Three-axis Acceleration Sensor Research Articles

Exploring Physical Activity Levels in Patients with Cardiovascular Disease-A Preliminary Study.

Increased physical activity may prevent disease onset and severity in individuals with cardiovascular disease. However, studies evaluating physical activity in people with cardiovascular disease are limited. This prospective observational study aimed to objectively assess the level of physical activity in patients with cardiovascular disease and determine the actual extent of physical activity in their daily lives. Participants aged 20 years or older with cardiovascular disease at a cardiology clinic were included. Physical activity was measured using an activity meter with a three-axis acceleration sensor. Overall, 58 patients were included in the study. Household activities were found to be more frequent sources of physical activity. The step count was related to age and housework, while total physical activity and household activity were related to age and work. Locomotive activity was related to sex and housework. Total physical and household activities tended to decrease with age. These findings indicate the influence of work and household chores on physical activity and suggest that physical activity may be underestimated if household activity is not also assessed. These fundamental findings may provide clinical evidence to underpin physical activity for patients with cardiovascular disease.

Evaluation of Fatigue in Older Drivers Using a Multimodal Medical Sensor and Driving Simulator

In recent years, the spread of wearable medical sensors has made it possible to easily measure biological signals such as pulse rate and body acceleration (BA), and from these biological signals, it is possible to evaluate the degree of biological stress and autonomic nervous activity in daily life. Accumulated fatigue due to all-day work and lack of sleep is thought to be a contributing factor to distracted driving, and technology to estimate fatigue from biological signals during driving is desired. In this study, we investigated fatigue evaluation during a driving simulator (DS) using biological information on seven older subjects. A DS experiment was performed in the morning and afternoon, and no significant differences were observed in the change over time of heart rate variability and skin temperature. On the other hand, in the afternoon DS, changes in arousal and body movements were observed based on BA calculated from the three-axis acceleration sensor and fingertip reaction time in a psychomotor vigilance test. It is suggested that by combining biological information, it may be possible to evaluate the degree of fatigue from the presence or absence of arousal and changes in body movements while driving.

Binocular vision vibration measurement based on pixel coordinate matching of inner corner points in a chequerboard

A binocular vision measurement system provides a simple method for obtaining three-dimensional vibration data from moving objects, which is suitable for vibration monitoring of large structures such as bridges. Aiming to address the problem that the feature selection process for binocular visual inspection affects the measurement accuracy, chequerboard feature points are selected in this paper for carrying out a visual displacement measurement method. Firstly, pixel coordinate matching of the inner corner points in the chequerboard is completed and then a binocular vision measurement system is established. The measurement results are compared with using circular feature points. Secondly, the binocular vision measurement model is applied to the vibration measurement of a cantilever beam. Using comparisons with a three-axis acceleration sensor, the effectiveness and accuracy of this method are evaluated. Finally, the method is applied to measure the vibration of the cantilever beam under different load conditions and its vibration characteristics are analysed. The results show that the accuracy of the binocular vision measurement method based on pixel coordinate matching of the inner corner points in the chequerboard is higher than that using circular feature points. From comparisons with the acceleration sensor, the measurement error of this method is found to be small. In addition, the method can effectively analyse the vibration performance of a cantilever beam under different load conditions. Therefore, this measurement method is effective and provides a theoretical basis for the identification of vibration characteristics in large engineering structures.

Measurement method of wet gas two-phase flow rate based on the main frequency and the modulus of vector acceleration of swirl flowmeter

The swirl flowmeter is widely used in the wellhead measurement of low-permeability gas fields in China. In this study, the MEMS three-axis acceleration sensor (MEMS-TAAS) replaced the traditional piezoelectric sensor to distinguish the frequency signal from the interference noise. Based on the MEMS-TAAS, the vector acceleration signals of swirl flowmeter were acquired and analyzed simultaneously with the precession frequency signals to establish a wet gas two-phase flow measurement model. The method of establishing a liquid volume fraction prediction model based on the vector acceleration modulus parameter was pioneered. Finally, a dual-parameter wet gas measuring method under normal pressure was constructed using an iteration algorithm. The results demonstrate that this model’s relative errors are acceptable. This method effectively provides further development thought for swirl flowmeter which can achieve the accurate prediction of two-phase flow using only one swirl meter with a MEMS-TAAS, without the need for other flow meters or differential pressure sensors, especially for the low-liquid-fraction wet gas.

