A system and method for collecting a set of kinematic data streams from an activity tracking system with at least one activity monitoring system that is attached to a user; segmenting at least a subset of the kinematic data streams into a set of segments according to actions of the user; generating a set of biomechanical signals from the kinematic data streams wherein a biomechanical signal characterize a biomechanical property during a segment and includes generating a first integration data set through data integration and baseline error correction across the set of segments of the normalized kinematic data stream and determining at least a first biomechanical signal that is at least partially based on the first integration data set; and applying the set of biomechanical signals.

The performance of a microelectromechanical systems (MEMS) device may be subject to unwanted thermal gradients or nonuniform temperatures. The thermal gradients may be approximated based on voltage measurements taken through bond wires coupled to bond points located on the MEMS device. Thermal gradient measurement may be improved depending on the arrangement of bond wires and/or the material of the bond wires. Sense circuitry that is coupled to the MEMS device may determine corrective actions, such as updating the operation of the MEMS device, that compensate for the adverse effects from the thermal gradients.

This measurement device for measuring the angular velocity or acceleration of a two-wheel vehicle, is provided with a main detection unit which detects the three-axis angular velocity or three-axis acceleration, a support unit which can support the main detection unit on the body of the two-wheel vehicle, and a correction unit which cancels the lean of the body to the left and right in the main detection unit.

Examples of systems and methods for locating movable objects such as carts (e.g., shopping carts) are disclosed. Such systems and methods can use dead reckoning techniques to estimate the current position of the movable object. Various techniques for improving accuracy of position estimates are disclosed, including compensation for various error sources involving the use of magnetometer and accelerometer, and using vibration analysis to derive wheel rotation rates. Various techniques utilize characteristics of the operating environment in conjunction with or in lieu of dead reckoning techniques, including characteristic of environment such as ground texture, availability of signals from radio frequency (RF) transmitters including precision fix sources. Navigation techniques can include navigation history and backtracking, motion direction detection for dual swivel casters, use of gyroscopes, determining cart weight, multi-level navigation, multi-level magnetic measurements, use of lighting signatures, use of multiple navigation systems, or hard/soft iron compensation for different cart configurations.

Systems and methods for providing location and guidance, and more particularly for providing location and guidance in environments where global position systems (GPS) are unavailable or unreliable (GPS denied and/or degraded environments) utilizing inertial measurement units IMUs to provide such location and guidance. A series of low-accuracy or low-resolution IMUs, in combination, are utilized to provide high-accuracy or high-resolution location and guidance results along with an electronics-control system for handing off control of the measurement and guidance of a body in flight between groups or subgroups of IMUs to alternate between high dynamic range/lower resolution and lower dynamic range/higher resolution measurement and guidance as the environment dictates.

Controlling machine pose using sensor fusion includes receiving from each of a plurality of Inertial Measurement Units mounted on different components, a time series of signals indicative of acceleration and angular rate of motion for each of the components of the machine, and from at least one non-IMU sensor, a signal indicative of at least one of position, velocity, or acceleration of at least one of the components, a position, velocity, or acceleration of any potential obstacles or other features, or an operator input. The signals are fused with a separate Kalman filter module to estimate an output joint angle. Estimated and measured values of the output joint angle in successive timesteps are combined, a kinematic equation is solved to determine a real time value for at least one of position, velocity, and acceleration of the component at successive timesteps, and the determined real time value is applied in an implementation of a controlled operational movement of the machine component in a successive time step.

Technologies for determining a user's location by a mobile computing device include detecting, based on sensed inertial characteristics of the mobile computing device, that a user of the mobile computing device has taken a physical step in a direction. The mobile computing device determines a directional heading of the mobile computing device in the direction and a variation of an orientation of the mobile computing device relative to a previous orientation of the mobile computing device at a previous physical step of the user based on the sensed inertial characteristics. The mobile computing device further applies a Kalman filter to determine a heading of the user based on the determined directional heading of the mobile computing device and the variation of the orientation and determines an estimated location of the user based on the user's determined heading, an estimated step length of the user, and a previous location of the user at the previous physical step.

A-implemented method initializes a localization system for a vehicle, the localization system using map data comprising a plurality of maps for localizing the vehicle in an area The method includes associating map related information with a last available position of the vehicle which was used by the localization system before shutdown of the localization system, wherein the map related information comprises a unique identifier for a first map of the plurality of maps which was used for localizing the vehicle at the last available position, at start-up of the localization system with respect to the last available position, loading a map from the plurality of maps for localizing the vehicle at the last available position and verifying if the loaded map is the first map by using the unique identifier, and when it is verified that the first map is loaded, initializing the localization system with the last available position in the first map.

Embodiments are disclosed for submersion detection and underwater depth and low-latency temperature estimation. In an embodiment, a method comprises: determining a first set of vertical accelerations obtained from an inertial sensor of a wearable device; determining a second set of vertical accelerations obtained from pressure data; determining a first feature associated with a correlation between the first and second sets of vertical accelerations; and determining that the wearable device is submerged or not submerged in water based on a machine learning model applied to the first feature. In another embodiment, a method comprises: determining a submersion state of a wearable device; and responsive to the submersion state being submerged, computing a forward estimate of water temperature based on measured ambient water temperature at the water surface, a temperature error lookup table, and a rate of change of the ambient water temperature.

Techniques described are related to determining when a discrepancy between data of multiple sensors (e.g., IMUs) might be attributable to a sensor error, as opposed to operating conditions, such as sensor bias or noise. For example, the sensor data is passed through one or more filters (e.g., bandpass filter) that model the bias or noise, and the filtered data may then be compared for consistency. In some examples, consistency may be based on residuals or some other metric describing discrepancy among the filtered sensor data.