[ PEBBLE] Working With accelerometer NEW!
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As we have seen back in Chapter 1, there are several sensors packed into Pebble smartwatches. The sensors available depend on the model of smartwatch, but many smartwatches have a 3-axis accelerometer, a magnetometer, and an ambient light sensor. We will also access the battery information, even though it is technically not a sensor. Accessing the timer is also not a sensor, but we will discuss it anyway. We will also consider how to work with the vibrating motor.
A magnetometer is an instrument that measures the direction and strength of a magnetic field. In a Pebble smartwatch, a magnetometer can be used to calculate the smartwatch's position relative to the Earth's magnetic north. The operating system combines magnetometer measurements with accelerometer data to both calibrate a compass and to provide data on the heading of smartwatch with respect to magnetic north.
We have spent considerable time and space in this chapter discussing how to work with sensors on a Pebble smartwatch. However, the point to this chapter was not really to understand sensor programming, although that's a good result, but to understand the techniques that system programming in C uses and to practice working with those techniques. In particular, here's a few lessons that should stand out from this chapter.
We know from previous studies that limb-specific symptom severity requires a larger number of sensors19,20,21,22, but also that using multiple sensors increases patient burden and may not be feasible for long-term monitoring of patients participating in clinical trials or for disease management. For this reason, many research studies have used a smartphone23,24,25 or a combination of a smartphone and a smartwatch26 to collect accelerometer data in the home and community settings and estimate motor symptom severity in individuals with PD. Using a minimum set of sensors does not allow one to monitor motor patterns displayed by individual body segments. However, it is unclear if this is necessary from a clinical point of view. To enable a comparison between monitoring individuals using a minimum set of sensors and monitoring them using sensors on each major body segment (i.e. upper limbs, lower limbs, and trunk), subjects recruited at Spaulding Rehabilitation Hospital were also outfitted with a set of five Shimmer 3 sensors (Shimmer Research Ltd, Dublin, Ireland), one on each limb and one on the pelvis. This subset of data is described in detail in the companion paper27.
The intermittent motion characteristics of bed sediment and the randomness of particle motion forms make the experimental observation of sediment entrainment very difficult. At present, experimental measurement techniques such as underwater photography (UP) and high-speed imaging are used to observe and measure the phenomenon of particle motion. However, the vibration data during sediment entrainment cannot be measured by traditional methods, and new monitoring methods need to be explored. In recent years, the miniaturization of sensing equipment has made the concept of a smart pebble21,30,31,32,33,34,35 (a small, free-moving multisensor capable of measuring inertial dynamics such as acceleration and angular velocity) feasible. Maniatis proposed a new method to approximate the probability of individual coarse particle entrainment display21, and then measured the inertial drag and lift of coarse particles on a rough alluvial layer using a particle accelerometer35. Oliver Gronz34 used a small nine-axis sensor implanted in a stone to track the movement of the pebbles. Smart pebbles have measured the sediment transport process, but the sediment vibration process has not been monitored.
To study the mechanism of sediment vibration, this paper is the first to explore the method of monitoring the natural sediment vibration process using micro-accelerometer. The vibration signal of a fully exposed, isolated natural pebble was collected through a laboratory flume experiment, and the local water velocity was measured simultaneously using an acoustic Doppler velocimeter (ADV). Explore the relationship between near-bed flow velocity and pebble vibration, and analyze the pebble vibration characteristics (vibration intensity, vibration frequency).
The experiment was designed so that pebble vibrations could be more easily identified and correlated with simultaneously measured near-bed velocities. In this study, to simplify the phenomenon to its most elemental form to facilitate the development of cause and effect relations (while retaining the physics which dominates), the vibration of an isolated, fully exposed, natural particle placed on a rough bed was examined.
Experiments were conducted in the Hydraulics Laboratory at Chongqing Jiaotong University. A glass-walled, tilting rectangular flume which was 0.55-m wide, 0.65-m deep, and 25-m long, was used. The bed slope was kept constant for all experiments, at 0.3%. Water was supplied to the flume using pumps that draw from a constant head reservoir. The flow rate is controlled by a variable speed pump. The flume was laid with a concrete bottom of the same roughness. To ensure fully developed turbulent conditions, the test section was located 15 m downstream of the flume inlet. Fully exposed, isolated smart pebble was placed in the test section, and their vibrational processes, as well as entrainment events and local flow velocities, were monitored. The local flow velocity was measured with an acoustic Doppler velocimeter (ADV) with a sampling frequency of 100 Hz, which was set 10 cm upstream along the centerline of the test particles, an arrangement illustrated in Fig. 1c.
