Assisted living applications can support aging in place efficiently when their context-awareness is based on a real-life-proof approach to activity detection. Recently, Caroux et al. proposed a new approach to monitoring activities dedicated to older adults, named "activity verification". This approach uses a knowledge-driven framework that draws from the literature on older adults. The purpose of the present study is to address the limitations of this approach by scaling it up and by demonstrating that it is applicable to context-aware assistive applications for aging in place. First, an experimental study was conducted in which this approach was used to monitor a large range of daily activities, for a long period (8 weeks of experimentation) and involving several participants (7 participants). Second, this approach was used to validate two examples of context-aware assisted living applications, via simulation, based on real-life sensor log data. Results showed that the applicability of the "activity veri-fication" approach scales up to a large range of daily activities by extending this approach (with accuracy values ranging between 0.82 and 1.00 depending on the activity of interest). Its inter-participant and intra-participant consistencies were demonstrated. Its limitations were addressed and the applicability to context-aware assistive applications for aging in place running on a dedicated platform was demonstrated.