Abstract: Implantable biosensors and methods of making and using such biosensors are disclosed. The biosensors can be micro-devices, for example, micro-sized bead implants having an associated gyroscope, accelerometer and/or magnetometer to detect and transmit changes in the position of the biosensor following implantation. The biosensors can be implanted into a subject’s bone and/or a subject’s prosthesis to detect, for example, changes in position or orientation of a prosthetic implant that can indicate loosening or potential onset of structural failures. Devices for implantation of biosensors, e.g., kinematic sensors, into bone are also disclosed as well as methods and systems for measuring or monitoring physiological kinematics.

Abstract: Methods and systems for object tracking are disclosed in which the bandwidth of a “slow” tracking system (e.g., an optical tracking system) is augmented with sensor data generated by a “fast” tracking system (e.g., an inertial tracking system). The tracking data generated by the respective systems can be used to estimate and/or predict a position, velocity, and orientation of a tracked object that can be updated at the sample rate of the “fast” tracking system. The methods and systems disclosed herein generally involve an estimation algorithm that operates on raw sensor data (e.g., two-dimensional pixel coordinates in a captured image) as opposed to first processing and/or calculating object position and orientation using a triangulation or “back projection” algorithm.

Abstract: Methods and systems for object tracking are disclosed in which the bandwidth of a “slow” tracking system (e.g., an optical tracking system) is augmented with sensor data generated by a “fast” tracking system (e.g., an inertial tracking system). The tracking data generated by the respective systems can be used to estimate and/or predict a position, velocity, and orientation of a tracked object that can be updated at the sample rate of the “fast” tracking system. The methods and systems disclosed herein generally involve an estimation algorithm that operates on raw sensor data (e.g., two-dimensional pixel coordinates in a captured image) as opposed to first processing and/or calculating object position and orientation using a triangulation or “back projection” algorithm.