3-axis Acceleration Sensor Research Articles

Research on the remote monitoring system for falls in the elderly based on the internet of things and six axis acceleration sensors

With the integration and collision of the Internet of Things, machine learning, Big data computing and other technologies, related applications of the Internet of Things also need to process a lot of real-time data streams. This article focuses on the research of remote monitoring of falls using the Internet of Things and six axis acceleration sensors, and explores the efficient application of Internet of Things technology in the system. The hardware design and related software development of remote monitoring of falls for the elderly are the key parts, and the overall framework, main modules, and specific implementation of the system are elaborated in detail. A complete remote monitoring system is designed by selecting a suitable six-axis acceleration sensor, collecting and analyzing the data. The continuous development of the Internet of Things and six axis acceleration sensor technology can provide real-time intelligent remote monitoring. Compared to cloud computing platforms, edge clusters have limited computing and storage resources and diverse types of computing node architectures. Therefore, it is necessary to use lightweight application service deployment methods to build an efficient and autonomous data processing platform. Through research and innovation on the remote monitoring system for elderly falls, with optimized and comprehensive technology and detailed research support, the overall system design was experimentally debugged and the experimental plan was ultimately determined. Through data communication module, fall detection and diagnosis module, and database management module, rapid analysis of remote acceleration data and information exchange are achieved, thereby minimizing the possibility of accidents caused by falls in the elderly.

Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data

HAR technology uses computer and machine vision to analyze human activity and gestures by processing sensor data. The 3-axis acceleration and gyro sensor data are particularly effective in measuring human activity as they can calculate movement speed, direction, and angle. Our paper emphasizes the importance of developing a method to expand the recognition range of human activity due to the many types of activities and similar movements that can result in misrecognition. The proposed method uses 3-axis acceleration and gyro sensor data to visually define human activity patterns and improve recognition accuracy, particularly for similar activities. The method involves converting the sensor data into an image format, removing noise using time series features, generating visual patterns of waveforms, and standardizing geometric patterns. The resulting data (1D, 2D, and 3D) can simultaneously process each type by extracting pattern features using parallel convolution layers and performing classification by applying two fully connected layers in parallel to the merged data from the output data of three convolution layers. The proposed neural network model achieved 98.1% accuracy and recognized 18 types of activities, three times more than previous studies, with a shallower layer structure due to the enhanced input data features.

Detection of Physical Frailty Status of Middle-Aged and Elderly People Using Gait Features Based on 3-Axis Acceleration Sensors

In Japan's super-aged society, it is important to prolong independent daily living. This paper, we focus on walking, which is indispensable for daily living, to diagnose frailty. This study included acceleration data during limb-loaded walking to increase muscle strength in the analysis. Also, we classified and identified whether middle-aged and elderly persons are in a frail state or not were conducted following the Cardiovascular Health Study. After selecting useful features by random forest, a Support Vector Machine was used to identify the frailty state. As a result, we reported that we could identify frailty with an accuracy rate of 82%, suggesting the possibility of detecting low grip strength from the features extracted from gait data.

A system to monitor one’s nearsightedness implicitly

The number of nearsighted young people grows rapidly because of over-use of eyes, and it becomes a wide health problem in the world. Efficient vision monitoring methods are required to determine whether people are getting nearsightedness problem in the early stages so as to take further treatment. Current technologies for vision examination are usually either expensive or time costly. This paper proposes a nearsightedness monitoring system, which exploits the widely used smartphones to detect the deterioration of nearsightedness by monitoring and analyzing the the distance between the eyes and the smartphone screen. The detection process is implicit since the system is implemented on the daily used phones and people do not have to be interrupted frequently. The proposed system consists of two key components: activity recognition component and the eye detecting component. The activity recognition component is used to determine whether a person is watching the phone. Once a person is watching the phone, the eye detecting component can be triggered to take a picture using the front camera and localize the two eyes. The distance between eyes and the screen is estimated by the ratio of the two-eye distance in the picture and the width of the picture. This paper uses the 3-axis acceleration sensor and the front camera which are equipped on most of the smartphones for activity recognition and eye detection respectively. A prototype has been developed in order to evaluate the effectiveness of the system under various environmental conditions. From more than half year monitoring period which consisted of about 20 volunteers, we were able to accurately detect the degradation of nearsightedness in two volunteers.

Research of a method of respiratory examination using a 3-axis accelerator and a 3D sensor

Respiratory diseases include restrictive diseases such as pulmonary fibrosis, obstructive diseases such as COPD (Chronic Obstructive Pulmonary Disease), and mixed diseases.All of which are serious and require early diagnosis. Lung function tests for respiratory diseases are performed using Spirometry and X-ray CT in the Hospital. We have been studying to construct a non-invasive and simple pulmonary function diagnosis system. In this paper we report on a method of measuring ventilation volume using a three axis acceleration sensor and a three dimension depth sensor.

