Leg amputations are common in accidents and diseases. The present active bionic legs use Electromyography (EMG) signals in lower limbs (just before the location of the amputation) to generate active control signals. The active con-trol with EMGs greatly limits the potential of using these bionic legs because most accidents and diseases cause se-vere damages to tissues/muscles which originates EMG signals. As an alternative, the present research attempted to use an upper limb swing pattern to control an active bionic leg. A deep neural network (DNN) model is implemented to recognize the patterns in upper limb swing, and it is used to translate these signals into active control input of a bionic leg. The proposed approach can generate a full gait cycle within 1082 milliseconds, and it is comparable to the normal (a person without any disability) 1070 milliseconds gait cycle.