Activity of Daily Living has become a clinical de facto instrument to assess daily functional status of older people living independently at home. Almost all ADLs scales are based on subjective assessment of clinical staff and self-reported responses of the elderly person. A great deal of variability in ADL assessment is likely due to the different cultural beliefs, language and education, and over-assessment of personal capability to potentially avoid negative consequences. This paper proposes automatic and objective ADLs assessment as key component of a technology platform that supports older people to live independently in their home, called Smarter Safer Homes. The objective ADL assessment is achieved through communicating data from simple non-intrusive, wireless sensors placed in a home environment. Pilot sensor data sets were collected over six months from nine independent living homes of participants aged 70+ year. The application of a clustering based, unsupervised learning method on these data sets demonstrates the potential to automatically detect five domains of activity contributing to functional independence. Furthermore, the method provides features that support elderlys self-monitoring of daily activities more regularly, that could provide the potential for timely and early intervention from family and carers.