Abstract: This disclosure relates to new multi-modal sensing array architectures that can sense normal and shear forces. Examples include resistive sensing arrays that are used to measure changes in the environment and manufacturing processes to produce multi-modal sensing arrays in a highly-automated and inexpensive fashion. Specifically, a manufacturing process includes a cutting operation and sewing/embroidery technique that creates a fully automated process to produce a resistive sensing array. The new manufacturing process enables the reduction of processing time and materials to produce a functioning sensor without limiting the designs space of achievable sensor geometries. Also presented are sensor reading methodologies for high-speed reading that are capable of sensing vibrations and a wide range of forces. Examples include the use of the new sensor arrays in wearable devices.

Abstract: Systems and methods are provided for estimating 3D poses of a subject based on tactile interactions with the ground. Test subject interactions with the ground are recorded using a sensor system along with reference information (e.g., synchronized video information) for use in correlating tactile information with specific 3D poses, e.g., by training a neural network based on the reference information. Then, tactile information received in response to a given subject interacting with the ground can be used to estimate the 3D pose of the given subject directly, i.e., without reference to corresponding reference information. Certain exemplary embodiments use a sensor system in the form of a pressure sensing carpet or mat, although other types of sensor systems using pressure or other sensors can be used in various alternative embodiments.

Abstract: Systems and methods are provided for estimating 3D poses of a subject based on tactile interactions with the ground. Test subject interactions with the ground are recorded using a sensor system along with reference information (e.g., synchronized video information) for use in correlating tactile information with specific 3D poses, e.g., by training a neural network based on the reference information. Then, tactile information received in response to a given subject interacting with the ground can be used to estimate the 3D pose of the given subject directly, i.e., without reference to corresponding reference information. Certain exemplary embodiments use a sensor system in the form of a pressure sensing carpet or mat, although other types of sensor systems using pressure or other sensors can be used in various alternative embodiments.