Velocity, acceleration and rotation measurement
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The class of mechanical sensors includes a fairly large number of different sensors based on many principles, but the four groups of general sensors discussed here - force sensors, accelerometers, pressure sensors, and gyroscopes - cover most of the principles involved in the sensing of mechanical quantities either directly or indirectly. Some of these sensors are used for applications that initially do not seem to relate to mechanical quantities. For example, it is possible to measure temperature through the expansion of gases in a volume. The expansion can be sensed through the use of a strain gauge, which is a classical mechanical sensor. In this application an indirect use of a strain sensor is made to measure temperature. On the other hand, some mechanical sensors do not involve motion or force. An example of this is the optical fiber gyroscope, which will be discussed later in this chapter.
Proposed is a light-weight unsupervised decision tree based classification method to detect the user's postural actions, such as sitting, standing, walking and running as user states by analysing the data from a smartphone accelerometer sensor. The proposed method differs from other approaches by applying a sufficient number of signal processing features to exploit the sensory data without knowing any a priori information. Experiments show that the proposed method still makes a solid differentiation in user states (e.g. an above 90% overall accuracy) even when the sensor is operated under slower sampling frequencies.
During running and walking the human centre of mass experiences a symmetric acceleration along the mediolateral direction. This reported work shows how to exploit this knowledge to correct misalignments of the axes of a trunk-mounted accelerometer with respect to the body axes. After vertical alignment, based on the gravitational component of the signal, the technique computes the virtual rotation angle of the axes lying in the horizontal plane. The chosen angle minimises the autocorrelation of the signal along the mediolateral direction.
Presented is an accurate swimming velocity estimation method using an inertial measurement unit (IMU) by employing a simple biomechanical constraint of motion along with Gaussian process regression to deal with sensor inherent errors. Experimental validation shows a velocity RMS error of 9.0 cm/s and high linear correlation when compared with a commercial tethered reference system. The results confirm the practicality of the presented method to estimate swimming velocity using a single low-cost, body-worn IMU.
Knowing the wind speed and direction in an electrical power generating wind farm is an important factor in its management. A measuring device for this function with no moving parts is desirable to reduce maintenance due to wear of the mechanical parts. Ultrasonic measuring techniques can be employed in such a device to fulfill this requirement by measuring the time it takes for a signal to propagate from an emitter to a receiver sensor. Wind in the direction of the sound wave, or against it will affect the travel time of the sound wave and the wind speed can be extracted from these Time Of Flight (TOF) measurements. Commonly used narrowband ultrasonic transducers have the disadvantage of generating a long oscillating signal where the start of the received signal has a very small amplitude and cannot be directly detected and flagged with a simple threshold circuit. The present paper describes a method where two signal bursts of different frequencies within the bandwidth of the transducers are used to obtain the TOF. The phase difference between the emitted and the received signal is measured at two frequencies and the results are then combined to give the TOF. The wind direction can be obtained with an additional measurement in the orthogonal direction by a second pair of sensors.