With sensors becoming smaller and more power efficient, wearable sensors that anyone could wear are becoming a feasible concept. We demonstrate a small lightweight module, called Porcupine, which aims at continuously monitoring human activities as long as possible, and as fine-grained as possible. We present initial analysis of a set of abstraction algorithms that combine and process raw accelerometer data and tilt switch states, to get descriptors of the user's motion-based activities. The algorithms are running locally, and the information they produce is stored in on-board memory for later analysis.