An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

An electronic speed detection system is provided. The electronic speed detection system includes an accelerometer coupled to a moving object and a hybrid altimeter, which includes a first pressure sensor coupled to the moving object and a second pressure sensor. The electronic speed detection system also includes a controller comprising a memory and a processor. The memory storing computer program instructions executable by the processor to cause the electronic speed detection system to determine a normalized position based on first inputs received from the hybrid altimeter, or determine a corrected velocity position based on a second input received from the accelerometer and the normalized position.

An electronic speed detection system is provided. The electronic speed detection system includes an accelerometer coupled to a moving object and a hybrid altimeter, which includes a first pressure sensor coupled to the moving object and a second pressure sensor. The electronic speed detection system also includes a controller comprising a memory and a processor. The memory storing computer program instructions executable by the processor to cause the electronic speed detection system to determine a normalized position based on first inputs received from the hybrid altimeter, or determine a corrected velocity position based on a second input received from the accelerometer and the normalized position.

A method and computer program are provided for operating a motorized two-wheeled vehicle, in particular a motorcycle that includes a sensor system for accident recognition that generates measuring signals. The sensor system is used for recognizing a rotation of a front wheel of the two-wheeled vehicle that deviates from a normal steering movement and allows an inference concerning a collision of the two-wheeled vehicle with another object. Moreover, the invention relates to a computer program for carrying out the method.

The present invention involves a test subject performing a sit-to-stand (STS) operation while wearing a device (MD) that contains an acceleration sensor (11) on the front of the chest. The present invention derives a muscular strength index (maximum acceleration value per unit of muscle mass during STS activity) representing the muscular strength of a human body by obtaining maximum acceleration value data from a signal expressing the size of an acceleration vector comprising a tri-axial component in detected acceleration, and using the maximum acceleration value data and the muscle mass or body fat mass of the text subject.

The present invention involves a test subject performing a sit-to-stand (STS) operation while wearing a device (MD) that contains an acceleration sensor (11) on the front of the chest. The present invention derives a muscular strength index (maximum acceleration value per unit of muscle mass during STS activity) representing the muscular strength of a human body by obtaining maximum acceleration value data from a signal expressing the size of an acceleration vector comprising a tri-axial component in detected acceleration, and using the maximum acceleration value data and the muscle mass or body fat mass of the text subject.

A device for multi-parameter integrated monitoring of a deep submarine turbidity current primarily includes cement pile pore-pressure monitoring, optical turbidity monitoring, floating ball flow velocity monitoring, and turbidity current sediment sampling, can observe the turbidity, excess pore pressure, flow velocity, and other parameters of the turbidity current, can fulfill simultaneous and real-time transmission for in-situ monitoring, and can complete multiple tasks at the same observing position, so that the situation where the sampling position and the observing position are different due to the movement of an apparatus along with a ship during ordinary work is avoided.

A device for multi-parameter integrated monitoring of a deep submarine turbidity current primarily includes cement pile pore-pressure monitoring, optical turbidity monitoring, floating ball flow velocity monitoring, and turbidity current sediment sampling, can observe the turbidity, excess pore pressure, flow velocity, and other parameters of the turbidity current, can fulfill simultaneous and real-time transmission for in-situ monitoring, and can complete multiple tasks at the same observing position, so that the situation where the sampling position and the observing position are different due to the movement of an apparatus along with a ship during ordinary work is avoided.

A method includes determining a first speed value based on a first signal from a first data source. The method also includes determining a second speed value based on a second signal from a second data source. The method further includes determining a first likelihood of icing value based on a third signal from a third data source. The method also includes determining a second likelihood of icing value based on a fourth signal from a fourth data source. The method further includes performing a first comparison between the first speed value and the second speed value and performing a second comparison between the first likelihood of icing value and the second likelihood of icing value. The method also includes generating sensor reliability data based on the first comparison and the second comparison and displaying situational awareness data based on the sensor reliability data.