This paper evaluates threshold-based fall detection algorithms which use data from acceleration sensors that are part of the current smart phone technology. The evaluation was done with sampled fall records where young people simulate falls. To test the false positive rate of the algorithms, another record set with Activities of the Daily Living (ADLs) from elderlies was used. The results are very promising and show that smart phone sensors are suitable for fall detection. This will offer a new opportunity to assist elderlies in their daily living and extend their period of self-determined living.