Development of a Knowledge Discovery Computing based wearable ECG monitoring system

ECG signals contain a lot of information related to cardiac activity and play a critical role in diagnosing of heart disease. However, relying on health information monitoring with simple ECG measurement can lead to errors in health information analysis. Therefore, for accurate diagnosis and analysis, an algorithm that can efficiently process user’s measurement environment and activity information is required. In this paper, we propose a wearable ECG monitoring system based on Knowledge Discovery Computing using 3-axis acceleration sensor. The proposed system measures real-time cardiac information and activity information simultaneously to minimize errors in health information analysis through Knowledge Discovery Computing between the user’s environment information and abnormal ECG according to the measurement environment in everyday life. In addition, we implemented a packet transmission protocol to effectively transmit large amounts of data analyzed through Knowledge Discovery Computing to the base station. First, arrhythmia detection was performed using R-peak detection preprocessing and pattern matching algorithm. Also, a classification algorithm was implemented to classify activity types by utilizing an accelerometer in order to recognize the context surrounding the user. Information on the user’s vital signs and activity information can be used for more accurately determine arrhythmia in daily life. Also, variable packet generation protocol was designed for an effective transmission of data packets increased exponentially by long-term measurements and wireless data transfer. The variable packet generation protocol is efficient in limited wireless network environments, because it generate packets of the entire data only with case of abnormal cardiac rhythm and transmits minimal information for normal cardiac rhythm. In order to evaluate the performance of ECG monitoring system based on Knowledge Discovery Computing, we designed a 2-lead ECG measurement belt manufactured with conductive fiber to minimize user discomfort, and assessed the system performance in data packet transmission, data recovery, and arrhythmia detection in dynamic states in daily life. In static states, the posture detection was 100%, heart rate detection 99.8%, and CR (Compression Ratio) was 193.99 and correlation coefficient of 0.95 with commercial systems. In dynamic states, 96% detection rate and 59.14 of CR is identified. If arrhythmia is determined based only on ECG signals, it is difficult to differentiate an actual abnormal cardiac rhythm from an ECG signal altered due to motion. The experiments conducted in this study confirmed that Knowledge Discovery is possible in the dynamic state through the proposed system in daily life.

Vibration Monitoring of Daniel Maclise’s Wall Painting Trafalgar

ABSTRACT Vibration generated by use of masonry equipment can be a threat to wall paintings. Its assessment, mitigation and control is of critical importance to risk management and the safeguarding of works of art, particularly those which are immovable. The planned programme of window restoration in the Royal Gallery, Palace of Westminster, started in 2016, directly above the monumental wall painting of Trafalgar, had the potential to cause serious damage to this painting. For a period of 18 months the Curator’s Office worked with stakeholders, conservation scientists, conservators and stone masons to minimise risk. The project developed a monitoring strategy, set vibration thresholds, managed risk and negotiated the use of tools and working methods. To assess vibration data loggers equipped with 3-axis acceleration sensors were installed at critical locations. To corroborate these findings assessment was undertaken using a sound level analyser. This assessed acceleration at a series of frequencies in the 0–100 Hz range considered most damaging to artworks. However, a critical component was conservator-led in situ monitoring of vibration. When necessary, this live monitoring allowed conservators to dynamically assess and negotiate the adaptation of tools and techniques to reduce levels. This real-time approach proved essential in understanding peaks and changes in levels of vibration; informing discussion between stakeholders, ensuring monitoring translated into effective preservation.

Design, fabrication, and characterization of SU-8/carbon black nanocomposite based polymer MEMS acceleration sensor

Design, fabrication, and characterization of SU-8/carbon black (CB) nanocomposite based single axis acceleration sensor is presented. The entire sensor made of SU-8 photosensitive polymer is fabricated using the process spin pattern anchor release device. Thanks to the low young’s modulus of SU-8 polymer, the cantilever beams used to support the proof mass are very flexible, which resulted in increased sensitivity. A composite layer of CB nanoparticles mixed with the SU-8 polymer is sandwiched between two SU-8 2002 layers in the cantilever beam. This nanocomposite layer acts as a piezoresistive layer and is used to measure the induced stress and strain. The piezoresistors are patterned in a full bridge Wheatstone arrangement to cancel the effect on temperature variation. Maximum stress locations are identified through Finite Element -Modeling based simulations under accelerations up to 100 g. The natural frequency, stiffness, and sensitivity of the acceleration sensor are characterized using the techniques of nanoindenter and Laser Doppler Vibrometer. The measured natural frequency is 1.884 kHz. The relative change in resistance per unit strain of the prototype is measured using I–V measurements, which gives a gauge factor of 13.05.

