This paper describes the development of an accurate, accelerometer and gyroscope based fall-event detection system to distinguish between activities of daily living (ADL) and fall-events. Using simulated fall-events onto crash mats (under supervised conditions) and ADL performed by elderly subjects, distinguishing between falls and ADL is achieved using accelerometer and gyroscope-based sensors, mounted on the trunk and thigh of the person. Data analysis was performed using MATLAB^® to determine the peak accelerations and angular velocities recorded during eight different types of falls. A fall detection algorithm was proposed using thresholding techniques. Results from an evaluation of the detection algorithm show that a fall-event can be distinguished from an ADL with 100% accuracy using a single threshold applied to the resultant acceleration signal from a tri-axial accelerometer located at the chest. Thresholding was thus demonstrated to be capable of discriminating between an ADL and a fall-event, when those falls were simulated falls.