The present invention provides systems, methods and apparatus for a wearable band adapted to be worn by a user. The wearable band may include one or more biometric sensors such as a pulse monitor adapted to monitor a pulse of the user, and a transmitter adapted to wirelessly transmit pulse information from the pulse monitor to a mobile device such as a cellular telephone or PDA. The wearable band does not include a display for the biometric information. Numerous additional embodiments are disclosed.

A mixer vehicle includes a mixer drum, a first acceleration sensor, a second acceleration sensor, and a controller. The first acceleration sensor is configured to produce first acceleration signals and the second acceleration sensor is configured to measure accelerations within the mixer drum to produce second acceleration signals. The controller is configured to receive the first acceleration signals from the first acceleration sensor and second acceleration signals from the second acceleration sensor. The controller is further configured to determine a presence of material within the mixer drum based on the first acceleration signals and the second acceleration signals. The controller is further configured to determine one or more properties of the material within the mixer drum based on the first acceleration signals and the second acceleration signals.

A mixer vehicle includes a mixer drum, a first acceleration sensor, a second acceleration sensor, and a controller. The first acceleration sensor is configured to produce first acceleration signals and the second acceleration sensor is configured to measure accelerations within the mixer drum to produce second acceleration signals. The controller is configured to receive the first acceleration signals from the first acceleration sensor and second acceleration signals from the second acceleration sensor. The controller is further configured to determine a presence of material within the mixer drum based on the first acceleration signals and the second acceleration signals. The controller is further configured to determine one or more properties of the material within the mixer drum based on the first acceleration signals and the second acceleration signals.

Motion can be induced at a vehicle, e.g., by actuating components of an active suspension system, and first sensor data and second sensor data representing an environment of the vehicle can be captured at a first position and a second position, respectively, resulting from the induced motion. A second sensor can determine motion information associated with the first position and the second position. Calibration information about the sensor, the first sensor data, and the motion information can be used to determine an expectation of sensor data at the second position. A calibration error can be the difference between the second sensor data and the expected sensor data.

Motion can be induced at a vehicle, e.g., by actuating components of an active suspension system, and first sensor data and second sensor data representing an environment of the vehicle can be captured at a first position and a second position, respectively, resulting from the induced motion. A second sensor can determine motion information associated with the first position and the second position. Calibration information about the sensor, the first sensor data, and the motion information can be used to determine an expectation of sensor data at the second position. A calibration error can be the difference between the second sensor data and the expected sensor data.

In a method of estimating displacement of a bridge, a first displacement including a low frequency component and a first high frequency component is generated based on a strain that is measured by a plurality of pairs of strain gauges installed at positions in a first direction from a reference point, in a bridge, a second displacement including a second high frequency component is generated based on an acceleration that is measured by an accelerometer installed at a first position spaced apart from the reference point by a first distance in the first direction, in the bridge, and a final displacement of the bride is generated based on an unknown parameter associated with the displacement, the low frequency component and the second high frequency component. The unknown parameter is generated by applying a recursive least square algorithm to the first high frequency component and the second high frequency component.

Examples are disclosed that relate to methods and systems for classifying a surface type. One example provides a system comprising a wearable device comprising at least one force sensor, and a computing device having a processor and associated memory storing instructions executable by the processor. The instructions are executable by the processor to, during a training phase, receive training data including a plurality of training data pairs. Each training data pair includes force sensor training data received from the at least one force sensor, or from a simulation or observation, and a label indicating at least one of a plurality of defined surface types. An AI model is trained to predict a classified surface type based on run-time force sensor data. The run-time force sensor data is input into the trained AI model to thereby cause the AI model to output a predicted classification of a run-time surface type.

Examples are disclosed that relate to methods and systems for classifying a surface type. One example provides a system comprising a wearable device comprising at least one force sensor, and a computing device having a processor and associated memory storing instructions executable by the processor. The instructions are executable by the processor to, during a training phase, receive training data including a plurality of training data pairs. Each training data pair includes force sensor training data received from the at least one force sensor, or from a simulation or observation, and a label indicating at least one of a plurality of defined surface types. An AI model is trained to predict a classified surface type based on run-time force sensor data. The run-time force sensor data is input into the trained AI model to thereby cause the AI model to output a predicted classification of a run-time surface type.

A method determines a corrected distance between a mobile transceiver fastened to a vehicle and a fixed transceiver. The method includes: identification, among a set of predetermined path segments, of the segment on which the vehicle is currently found on the basis of the last position determined for this vehicle, then selection of a correction function specifically associated with the identified segment using a table that associates, with each path segment, a respective correction function, then execution of the identified correction function to obtain a current correction coefficient for correcting a raw distance computed from transmission and reception times of the radio signals exchanged between the fixed and mobile transceivers, then correction of the last raw distance computed using the current correction coefficient to obtain the corrected distance.

A method determines a corrected distance between a mobile transceiver fastened to a vehicle and a fixed transceiver. The method includes: identification, among a set of predetermined path segments, of the segment on which the vehicle is currently found on the basis of the last position determined for this vehicle, then selection of a correction function specifically associated with the identified segment using a table that associates, with each path segment, a respective correction function, then execution of the identified correction function to obtain a current correction coefficient for correcting a raw distance computed from transmission and reception times of the radio signals exchanged between the fixed and mobile transceivers, then correction of the last raw distance computed using the current correction coefficient to obtain the corrected distance.