Research on heart rate extraction algorithm in motion state based on normalized least mean square combining ensemble empirical mode decomposition

In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.

Experimental study on the sEMG of "joint angle effect" of human muscle strength—Taking biceps brachii as an example

In the experiment, the author used wave plus wireless surface electromyography system (SEMs + 3-axis acceleration sensor) made in Italy and wave wireless EMG software system, high-definition high-speed camera and human joint angle measuring instrument. Taking human biceps brachii as an example, the static and dynamic isometric contraction of biceps brachii was completed surface electromyography. In the experiment, the surface electromyography of biceps brachii was measured at 30°, 60°, 90°, 120°, 150°, 180° and the surface electromyography of biceps brachii was measured at the same time when the biceps brachii was not loaded or when the biceps brachii was loaded. Secondly, the surface electromyography of biceps brachii was measured at the same time when the biceps brachii completed the whole process of flexion and extension of the elbow (centripetal and centrifugal). Finally, the paper combined with HD The effect of joint angle on the contraction of biceps brachii muscle was analyzed by camera technique. The results show that the static contraction force of biceps brachii is different when the elbow joint is at different angles; in addition, when the dynamic contraction, the contraction force of biceps brachii is inversely proportional to the angle of elbow joint.

Fall detection system with 3-axis accelerometer

In this work, the designed and developed process of a fall detection system using a NodeMCU microcontroller is described. A 3-axis acceleration sensor is used to obtain the data of the movement of humans and changes in acceleration are used to determine trends in falling. The ESP8266 microcontroller is used to the receive the data in real time and alert the family through the LINE application. Experimental results show that the system is capable of precisely producing alarms for falling actions and has a high detection precision and effectiveness.

Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone

Bicycle Behavior Recognition Using 3-Axis Acceleration Sensor and 3-Axis Gyro Sensor Equipped with Smartphone

Non-invasive measurement of floating–sinking motion of a large object in a gas–solid fluidized bed

A Lagrangian sensor system has been established to non-invasively measure both the vertical position and dynamic force acting on itself. It consists of a 3-axis acceleration sensor, a 3-axis magnetometer, a microcontroller, a wireless module, batteries, and external electromagnetic coils. In this study, we applied the system to a free-moving coarse object in a gas–solid fluidized bed. The floating and sinking motions of the object in the fluidized bed are essentially caused by differences between its density and the apparent density of the fluidized media. However, the object sometimes shows strange behavior under the influence of variance in the fluidization state. We measured the temporal change of the upward force acting on the object as well as the vertical position, which is invisible from the outside. The experimental results indicate that the force acting on the object differs significantly between the floating and sinking states and is greatly complicated by interference with rising bubbles in the fluidized bed. The probability density of the vertical position of the object shows that its motion is explained not only by hydrostatic effects, but also by inhomogeneity of the fluidization state in the bed.

Unconstrained detection of ballistocardiogram and heart rate based on vibration acceleration

The requirement for unconstrained monitoring of heartbeat during sleep is increasing, but the current detection devices can not meet the requirements of convenience and accuracy. This study designed an unconstrained ballistocardiogram (BCG) detection system using acceleration sensor and developed a heart rate extraction algorithm. BCG is a directional signal which is stronger and less affected by respiratory movements along spine direction than in other directions. In order to measure the BCG signal along spine direction during sleep, a 3-axis acceleration sensor was fixed on the bed to collect the vibration signals caused by heartbeat. An approximate frequency range was firstly assumed by frequency analysis to the BCG signals and segmental filtering was conducted to the original vibration signals within the frequency range. Secondly, to identify the true BCG waveform, the accurate frequency band was obtained by comparison with the theoretical waveform. The J waves were detected by BCG energy waveform and an adaptive threshold method was proposed to extract heart rates by using the information of both amplitude and period. The accuracy and robustness of the BCG detection system proposed and the algorithm developed in this study were confirmed by comparison with electrocardiogram (ECG). The test results of 30 subjects showed a high average accuracy of 99.21% to demonstrate the feasibility of the unconstrained BCG detection method based on vibration acceleration.

