The paper attempts to evaluate the effect of acceleration sensor mounting on the recorded vibration time course. The study used a prepared model of a railroad rail and triaxial acceleration sensors. ...Three non-invasive methods of mounting the vibration acceleration transducers were selected for analysis: mounting with cyanoacrylate glue, mounting with a magnet, and mounting with wax. The information capacity of the signals was analyzed based on the recorded time waveforms, which totaled more than 90, and their vibration signals. The analysis compared both the basic parameters of the signals (maximum amplitudes and root mean square values) and a comprehensive analysis of the signals using the short-time Fourier transform method, as well as the wavelet transform. The results show significant differences in the recorded signal parameters depending on how the acceleration sensor is mounted, as well as the axis analyzed. The differences can negatively affect the correctness of the measurements made and falsify the picture of the real condition.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and ...smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as 'curse of dimensionality'. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.
Great leaps forward in scientific understanding are often spurred by innovations in technology. The explosion of miniature sensors that are driving the boom in consumer electronics, such as smart ...phones, gaming platforms, and wearable fitness devices, are now becoming available to ecologists for remotely monitoring the activities of wild animals. While half a century ago researchers were attaching balloons to the backs of seals to measure their movement, today ecologists have access to an arsenal of sensors that can continuously measure most aspects of an animal's state (e.g., location, behavior, caloric expenditure, interactions with other animals) and external environment (e.g., temperature, salinity, depth). This technology is advancing our ability to study animal ecology by allowing researchers to (1) answer questions about the physiology, behavior, and ecology of wild animals in situ that would have previously been limited to tests on model organisms in highly controlled settings, (2) study cryptic or wide-ranging animals that have previously evaded investigation, and (3) develop and test entirely new theories. Here we explore how ecologists are using these tools to answer new questions about the physiological performance, energetics, foraging, migration, habitat selection, and sociality of wild animals, as well as collect data on the environments in which they live.
This study evaluates accelerometer performance of three new state of the art smartphones and focuses on accuracy. The motivating research question was whether accelerator accuracy obtained with these ...off-the-shelf modern smartphone accelerometers was or was not statistically different from that of a gold-standard reference system. We predicted that the accuracy of the three modern smartphone accelerometers in human movement data acquisition do not differ from that of the Vicon MX motion capture system. To test this prediction, we investigated the comparative performance of three different commercially available current generation smartphone accelerometers among themselves and to a gold-standard Vicon MX motion capture system. A single subject design was implemented for this study. Pearson's correlation coefficients
were calculated to verify the validity of the smartphones' accelerometer data against that of the Vicon MX motion capture system. The Intraclass Correlation Coefficient (ICC) was used to assess the smartphones' accelerometer performance reliability compared to that of the Vicon MX motion capture system. Results demonstrated that (a) the tested smartphone accelerometers are valid and reliable devices for estimating accelerations and (b) there were not significant differences among the three current generation smartphones and the Vicon MX motion capture system's mean acceleration data. This evidence indicates how well recent generation smartphone accelerometer sensors are capable of measuring human body motion. This study, which bridges a significant information gap between the accuracy of accelerometers measured close to production and their accuracy in actual smartphone research, should be interpreted within the confines of its scope, limitations and strengths. Further research is warranted to validate our arguments, suggestions, and results, since this is the first study on this topic.
This study presents a novel fast Fourier transform (FFT)-based non-contact vibrational harmonics measurement system using a position sensitive detector (PSD) along with calibration using a ...piezoelectric accelerometer. Frequency-domain vibrational analysis is required as the changes in machine dynamics are directly related to its failures and could provide more insight into the vibration signal. In this regard, FFT is used for spectral analysis to detect the harmonics in the vibration signal. The novelty of the applied technique for detecting vibrational harmonics lies in its innate contactless nature where the vibration detection sensor i.e. PSD is placed at a particular distance from the vibrating target. Additionally, the parasitic and external vibrations, which might pose unforeseen errors in the detected vibration data, have been nullified by employing a self-vibration technique using an ADXL-345 three-axis accelerometer. The results obtained through PSD have been calibrated via a standard Brüel & Kjaer (B & K) vibration measurement system which uses a piezoelectric accelerometer (B & K accelerometer). The proposed measurement technique is equipped with NI compact RIO-9074 that features a real-time processor and an FPGA. The system was observed to effectively measure the frequency range 5–600 Hz with a maximum relative error of 2% in FFT amplitudes.
•Several important ruminant behaviours can be predicted from portable accelerometers.•Rarely observed and transitional behaviours are more difficult to predict.•An obstacle to commercial deployment ...arises from a lack of model generalisation.•Large datasets with a wide range of variability ensure a better generalisation.•Pre-processing should be adapted to the objective and protocol of each study.
