease to doctors and caregivers. A joint work between a memory clinic and computer vision scientists enabled studying real-case life scenarios of a dyad couple consisting of a caregiver and patient with Alzheimer. As a result of this collaboration, a new @Home, real-life video dataset was recorded, from which a truly relevant taxonomy of activities was extracted. Following a state of the art Activity Recognition framework we further studied and assessed these IADLs in term of recognition performances with different calibration approaches. Egocentric vision, Action Recognition, Context, Object Recognition, Place Recognition, Home Clinical Scenario