Gesture spotting is the task of detecting and recognizing gestures defined in a vocabulary. The difficulty of gesture spotting stems from the fact that valid gestures appear sporadically in a continuous gesture stream, interspersed with invalid gestures (movements that do not correspond to any gesture contained in the vocabulary). In this paper, a novel method for designing threshold models from valid gesture models learnt through Adaptive Boosting is proposed. This threshold model is adaptive in nature and discriminates between valid and invalid gestures. Furthermore, a gesture spotting network consisting of the individual gesture models and the threshold model is proposed to perform the task of spotting and recognition simultaneously. This technique is evaluated in the context of spotting and recognizing activity gestures (hand gestures) from continuous accelerometer data streams. The proposed technique results in a precision of 0.78 and a recall of 0.93 out performing the HMM based threshold model which resulted in 0.4 and 0.81 precision and recall values.