This talk presents recent results on nonlinear observers (estimation algorithms) and their applications in motion estimation problems ranging from wearable sensors to bicycles. First, a new observer design technique that integrates the classical high-gain observer with a novel LPV/LMI observer to provide significant advantages compared to both methods is presented. Second, the challenges in designing observers for nonlinear systems which are non-monotonic are discussed. Non-monotonic systems are commonly encountered, but popular observer design methods fail to yield feasible solutions for such systems. Hybrid observers with switched gains enable existing observer design methods to be utilized for these systems. Following the analytical observer results, some of their applications in motion estimation are presented, including a wearable device for activity classification in Parkinson’s disease patients, a smart bicycle that utilizes cameras and sensor fusion to protect itself from other vehicles on the road, autonomous cars equipped for teleoperator remote intervention, and smart agricultural/construction vehicles that utilize inexpensive sensors for end-effector position estimation. Each application is accompanied by a video of a prototype experimental demonstration. One of these applications has been successfully commercialized through a start-up company which sold over 11,000 sensor boards in 2022.