Classification and Recognition of Goat Movement Behavior Based on SL-WOA-XGBoost

Aiming at the problem of time-consuming, labor-intensive, and low-accuracy monitoring of goat motion behavior (lying, standing, walking, and running) while relying on the three-axis acceleration sensor and taking the acceleration data obtained from the goat back collection point as the research object, a method based on social learning (SL) is proposed using the Whale Optimization Algorithm (WOA) and XGBoost for goat motion behavior recognition. In this method, the XGBoost parameters are optimized by the WOA combined with social learning strategies to improve the classification and recognition accuracy. The results show that the recognition rate of lying behavior was as high as 97.14%, and the average recognition rate of the four movement behaviors was 94.42%, meeting the requirements of goat motion behavior recognition. Compared with the conventional XGBoost algorithm, the average recognition rate was increased by 3.41% and the recognition accuracy was improved. The results of this study can provide a reference for goat health assessment and intelligent disease warning.

A wearable fetal movement detection system for pregnant women.

A wearable device-based fetal movement detection system for pregnant women is proposed to resolve the problems of low accuracy of fetal movement detection by fetal heart monitor, difficulties of fetal movement monitoring by pregnant women in person, and inability to monitor for long periods of time by ultrasonic Doppler imaging device. The overall software design flow of the system is proposed after determining the overall structure of the system based on symmetric sensor. The application circuit of the three-axis acceleration sensor MC3672 and its supporting sensor data collection program are designed, and the application circuit of the main control chip NRF52840 with Cortex-M4 core is analyzed. The function of data collection and algorithm recognition result transfer to a smartphone is realized through the fetal movement recognition and algorithm design and Bluetooth communication design. Finally, the system test scheme is introduced, which involves performing functional tests on four healthy pregnant volunteers and analyzing the results. The experimental results show that the average recognition rate and correct rate of this system to recognize fetal movement is 89.74% when using the real fetal movement actively perceived by pregnant women as the standard, achieving a domestic and wearable design of fetal movement monitoring device for pregnant women that can be used to analyze and predict the fetal health condition.

A Detection Method for Vortex Precession Frequency Based on MEMS Three-Axis Acceleration Sensor

Swirlmeters are widely used in the wellhead metering of low permeability gas fields in China. However, the swirlmeter’s piezoelectric sensor has difficult distinguishing the vortex precession signal from fluid pulsation noise, pipeline mechanical vibration, and other interference. In this article, in order to detect the vortex precession frequency signal effectively and accurately from the interference noises, a signal detection method based on micro-electro-mechanical systems (MEMS) three-axis acceleration sensor was proposed. According to the difference between the force direction of vortex signal and interference signals, a dual-differential technique based on three-axis measurement is proposed to eliminate interference noises. The radiated interference of electromagnetic noise was eliminated by adding a notch circuit and radio frequency interference filter to the measuring circuit. Then, the sensitivity characteristics of vortex precession with different swirl angles were investigated by numerical simulation and the performance of the method was evaluated by experiments. The results of experiments show that the method proposed in this article can improve the sensor’s capability of anti-interference and widen the measurement range. Further wet gas experimental results show that the vortex precession frequency signal detected by MEMS-three-axis acceleration sensor (TAAS) does not change its linear output law in spite of the presence of liquid phase. Finally, a correction model of the instrument coefficient of the swirlmeter for wet gas measurement was established. The relative errors of the fitting model are within ±2.0%, the mean percentage error (MPE) is 0.17%, and the mean absolute percentage error (MAPE) is 0.81%. The method proposed in this article can improve the ratio of signal to noise of swirlmeter, overcome the influence of interference noise on the precession frequency, and broaden the gas flow measurement range. It realizes the efficient and accurate measurement of the gas flow rate for wet gas flow.

Studies of Acceleration of the Human Body during Overturning and Falling from a Height Protected by a Self-Locking Device.

