Through the measurement and thresholding of different kinematic and angular signals, from the waist using accelerometry, distinguishing between simulated falls and normal scripted and continuous, unscripted activates was performed and evaluated using these signals. Different combinations of individual signal thresholding algorithms were used to compile a suite of Fall-detection algorithms suitable for an autonomous waist worn system. The suite of algorithms were tested against a comprehensive data-set recorded from 10 young healthy subjects performing 240 falls and 120 activities of daily living and 10 elderly healthy subjects performing 240 scripted and 52.4 hours of continuous unscripted normal activities. Results show that using a simple algorithm employing IMPACT+POSTURE+VELOCITY can achieve a low false-positive rate of less than 1 FP/day (0.94FPs/day) with a sensitivity of 94.6% and a specificity of 100%. The algorithms were tested using continuous unsupervised activities performed by elderly healthy subjects, which is the target environment for a fall detection device.