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.