In this paper, we propose and investigate a novel fall detection scheme based on a wearable device. The device comprises of an FX1901 load cell placed in the sole of a footwear for monitoring the weight exerted by the body during locomotion and an ADXL335 tri-axial accelerometer for sensing tilt and associated angular displacement. Real world dataset from the sensors' output corresponding to the unique patterns that characterize the four basic activities of daily living were obtained and fed into multi-class learning algorithm for building an offline decision model. When a fall occurs, the weight exerted by the body on the sensor drops rapidly and for an unusually prolonged period of time. This is accompanied by a drastic variation in angular displacement relative to a reference position. The joint variations are processed via an Arduino LilyPad ATmega168V microcontroller incorporating the decision model of the learning algorithm and a request for help along with the subject's location information is generated and sent to caregivers by using a GPS/GSM module. Experimental results indicate that proposed scheme has the potential to detect fall with 100% accuracy.