Falls are behind many elderly hospitalizations and can lead to injuries that greatly debilitate old patients. Much of the deployed fall detection systems rely on the user wearing a personal emergency response device, being conscious and at home. The limitations of the existing systems regarding usability and efficiency have yield an overarching research question on whether systems based on new and advanced consumer mobile devices can be used as ubiquitous automatic fall detectors for seniors. This paper specifically looks into the accuracy of a fall detection system based on an off-the-shelf smartwatch and smartphone. We have implemented a system which combines threshold based and pattern recognition techniques in both devices, with the intent of having the watch to contribute to the specificity of the fall detection strategy. We tested the accuracy of the system through a series of simulated falls and activities of daily living, resulting on the correct identification of 63% of the falls and 78% of the activities and outperforming two other baseline fall detection applications (iFall and Fade). The sensors and algorithm on the watch were able to provide a marginal contribution to the system's accuracy. Indications from the tests suggest that it should be possible to improve the system accuracy by adjusting the used thresholds and fuzzyfying them. Moreover, it is expected that the open source nature of this work and it's results boost such threshold tuning and serve as a better basis for researchers to benchmark their work.