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While inexpensive wearable motion-sensing devices have shown great promise for fall detection and posture monitoring, two major problems still exist and have to be solved: 1) a framework for the development of firmware and 2) software to make intelligent decisions. We address both the problems. We propose a generic framework for developing the firmware. We also demonstrate that the k-means clustering algorithm can semi-automatically extract training examples from motion data. Moreover, we trained and evaluated several one-and two-level classification networks to monitor non-fall activities and to detect fall events. The proposed classification networks are the combinations of neural networks and softmax regression. These networks are trained offline with examples extracted by our proposed method. The cross-validation of trained two-level networks shows 100% accuracy for non-fall activities and fall events. The data sets for training and testing have been collected using the devices we assembled with four off-the-shelf components. We have programmed them using a prototype of our proposed framework. The data sets include seven types of nonfall activities and four types of fall events. This paper advances the state of the art for the development and training of wearable devices for monitoring non-fall activities and detecting fall events.Index Terms-Fall detection, learning algorithms, neural networks, MEMS, 9-axis sensor, semi-automatic extraction of training examples, softmax regression.

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