Precision Technologies are emerging in the context of livestock farming to improve management practices and the health and welfare of livestock through monitoring individual animal behaviour. Continuously collecting information about livestock behaviour is a promising way to address several of these target areas. Wearable accelerometer sensors are currently the most promising system to capture livestock behaviour. Accelerometer data should be analysed properly to obtain reliable information on livestock behaviour. Many studies are emerging on this subject, but none to date has highlighted which techniques to recommend or avoid. In this paper, we systematically review the literature on the prediction of livestock behaviour from raw accelerometer data, with a specific focus on livestock ruminants. Our review is based on 66 surveyed articles, providing reliable evidence of a 3-step methodology common to all studies, namely (1) Data Collection, (2) Data Pre-Processing and (3) Model Development, with different techniques used at each of the 3 steps. The aim of this review is thus to (i) summarise the predictive performance of models and point out the main limitations of the 3-step methodology, (ii) make recommendations on a methodological blueprint for future studies and (iii) propose lines to explore in order to address the limitations outlined. This review shows that the 3-step methodology ensures that several major ruminant behaviours can be reliably predicted, such as grazing/eating, ruminating, moving, lying or standing. However, the areas faces two main limitations: (i) Most models are less accurate on rarely observed or transitional behaviours, behaviours may be important for assessing health, welfare and environmental issues and (ii) many models exhibit poor generalisation, that can compromise their commercial use. To overcome these limitations we recommend maximising variability in the data collected, selecting pre-processing methods that are appropriate to target behaviours being studied, and using classifiers that avoid over-fitting to improve generalisability. This review presents the current situation involving the use of sensors as valuable tools in the field of behaviour recording and contributes to the improvement of existing tools for automatically monitoring ruminant behaviour in order to address some of the issues faced by livestock farming.
Physical activity (PA) among children and adolescents is often reported by time segments centered around the school day, including before school. However, there is no consistent approach to defining ...the before-school segment, to accurately capture PA levels and facilitate synthesis of results across studies. Therefore, this study aimed to (a) examine how studies with children and adolescents have defined the before-school segment, and (b) compare adolescents’ before-school PA using various segment definitions. We conducted a systematic search and review of literature from six databases, and subsequently analyzed accelerometer data from Australia ( n = 472, mean age 14.9 years, 40% male), to compare PA across five before-school definitions. Our review found 69 studies reporting before-school PA, 59 of which used device-based measures. Definitions ranged widely, but justifications were rarely reported. Our empirical comparison of definitions resulted in a range of participants meeting wear time criteria (≥3 days at >50% of segment length) from the latest-starting definition (30 min prior to school; n = 443) to the earliest-starting definition (6:00 a.m.–school start; n = 155), implying that for many participants, accelerometer wear was low in the early hours due to sleep or noncompliance. Statistically significant differences in light and moderate-to-vigorous PA (mean minutes/school day, proportion of segment length, and proportion of wear time) were found between definitions, indicating that before-school PA could potentially be underestimated depending on definition choice. We recommend that future studies clearly report and justify segment definition, apply segment-specific wear time criteria, and collect wake time data to enable individualized segment start times and minimize risk of data misclassification.
BACKGROUNDFew studies have examined the non-linear relationships of objectively-measured sedentary behavior and physical activity with insomnia symptoms in older adults. We investigated such ...relationships of sedentary and physically-active behaviors with total sleep time and nocturnal wakefulness. METHODSWe recruited adults aged 60 years and above who have received health check-ups or been to geriatric outpatient services from a hospital setting. Sedentary and physically-active behaviors, total sleep time, and wakefulness time after sleep onset were measured by Actigraphy, and their relationships were estimated using generalized additive models. RESULTSThe 157 older adults receiving health-related services slept 7.5 h (20.8 min awake) on average per day. Total sleep time was negatively associated with sedentary and physically-active behaviors. By contrast, a U-shape relationship was found between sedentary behavior and wakefulness time after sleep onset, with a turning point at a daily sedentary time of 10.9 h. CONCLUSIONLonger high-intensity physical activity time was related to a shorter wakefulness time after sleep onset. By contrast, daily sedentary time longer than 10.9 h was related to shorter total sleep time but more nocturnal wakefulness time. Future nonpharmacological strategies for sleep improvement should consider the sedentary threshold.
•The structure combines the advantages of cantilever beam and circular diaphragm.•The structure adopts single sensing element and single inertial mass block.•The sensitivity and lateral ...anti-interference ability are improved.
To meet the requirements for low-frequency vibration monitoring, a new type of FBG (fiber Bragg grating) accelerometer based on diaphragm-type cantilever is proposed. The theory analysis of the structure was carried out and the finite element model was constructed to simulate and analyze the acceleration sensing characteristic of the sensor. Simultaneously, the tested results of sensing characteristic from the shaking table indicate that the system has excellent response to low-frequency acceleration excitation signal when the natural frequency of the system is 90 Hz. The frequency response range of the system is 5.0–60.0 Hz, in which the acceleration sensitivity is 485.75 pm/g. The acceleration sensor is designed with strong lateral immunity since the sensitivity in the transverse sensitivity is only 3.6% of the sensitivity in the working direction.
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