Falls in the elderly population are a major problem for today's society. The immediate automatic detection of such events would help reduce the associated consequences of falls. This paper describes the development of an accurate, accelerometer-based fall detection system to distinguish between Activities of Daily Living (ADL) and falls. It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using an optical motion capture system. In this study however accurate vertical velocity profiles of the trunk were generated by simple signal processing of the signals from a tri-axial accelerometer (TA). By recording simulated falls onto crash mats and ADL performed by 5 young healthy subjects, using both a single chest mounted TA and using an optical motion capture system, the accuracy of the vertical velocity profiles was assessed. Data analysis was performed using MATLAB to determine the peak velocities recorded and RMS error during four different fall and six ADL types. Results show high correlations and low percentage errors between the vertical velocity profiles generated by the TA to those recorded using the optical motion capture system. In addition, through thresholding of the vertical velocity profiles generated using the TA at -1.3m/s, falls can be distinguished from normal ADL with 100% sensitivity and specificity.