An analysis on the sensibility of casing vibration signal and its application to aero-hydraulic pump

Aero-hydraulic pump is a central part of hydraulic system in an aircraft. Acceleration sensors are installed in the axis, tangential and vertical direction for identifying the weak imbalance fault, and meanwhile analysis is made for the sensibility of weak imbalance fault from different direction acceleration signal. The result shows that the signal from vertical acceleration sensor is the most sensitive and the one from axis acceleration sensor is the least sensitive to identify and diagnose weak imbalance fault of aero-hydraulic pump.

Scaling Relationship of In Vivo Muscle Contraction Strength of Rabbits Exposed to High-Frequency Nanosecond Pulse Bursts.

We studied the influence of various parameters of high-frequency nanosecond pulse bursts on the strength of rabbit muscle contractions. Ten unipolar high-frequency pulse bursts with various field intensities E (1 kV/cm, 4 kV/cm, and 8 kV/cm), intraburst frequencies f (10 kHz, 100 kHz, and 1 MHz), and intraburst pulse numbers N (1, 10, and 100) were applied using a pair of plate electrodes to the surface skin of the rabbits’ biceps femoris, and the acceleration signal of muscle contraction near the electrode was measured using a 3-axis acceleration sensor. A time- and frequency-domain analysis of the acceleration signals showed that the peak value of the signal increases with the increasing strength of the pulse burst and that the frequency spectra of the signals measured under various pulse bursts have characteristic frequencies (at approximately 2 Hz, 32 Hz, 45 Hz, and 55 Hz). Furthermore, we processed the data through multivariate nonlinear regression analysis and variance analysis and determined that the peak value of the signal scales with the logarithm to the base 10 of ENx, where x is a value that scales with the logarithm to the base 10 of intraburst frequency (f). These results indicate that for high-frequency nanosecond pulse treatment of solid tumors in or near muscles, when the field strength is relatively high, the intraburst frequency and the intraburst pulse number require appropriate selection to limit the strength of muscle contraction as much as possible.

Examination of Single-Hand-Operation Wheelchair driving using EMG and 3-axis acceleration sensor

Examination of Single-Hand-Operation Wheelchair driving using EMG and 3-axis acceleration sensor

Design of the wearable device for hemiplegic gait detection using an accelerometer and a gyroscope.

Hemiplegic walking is the main symptom of hemiplegia and is the basis for judging the outcome or severity of hemiplegia. In this study, we developed a wearable measurement module for measuring the inertia signal generated during walking and evaluated the possibility of gait measurement as a basic study of the new hemiplegia diagnosis technology using wearable device. The developed measurement module is worn on the waist. It is equipped with 3-axis acceleration sensor and 3-axis angular velocity sensor and has the function to transmit and record data through wireless communication. As a result of measuring the gait signals using the developed measurement module, specific patterns are shown for each axis according to each step, and it is confirmed that gait signals can be distinguished intuitively.

Studying rotational dynamics with a smartphone—accelerometer versus gyroscope

The wide-spread availability of smartphones makes them a valuable addition to the measurement equipment of both the physics classroom and the instructional physics laboratory, encouraging an active interaction between measurements and modeling activities. Two useful sensors, available in most modern smartphones and tablets, are the 3-axis acceleration sensor and the 3-axis gyroscope. We explore the strengths and weaknesses of each type of sensor and use them to study the rotational dynamics of objects rotating about a fixed axis. Care has to be taken when interpreting acceleration sensor data, and in some cases the gyroscope will allow for rotational measurements not easily replicated using the acceleration sensor.

Acceleration sensor placement technique for vibration test in structural health monitoring using microhabitat frog-leaping algorithm

In this article, a microhabitat frog-leaping algorithm is proposed based on original shuffled frog-leaping algorithm and effective independence method to make the algorithm more efficient to optimize the 3-axis acceleration sensor configuration in the vibration test of structural health monitoring. Optimal sensor placement is a vital component of vibration test in structural health monitoring technique. Acceleration sensors should be placed such that all of the important information is collected. The resulting sensor configuration should be optimal such that the testing resources are saved. In addition, sensor configuration should be calculated automatically to facilitate engineers. However, most of the previous methods focus on the sensor placement of 1-axis sensors. Then, the 3-axis acceleration sensors are calculated by the method of 1-axis sensors, which results in non-optimal placement of many 3-axis acceleration sensors. Moreover, the calculation precisions and efficiencies of most of the previous methods cannot meet the requirement of practical engineering. In this work, the microhabitat frog-leaping algorithm is proposed to solve the optimal sensor placement problems of 3-axis acceleration sensors. The computation precision and efficiency are improved by microhabitat frog-leaping algorithm. Finally, microhabitat frog-leaping algorithm is applied and compared with other algorithms using Dalian South Bay Cross-sea Bridge.