Abstract: Systems and methods are described in which the location of a tap on the body of a handheld device is determined in real time using data streams from an embedded inertial measurement unit (IMU). Taps may be generated by striking the handheld device with an object (e.g., a finger), or by moving the handheld device in a manner that causes it to strike another object. IMU accelerometer, gyroscopic and/or orientation (relative to the magnetic and/or gravitational pull of the earth) measurements are examined for signatures that distinguish a tap at a location on the body of the device compared with signal characteristics produced by taps at other locations. Neural network and/or numerical methods may be used to perform such classifications.

Abstract: Systems and methods are described in which the location of a tap on the body of a handheld device is determined in real time using data streams from an embedded inertial measurement unit (IMU). Taps may be generated by striking the handheld device with an object (e.g., a finger), or by moving the handheld device in a manner that causes it to strike another object. IMU accelerometer, gyroscopic and/or orientation (relative to the magnetic and/or gravitational pull of the earth) measurements are examined for signatures that distinguish a tap at a location on the body of the device compared with signal characteristics produced by taps at other locations. Neural network and/or numerical methods may be used to perform such classifications.

Abstract: Systems and methods are described in which the location of a tap on the body of a handheld device is determined in real time using data streams from an embedded inertial measurement unit (IMU). Taps may be generated by striking the handheld device with an object (e.g., a finger), or by moving the handheld device in a manner that causes it to strike another object. IMU accelerometer, gyroscopic and/or orientation (relative to the magnetic and/or gravitational pull of the earth) measurements are examined for signatures that distinguish a tap at a location on the body of the device compared with signal characteristics produced by taps at other locations. Neural network and/or numerical methods may be used to perform such classifications.

Abstract: Systems and methods are described for measuring, using one or more cameras, anthropomorphic features of two or more individuals to classify and/or grade whether the two or more individuals possess physical abilities to collectively perform desired physical acts, actions or activities. The performance of such actions may involve objects with measured or known dimensions, and/or one or more assistive devices to compensate for differences in anthropomorphic features. Anthropomorphic features of the two or more individuals may also be projected into the future based on attributes such as ages, medical conditions, activity levels and predispositions. Classification schemes may use neural network-based approaches and/or statistical methods based on labelled datasets of abilities of two or more individuals with given anthropomorphic features to perform selected actions.