The use of individual fall protection equipment is one of the most commonly applied methods of protecting workers whose worksites are located above the floor level. The safety of the user in such a situation depends on both the proper selection and correct use of such equipment. Additionally, aspects such as minimizing the free-fall distance before the fall arrest, as well as quick notification of an accident and efficient rescue operation, are important factors influencing safety. This paper presents a new testing method for fall arrest equipment using a test stand consisting of the Hybrid III 50th Pedestrian ATD anthropomorphic manikin and measuring set with three-axis acceleration transducers. The proposed method and test stand were developed for the design and testing of new fall protection devices equipped with electronic detection and alarm systems, for which it is necessary to determine acceleration limits in order to determine the alarm threshold. The proposed method is based on the measurement of accelerations that occur during tipping and falling from the height of an anthropomorphic manikin secured by a self-locking device. Two places of attachment of the measuring set with a three-axis acceleration sensor were analyzed at the waist belt of the manikin (abdomen and back). Moreover, the self-locking device lanyard was attached to the two points of the safety harnesses (the front and back point). The aim of the research was to check whether the acceleration values depend on the places of attachment of the measuring and anchored system, as well as to determine their maximum values. Acceleration values corresponding to fall arrest and tipping were analyzed. Limits of acceleration have been established in order to determine the threshold of alarm triggering. The non-parametric Mann–Whitney U test was used to check whether the location of the three-axis acceleration transducer and the position of the self-locking device lanyard attachment affect the value of the recorded acceleration. For results of acceleration measurements when testing the behavior of the manikin during fall arrest, no statistically significant differences were found. For results of acceleration measurements when testing the tipping behavior of the manikin, statistically significant differences occurred. This means that during fall arrest, the location of the three-axis acceleration transducer and the position of the self-locking device lanyard attachment do not matter. This work is a continuation of previous research on accelerations characterizing human body positions occurring during normal physical activities (ADL—activities of daily living).

Vibration signal collection and analysis of mechanical equipment failure based on computer simulation detection

Abstract This article addresses the challenge of large error rate and low accuracy of the vibration signal collection of mechanical equipment failure, and proposes a mechanical equipment failure vibration signal collection and analysis based on computer simulation detection. Then, it uses the Kalman filter algorithm for data filtering, according to the mathematical model established by the system, thus choosing a suitable noise covariance calculation method. In the integration process after filtering, using a piecewise integration method between acceleration peaks, the integration calculation is optimized to obtain the vibration displacement. The simulation results of this article show the vibration data collected by the main controller, after Kalman filtering and piecewise trapezoidal integration method optimization. The error of the proposed method is 0.5% when the frequency is 80 Hz, relative to the displacement measurement method of the three-axis acceleration sensor at 8.3%, and the error of data calculation results is greatly reduced. The greater the amplitude of vibration, the smaller the error. This method significantly improves the accuracy of vibration signal collection of mechanical equipment.

Experimental Investigation on Pedestrian Walking Load in Steel Footbridges

Accurate simulation of walking load is of great significance in conducting human-induced vibration analyses. However, accurate pedestrian walking load data obtained from long-span footbridges is scarce and data reliability depends on the sensor used for measurement. In the current work, Yanluo Footbridge with 102 m span was adopted as test site and Xnode high-precision acceleration wireless sensor was applied for measurements. An experimental investigation was performed on walking loads according to bipedal walking force model. In experimental studies, single-person and multi-person walking tests were performed at Yanluo Footbridge to measure corresponding stride frequency and dynamic load factor. The acceleration time-histories of walking pedestrians were accurately recorded using three-axis wireless acceleration sensor Xnode. Furthermore, the equation of dynamic load factor was derived by analyzing time-histories and power spectra and the design models of pedestrian walking load and crowd load were developed based on a great number of experimental data. Time histories of pedestrian walking loads showed regular periodic changes and dynamic load factor increased by increasing stride frequency. Using the walking load model developed in this work, the reliable structure response of human-induced vibration analysis can be obtained.

The Design of the Intelligent Medicine Box for the Elderly

With the increase in the number of elderly people in the world, the problem of population aging has gradually become prominent. People are paying more and more attention to the elderly, and medical supplies for the elderly have also attracted great attention. At present, various intelligent medical boxes have appeared in the domestic and foreign markets. These medicine boxes mainly include functions such as setting time, reminding to take medicine regularly, and SMS notification service. This project designed an intelligent medicine box, which is based on 80C51 single-chip microcomputer, loaded with infrared on-beam sensor, three-axis acceleration sensor, short message module, and Bluetooth module. The main functions of the system include intelligent detection module, timing reminder function, short message notification module and anti-fall detection module. On the one hand, the intelligent system can monitor and remind the elderly to take medications in time. On the other hand, it can also monitor the personal safety of the elderly alone at home. This is different from the current intelligent medical box on the market and is also the highlight of the system.