We conducted flume tests under five uniform flow conditions to observe the movement of pebble under different flow conditions. The sediment vibration process becomes random due to the near-bed turbulence effect and the randomness of the geometric conditions of the sediment location on the bed26,36. Therefore, pebbles at the same flow rate were placed at five different locations for measurement, and five sets of data were collected with a collection time of 30 s. The following steps were repeated for each flow condition. First, the test flow was released for a sufficient time to measure the flow velocity in the lateral profile 10 cm upstream of the smart pebble with ADV to determine the flow as a fully developed uniform flow. Subsequently, after the accelerometer and ADV system had reached stable operation, the near-bed flow velocity and acceleration were collected simultaneously. Twenty-five sets of data were measured in sequence.
Table 2 shows the statistical parameters of 25 sets of acceleration data, and it can be found that the statistical parameters vary under the same water flow conditions without any regularity, which again proves that the pebble vibration is a random phenomenon influenced by the bed position. Table 2 shows that of the 25 sets of mean acceleration data, 24 groups are greater than zero, and 1 set is less than zero, indicating that the pebble vibrate mainly in the direction of the water flow under the impact of the current, and a few cases in the opposite direction of the water flow. The latter event was due to the raised contact surface between the pebble and the riverbed, which prevented the pebble from vibrating forward. To overcome the randomness caused by the bed shape and to analyze the pattern of statistical parameters with flow rate, the parameters under the same flow conditions are averaged in this paper. From the mean data, it can be seen that before approaching the threshold value, the mean value of mean squared difference and maximum value tends to increase, and the mean value of minimum value decreases as the flow increases, indicating that the discrete degree of pebble vibration acceleration is enhanced. However, near the threshold value, the mean values of mean squared difference and maximum value decrease, and the mean value of minimum value increases. The mean values of skewness coefficients were more significant than 1 for different water flow conditions, indicating that the probability distribution graph of vibration acceleration was shifted to the left.
The near-bed velocity is the main water flow parameter that determines whether the sediment vibrates and the intensity of the vibration. Observing the PDF plot of the instantaneous flow velocity near the bottom (Fig. 3e), it was found that it approximately obeyed a normal distribution (consistent with the findings of related studies). The pebble vibration event inherits the randomness of turbulent fluctuations, and the vibration acceleration PDF plot (Fig. 3f) before pebble entrainment is observed to conform to a normal distribution function. It belongs to the normal distribution with large kurtosis and sloping to the left. The results indicate that the pebble vibration is strongly correlated with the current action. The probabilistic model is characterized by a left near-Gaussian function and a long right tail. The acceleration in the near Gaussian part is relatively small and is mainly caused by in-situ vibrational events. The long right tail describes the ectopic strong vibrational events, so it is most relevant to high-energy turbulence events.
The near-bed flow velocity was converted from the time domain to the frequency domain (Fig. 5). It is found that 97% of the energy of the flow velocity signal is concentrated within 20 Hz. The results indicate that the near-bed flow velocity signal is a low frequency signal39. Therefore, both the pebble vibration acceleration and the flow velocity are low-frequency signals with similar frequencies. The pebble is excited by the water flow to produce a vibration response, and vibrate according to the frequency of the excitation signal, in line with the pebble vibration mechanism, proving that the data measured by the measurement system are reliable.
Based on the phenomenon of vibration or swaying before sediment particle entrainment, a micro inertial accelerometer was used for the first time to measure the vibration process of fully exposed, isolated natural pebble on a rough bed surface and to collect the near-bed velocity simultaneously. In this paper, a series of experimental studies with different water flow conditions were conducted. This study is the first attempt to collect vibrational acceleration data of sediment particles and analyze them in conjunction with near-bed turbulence data, and the main findings are as follows, 153554b96e
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