A strain measuring device (100) is a strain measuring device (100) for measuring strain on an FEP (10), and includes a viscous body (101) that has a lower rigidity than the FEP (10) and covers an uneven surface of the FEP (10), and a strain gauge (102) that is attached to a portion of a surface of the viscous body (101).

A strain measuring device (100) is a strain measuring device (100) for measuring strain on an FEP (10), and includes a viscous body (101) that has a lower rigidity than the FEP (10) and covers an uneven surface of the FEP (10), and a strain gauge (102) that is attached to a portion of a surface of the viscous body (101).

The present invention discloses a surgical traction system with a hand gripped tension adjustment device that slidably mounts onto a boom structure. When activated the tension adjustment device moves down the boom structure and incrementally adjusts the tension in a traction rope. The boom structure attaches to a bracket assembly that mounts onto the short end of a surgical table.

The present invention discloses a surgical traction system with a hand gripped tension adjustment device that slidably mounts onto a boom structure. When activated the tension adjustment device moves down the boom structure and incrementally adjusts the tension in a traction rope. The boom structure attaches to a bracket assembly that mounts onto the short end of a surgical table.

A wearable device can include a wearable band configured to contact a user of the wearable device, an actuator, a sensor, and one or more processors in communication with the actuator and the sensor. The processors can be configured to measure a back electromotive force (“EMF”) of the actuator; determine, based on the measured back EMF, data that describes a contact force between the wearable band and the user; and determine, based on the data that describes the contact force, a quality metric describing a data quality of sensor data collected by the sensor. In some embodiments, the processor(s) can determine, generate sensor output data based on the sensor data and based at least in part on the data describing the contact force between the wearable band and the user. For example, one or more machine-learned models maybe leveraged to generate sensor output data that is compensated for the wearable band being too tight or too loose.

A wearable device can include a wearable band configured to contact a user of the wearable device, an actuator, a sensor, and one or more processors in communication with the actuator and the sensor. The processors can be configured to measure a back electromotive force (“EMF”) of the actuator; determine, based on the measured back EMF, data that describes a contact force between the wearable band and the user; and determine, based on the data that describes the contact force, a quality metric describing a data quality of sensor data collected by the sensor. In some embodiments, the processor(s) can determine, generate sensor output data based on the sensor data and based at least in part on the data describing the contact force between the wearable band and the user. For example, one or more machine-learned models maybe leveraged to generate sensor output data that is compensated for the wearable band being too tight or too loose.

A method and apparatus for calculating line sag in a span of a conductor is provided. The method includes using a portable smart device having one or more accelerometers and running a line sag application on the processing device. The line sag application enables acceleration data of return waves generated on the conductor to be collected using the smart device and to be plotted as a function of time for display on the smart device. The method further includes placement of time markers on the plotted data displayed on the smart device to determine elapsed time and calculating line sag using the elapsed time.

A wearable device can include a wearable band configured to contact a user of the wearable device, an actuator, a sensor, and one or more processors in communication with the actuator and the sensor. The processors can be configured to measure a back electromotive force (“EMF”) of the actuator; determine, based on the measured back EMF, data that describes a contact force between the wearable band and the user; and determine, based on the data that describes the contact force, a quality metric describing a data quality of sensor data collected by the sensor. In some embodiments, the processor(s) can determine, generate sensor output data based on the sensor data and based at least in part on the data describing the contact force between the wearable band and the user. For example, one or more machine-learned models maybe leveraged to generate sensor output data that is compensated for the wearable band being too tight or too loose.

A wearable device can include a wearable band configured to contact a user of the wearable device, an actuator, a sensor, and one or more processors in communication with the actuator and the sensor. The processors can be configured to measure a back electromotive force (“EMF”) of the actuator; determine, based on the measured back EMF, data that describes a contact force between the wearable band and the user; and determine, based on the data that describes the contact force, a quality metric describing a data quality of sensor data collected by the sensor. In some embodiments, the processor(s) can determine, generate sensor output data based on the sensor data and based at least in part on the data describing the contact force between the wearable band and the user. For example, one or more machine-learned models maybe leveraged to generate sensor output data that is compensated for the wearable band being too tight or too loose.

A method for determining the tension of a drive belt, wherein the drive belt is provided for transmission of a torque generated by a drive unit to a load, for example, a door leaf of a door system includes a first process step a., the load is moved from a moving state into a rest state. In a following process step b., the motor voltage is recorded during execution of process step a. and the time-resolved motor voltage curve is prepared. In step c., the motor current is determined from the recorded motor voltage or the motor voltage curve and a time-resolved motor current curve is prepared. In a last step d., the time-resolved motor current curve is evaluated and the tension of the drive belt is determined by means of selected curve features of the motor current curve.