A study on effect of geomagnetic sensor correction in various enviroments

MEMS sensors have come to be used for various functions of smartphones and automatic driving system. The 9-axis motion sensor, which is one of the sensor modules, includes a three-axis gyro sensor, a three-axis acceleration sensor, and a three-axis geomagnetic sensor. The geomagnetic sensors which enable to measure the absolute direction are often used to estimate human posture. However, since the magnetic field is easily affected by the outside, it’s necessary to correct the sensor output. In this research, the geomagnetic sensor output is corrected using the gyro sensor output. The proposed method does not need complicated procedures. We verify the effectiveness of the proposed method even in environments where magnetic field disturbance generated, such as underground and high-rise buildings made of reinforced concrete. Furthermore, the knee joint angle during walking is estimated using the two 9-axis motion sensors by the proposed method.

Comfort evaluation and analysis of high-speed train

At present, the methods widely used to evaluate passenger comfort in train environment are ISO2631-1 and UTC-513R. In order to compare and analyze the application of the two standards in the actual line conditions, three-axis acceleration sensor is installed on CRH3 to collect the vibration data of the vehicle. All the ride comfort is calculated according to the standards of ISO2631-1 and UIC-513R, and mapped to the map line. The results are helpful to further improve the accuracy of comfort evaluation of high-speed trains.

Design and Implementation of Intelligent Monitoring Collar for Potential Patients with Viral Pneumonia Based on DA14699 Chip

In order to improve the level of epidemic prevention and control, and strengthen the observation and monitoring of potential patients with viral pneumonia in isolated state, a medical intelligent monitoring collar based on DA14699 Bluetooth low-power chip was proposed. DA14699 chip is used as the main controller in the design scheme, and the temperature, cough and location information of potential patients are recorded and analyzed by its high-efficiency wireless multi-core processing ability. The LIS3DH three-axis acceleration sensor is used to judge the cough symptoms. The MLX90640 infrared sensor is used to continuously measure the body temperature. The L218 four frequency GSM / GPRS positioning module is used to complete the rapid and accurate positioning of personnel, so as to realize the comprehensive supervision of the implementation of home isolation measures. DA14699 chip supports Bluetooth BLE5.1 protocol. Epidemic prevention personnel can transmit and read the data recorded in the smart collar from a long distance, and display it directly on the intelligent Bluetooth handheld terminal, effectively avoid the risk of infection caused by close contact. Through the actual test, the monitoring function of the key parameters of the collar is reliable and has high application value.

Intelligent Tire Sensor-Based Real-Time Road Surface Classification Using an Artificial Neural Network.

Vehicles today have many advanced driver assistance control systems that improve vehicle safety and comfort. With the development of more sophisticated vehicle electronic control and autonomous driving technology, the need and effort to estimate road surface conditions is increasing. In this paper, a real-time road surface classification algorithm, based on a deep neural network, is developed using a database collected through an intelligent tire sensor system with a three-axis accelerometer installed inside the tire. Two representative types of network, fully connected neural network (FCNN) and convolutional neural network (CNN), are learned with each of the three-axis acceleration sensor signals, and their performances were compared to obtain an optimal learning network result. The learning results show that the road surface type can be classified in real-time with sufficient accuracy when the longitudinal and vertical axis acceleration signals are trained with the CNN. In order to improve classification accuracy, a CNN with multiple input that can simultaneously learn 2-axis or 3-axis acceleration signals is suggested. In addition, by analyzing how the accuracy of the network is affected by number of classes and length of input data, which is related to delay of classification, the appropriate network can be selected according to the application. The proposed real-time road surface classification algorithm is expected to be utilized with various vehicle electronic control systems and makes a contribution to improving vehicle performance.

Research on intelligent perception and human activity monitoring for people with inconvenient movement

Aiming at the problems of various sensors, complex recognition algorithm poor implement ability and real-time performance in current human activity state recognition methods, a human activity monitor based on single three-axis acceleration sensor is designed. By collecting acceleration data of human waist, using sliding time window method to extract time domain features, four active states are identified: long-term violent active state. Long-term static state, fall state and normal active state. The technical development and the existing difficulties and problems are discussed for future related research.

