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**Magnet Based Interactions**  Gestures Several works have investigated input methods making use of magnetometers, whether they are looking for more absolute locators mimicking a mouse or more gestural input. uTrack (**cite**) implements an absolute locator using a magnet attached to the user's thumb and two magnetometers attached to their ring finger. The combined readings from the two sensors allows a fairly accurate location reading for the magnet. While this does show that it is possible, the implementation also highlights that a single sensor does have its limitations, especially when compromises are made such as in smartphones. Smartphone sensor reliability for augmented reality applications (**cite**) touches on the inherit inaccuracy of cheaper sensors used in smartphones. In addition, alot of attention is paid to how external forces can affect the readings. Improving Heading Accuracy in Smartphone-based PDR Systems using Multi-Pedestrian Sensor Fusion (**cite**) investigates this further and attempts to imrove upon the results by fusing readings from multiple devices. In their studies, they were able to reduce the error in the heading readings by some 27% using only naive averaging, leaving room for improvement using more complex algorithms. MagPairing (**cite**) attempts to make use of the unique magnetic forces that a device will pick up in a given location. By tapping the phones, you move them close enough together that the magnetic fields picked up by each device are extremely similar. They are able to reliable pair devices together by encoding these readings with authentication keys and comparing similarly timestamped readings to verify the connection.  Although, these do highlight how difficult and complex it can be to make these sensors accurate enough to be used as absolute locators, there is a lot that can be done by using them in a relative sense, especially as a form of gestural input. Not only can gestures be reliably recorded, but gestures  made in 3D Space is are also  unique to every user. If two users made box like gestures over the phone, both of their gestures would be somewhat different. This is due to users not being able to reproduce each others gestures in 3D space within a certain threshold \cite{Sahami_Shirazi_2012}. As we are developing this application, we need to take into account this threshold to be able to recognize certain swipe gestures across all users. MagiMusic (**cite**) and Magnetic Marionette (**cite**) show that these gesture based movements are a lot more feasible with the magnetometer than absolute inputs. MagiMusic allows digitul instruments to be played by making gestures with a magnet, such as strumming a guitar. Magnetic Marionette introduces a tangible avatar attached to the device, which can be moved around to produce different facial expressions on the screen.  While the sensors in these smartphones are generally inaccurate due to compromises made in the devices manufacturing and the effect of external magnetic forces, they can be made to be more accurate in a fixed setting. If you know the ambient forces you can account for them in order to pick out the desired readings. In Pulse (**cite**) for example, a system is desinged through which the device can recieve and decode a signal sent out as magnetic forces, and can even reach transfer speeds up to 44 bps. Due to the nature of the sensors used, it is an especially short-range communication method. A Sensor Fusion Method for Smart phone Orientation Estimation (**cite**) also provides methods to work around the inherit issues with these sensors by fusing data from multiple sensors for correction. Although the final implementation here is beyond this project, much of specifics are a good base for filtering some of the sensor data. For example the low-pass filter used to smooth the readings will prove important in Piknik, and adjustments to reference frames are something that very well may come into play.  **Drawing with Everyday Objects**