Wrist worn smart devices such as smart watches become increasingly popular. As those devices collect sensitive personal information, appropriate user authentication is necessary to prevent illegitimate accesses to those devices. However, the small form and function-based usage of those wearable devices pose a big challenge to authentication. In this paper, we study the efficacy of motion based authentication for smart wearable devices. We propose MotionAuth, a behavioral biometric authentication method, which uses a wrist worn device to collect a user's behavioral biometrics and verify the identity of the person wearing the device. MotionAuth builds a user's profile based on motion data collected from motion sensors during the training phase and applies the profile in validating the alleged user during the verification phase. We implement MotionAuth using Android platform and test its effectiveness with real world data collected in a user study involving 30 users. We tested four different gestures including simple, natural gestures. Our experimental results show that MotionAuth can achieve high accuracy (as low as 2.6% EER value) and that even simple, natural gestures such as raising/lowering an arm can be used to verify a person with pretty good accuracy.