Velocity, acceleration and rotation measurement
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A method of estimating force using an accelerometer is presented. This model is based on estimating the resultant acceleration of a body at its centre of mass using a triaxial accelerometer. A data set of ground reaction forces are gathered using a force platform, which is used as the control for this experiment. Signal processing techniques for resampling the accelerometer signals, along with a method of cross correlation to align the force platform and accelerometer traces are used. The purpose of this study was to compare force calculated using accelerometer data from the SHIMMER device, with force platform data on counter movement and drop jumps, for use in sports biomechanics. The method was validated using twelve physically active adults who performed 5 counter movement jumps and 5 drop jumps from a height of 0.30 m. An accelerometer was attached near the participant's centre of mass and simultaneous force and acceleration data were obtained for the jumps. Minimum eccentric force and peak concentric force were calculated concurrently for countermovement jumps and peak landing forces were calculated concurrently for drop jumps. The results showed moderate to low levels of agreement in forces and a consistent systematic bias between the results from the force platform and accelerometer. However, good agreement between the accelerometer and force platform was observed during the eccentric phase of the countermovement jump.
Groundwater accessed by handpumps is the primary water supply for many people in Africa. This “Smart Water” project considers the study of a region of Kenya where there is significant demand for groundwater, especially among the poor. Some of the engineering aims of this project are to determine if data acquired from accelerometers mounted in hand-pumps can be used to perform three tasks: (i) estimate the depth of the groundwater at the pump, (ii) predict pump failure, and (iii) classify the user of the pump (e.g., as being a man, woman, or child). This paper describes an initial investigation, based on one week of data collection, that demonstrates there is useful information in the accelerometer data collected from handpumps, which can be discovered using machine learning techniques. We show that features derived from the accelerometry data exhibit stable, similar behaviour suggesting that users and pump locations may be characterised. We demonstrate that a machine learning system can classify the data according to person and pump and accurately differentiate between different users. We conclude that our preliminary study suggests that information may exist in accelerometry from handpumps that could allow us to answer the three main questions of the “Smart Water” project, described above, motivating a largescale' data-collection activity.
The paper reviews features and applications of the modified inductive sensor, able to operate in contact with exhaust gases of temperature even as high as 1200 K. The new design includes the metal-ceramic housing ensuring proper heat transfer, the magnetic circuit containing set of permanent magnets with various magnetic field values and Curie temperatures, the completely redesigned windings and current/voltage converter used instead of the electromotive force amplifier. Its principle of operation is based on electrodynamical interaction and therefore it may be referred as a passive eddy-current sensor. Two applications of the technique are presented such as blade vibration measurement in High Pressure Turbine of military turbofan (tip-timing) and speed monitoring of roller-element bearing supporting the shaft in a turbojet engine.
