Advances in the area of ubiquitous, pervasive and wearable computing has resulted in the development of low band-width, data rich environmental and body sensor networks, providing a reliable and non-intrusive methodology for capturing activity data from humans and the environments they inhabit.  Assistive technologies that promote independent living amongst elderly and individuals with cognitive impairment are a major motivating factor for sensor based activity recognition systems. However, the process of discerning relevant activity information from these sensor streams such as accelerometers is a non-trivial task and is an on-going research area. The difficulty stems from factors such as spatio-temporal variations in movement patterns induced by different individuals and contexts, sparse occurrence of relevant activity gestures in a continuous stream of irrelevant movements and the lack of real-world data for training learning algorithms. This talk will discuss solutions proposed as part of the presenter’s doctoral dissertation to addresses these challenges in the context of wearable accelerometer based activity and gesture recognition.