Abstract: In one aspect, a computerized process useful for movement classification using a motion capture suit includes the step of providing the motion capture suit worn by a user. The motion capture suit comprises a set of position sensors and a Wi-Fi system configured to communicate a set of position sensor data to a computing system. The process includes the step of providing the computing system to: receive a set of position data from the motion capture suit for a specified time window of data comprising X, Y and Z axis positions and a joints-angle data for each position sensor of the set of position sensors, transforming each joints-angle data to a corresponding frequency domain using a fast Fourier transformation to remove any time dependency value, after the fast Fourier data transformation, train a support vector machine using the X, Y and Z axis positions data and the frequency domain data as input, using the support vector machine to predict a set of body positions and movements.

Abstract: In one aspect, a computerized process useful for movement classification using a motion capture suit includes the step of providing the motion capture suit worn by a user. The motion capture suit comprises a set of position sensors and a Wi-Fi system configured to communicate a set of position sensor data to a computing system. The process includes the step of providing the computing system to: receive a set of position data from the motion capture suit for a specified time window of data comprising X, Y and Z axis positions and a joints-angle data for each position sensor of the set of position sensors, transforming each joints-angle data to a corresponding frequency domain using a fast Fourier transformation to remove any time dependency value, after the fast Fourier data transformation, train a support vector machine using the X, Y and Z axis positions data and the frequency domain data as input, using the support vector machine to predict a set of body positions and movements.

Abstract: In one aspect, a computerized process useful for managing a hybrid motion sensing framework includes the step of providing a motion capture framework worn by a user to measure a user posture and motion by measuring an external source signal and an inertial property of the motion capture framework. The motion capture framework comprises a set of motion sensing units (MSUs) and an electromagnetic field generator (EFG). The MSU is a hybrid sensing system using a combination of sensors to measure position and orientation. The EFG generates an alternating electromagnetic field with a specified frequency. The method includes the step of calculating the user posture and motion based on the measuring an external source signal and an inertial property of the motion capture framework using a sensor fusion algorithm. The method includes the step of visualizing the position and orientation of the user.

Abstract: In one aspect, a computerized method useful for hand movement classification using a motion capture glove includes the step of providing a motion capture glove comprises one or multiple sensors connected to a back of the motion capture glove and one or multiple sensors connected to each finger of the motion capture glove. The method includes the step of, with the one or multiple sensors, measuring a set of physical quantities that describe a motion and a pose of a hand wearing the motion capture glove.

Abstract: In one aspect, a computerized process useful for movement classification using a motion capture suit includes the step of providing the motion capture suit worn by a user. The motion capture suit comprises a set of position sensors and a Wi-Fi system configured to communicate a set of position sensor data to a computing system. The process includes the step of providing the computing system to: receive a set of position data from the motion capture suit for a specified time window of data comprising X, Y and Z axis positions and a joints-angle data for each position sensor of the set of position sensors, transforming each joints-angle data to a corresponding frequency domain using a fast Fourier transformation to remove any time dependency value, after the fast Fourier data transformation, train a support vector machine using the X, Y and Z axis positions data and the frequency domain data as input, using the support vector machine to predict a set of body positions and movements.

Abstract: In one aspect, a computerized process useful for movement classification using a motion capture suit includes the step of providing the motion capture suit worn by a user. The motion capture suit comprises a set of position sensors and a Wi-Fi system configured to communicate a set of position sensor data to a computing system. The process includes the step of providing the computing system to: receive a set of position data from the motion capture suit for a specified time window of data comprising X, Y and Z axis positions and a joints-angle data for each position sensor of the set of position sensors, transforming each joints-angle data to a corresponding frequency domain using a fast Fourier transformation to remove any time dependency value, after the fast Fourier data transformation, train a support vector machine using the X, Y and Z axis positions data and the frequency domain data as input, using the support vector machine to predict a set of body positions and movements.

Abstract: In one aspect, a method of a motion-capture body suit include the step of providing a motion-capture body suit, wherein the motion-capture body suit comprises a plurality of position sensors, wherein each position sensor of the plurality of position sensors obtains a position of an associated body area, wherein each position sensor is integrated into a textile tunnel located on the associated body are of each position sensor. The method includes the step of obtaining a set of sensor-level position data from the plurality of position sensors as a series of position measurements observed over a specified period of time. The method includes the step of obtaining a body-level position data as another series of body-position measurements observed over the specified period of time. The method includes the step of combining the set of sensor-level position data and the body-level position data using a Kalman filter to produce a body-motion output stream.

Abstract: In one aspect, a method of a motion-capture body suit include the step of providing a motion-capture body suit, wherein the motion-capture body suit comprises a plurality of position sensors, wherein each position sensor of the plurality of position sensors obtains a position of an associated body area, wherein each position sensor is integrated into a textile tunnel located on the associated body are of each position sensor. The method includes the step of obtaining a set of sensor-level position data from the plurality of position sensors as a series of position measurements observed over a specified period of time. The method includes the step of obtaining a body-level position data as another series of body-position measurements observed over the specified period of time. The method includes the step of combining the set of sensor-level position data and the body-level position data using a Kalman filter to produce a body-motion output stream.