Design and Implementation of Wireless Identification and Sensing Platform in Structure Monitoring

In the process of structure monitoring, active wireless sensor devices are often used. In order to replace the energy limitation bottleneck of the power supply unit, the Wireless Identification and Sensing Platform (WISP) with real-time data transmission function is adopted to realize the collection of structural vibration acceleration data. Firstly, the various functional modules of the WISP with three-axis acceleration are introduced. Then, through software programming of the low-power MSPF430F2132 microprocessor on it, the drive and control of the external three-axis acceleration sensor ADXL330 is realized. Finally, it was tested through comparative experiments. The research results show that the acceleration data monitored by the WISP is accurate, effective and reliable. Compared with the active wireless Narada unit, the similarity is more than 83%. Compared with the OpenSEES system, the accuracy of the structure frequency monitored by the WISP is 99.07%. Therefore, it is feasible and reliable to use the WISPs to perform multi-point deployment control in structural dynamic monitoring.

Heterogeneous Sensor Data Fusion for Human Falling Detection

With the continuous improvement of human living standards, population aging has become a global development trend. At present, China has entered an aging society, the health and safety of the elderly have become the focus of social concern. Due to the aging of physiological structure and the decline of physical function, the probability and frequency of accidental falls in the elderly are very high. Under the above background, the purpose of this study is based on a heterogeneous sensor data fusion algorithm in an intelligent wearable sensor network. This article proposes a heterogeneous sensor data fusion algorithm based on wearable wireless body area network technology, and constructs a high-precision and stable wearable elderly activities of daily living (ADLs) and fall monitoring system. We first select the three-axis acceleration sensor, three-axis magnetic sensor, and three-axis angular velocity sensor to monitor the activities of the elderly. Then, we use Bluetooth to transmit the data collected by heterogeneous sensors to smartphones, and communicate with service centers and users through the mobile phone communication network, Family members interact to form a wireless city network based on wearable technology. Our proposed data fusion approach is based on the Kalman filter algorithm, which can reduce the system noise and improve the stability of the system. The experimental results demonstrate that the fall detection system proposed and implemented in this study can well detect accidental falls in the daily activities of the elderly, the sensitivity and specificity of the fall detection system are 98.7% and 98.5% respectively. The study in this article has a high research value and practical application significance in protecting the healthy life of the elderly.

Wearable Coaching System

Wearable Coaching System is a multi-sensor device for real-time training coaching. The system comprises of six modules which are shoulder, waist, a pair of wrist and a pair of ankle modules. It is compact, light, comfortable to wear and is suitable for any size of user. It system has five main embedded sensors: heart rate, three-axis acceleration, gyro, temperature and blood oxygen saturation (SpO2) sensor. It operates in streaming mode for real-time data processing using two telecommunication tools: ZigBee and Bluetooth and low energy interface for recording mode is using an internal flash memory. In this paper, a wearable coaching system is designed to help athlete or trainer to perform proper and efficient exercise suitable to one's body needs. It measures the parameter of the body, analyze and provide feedback in a form of an advice. In order to improve the system, arm gesture mode performed by the wrist modules of the system is applied. This mode is to enable user to manage their exercising level to a more appropriate pace, suitable with their individual body by reproducing the feedback. In a supervised coaching training, percentage of heart rate threshold target is increasing, hence, producing a greater performance of training. It is proven in this paper, that wearable coaching system performs an accurate measurement in enhancing sports and coaching training, adding value to novel lifestyle exercising and health monitoring.

Improvement of structure of frequency-change-type three-axis acceleration sensor

In the frequency-change-type three-axis acceleration sensor developed so far, the frequency-change rate in the z-axis direction of its thickness direction was considerably smaller than the change rates in the x- and y-axis directions of its plane direction. As a method for increasing the small change rate, a sensory structure was proposed in which the mass was sandwiched between two right-angled vibrators. However, there was a problem that the structure became three-dimensional and complicated. Therefore, simplification of the structure has been considered desirable. Here, to simplify the structure and increase the sensitivity, an improved new frequency-change-type three-axis acceleration sensor is proposed in which all of the vibrators are arranged only on the upper surface of the mass. The sensor characteristics were analyzed by the finite element method, and verified experimentally.