Acceleration Sensor Of Smartphone Research Articles

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks.

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical examinations to check their health status. However, most of the time, it is difficult to reach a specific medical environment and use special medical equipment. In this paper, a deep learning framework based on the bidirectional gated recurrent unit for health status recognition is implemented to improve the accuracy by making full use of the information provided by smartphone acceleration sensors. A model based on a bidirectional gated recurrent unit is constructed to describe the relationship between input acceleration signals and output information through a gating approach. Therefore, it can automatically detect the health status of the sportsman as healthy, subhealthy, and unhealthy. Finally, the practical data collected from an athlete have been used to evaluate the recognition performance of the system. Results show that the proposed methodology can predicate the sports health status accurately.

The Atwood machine experiment assisted by smartphone acceleration sensor for enhancing classical mechanics experiments

Newton’s second law can be demonstrated by classical mechanical experiments, one of which is using the Atwood machine. Atwood machines consist of two weights suspended by a rope and connected by a pulley. In this study, an Atwood machine experiment was carried out, where the smartphone was placed on one of the loads, so that the vertical acceleration of the motion system could be measured easily using an acceleration sensor. We also consider the effect of pulley rotation which is usually missed from previous studies. By deriving the second law equation of Newton on rotational and translational motion, we can get the relationship between the system acceleration and the gravitational acceleration of the earth. The results of the earth’s gravitational acceleration obtained, compared with the results of the experiment using a stopwatch (conventional method), and with the value of the standard earth gravitational acceleration at that place. Earth’s gravitational acceleration from the experiment using a smartphone is closer to the standard earth gravitational acceleration value than using a stopwatch. So, in this experiment, the use of a smartphone acceleration sensor contributes to enhance a conventional demonstration of classical mechanics experiments.

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor.

In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the k-nearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and low-cost road maintenance.

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone.

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals' physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances.

Comparison between the use of acceleration sensor and video tracker on smartphone for spring oscillation experiment

Nowadays, mechanical experiments such as spring oscillations for high schools and colleges have been developed using smartphones. This study aims to compare the use of acceleration sensors and video tracker available on smartphones for free and damped spring oscillation experiments. This experiment used varying load masses. In experiments using acceleration sensors, acceleration versus time data were obtained, while experiments using a video tracker got position versus time data. The data were then calculated to obtain the spring constant value and the damping coefficient are obtained. The experimental results showed that for experiments using the acceleration sensor, the spring constant values k = (11.8 ± 0.2) N m−1. For experiments using a video tracker the spring constant values k = (12.1 ± 0.4) N m−1. Experiments using the acceleration sensor can explain the acceleration trends that change periodically whereas when we use a video tracker it explains the trends in positions that change periodically. But, based on the standard deviation, showing that experiments using acceleration sensors obtain more precise results. We hope that mechanical experiment using smartphone acceleration sensors can be applied in physics laboratories on high schools and colleges, because that is easy, inexpensive, and also the results are more precise.

Gait Recognition of Acceleration Sensor for Smart Phone Based on Multiple Classifier Fusion

Gait, as a kind of biological feature, has a profound value in personnel identification. This paper analyzes gait characteristics based on acceleration sensors of smart phones and proposes a new gait recognition method. First, in view of the existing methods in the process of extraction of gait features, a large number of redundant calculations, cycle detection error, and the phase deviation issue during the week put forward the Shape Context (SC) and Linear Time Normalized (LTN) combining SCLTN calibration method of gait cycle sequence matching, to represent the whole extract typical gait cycle gait. In view of the existing extracted gait features are still some conventional features; the velocity change of relatively uniform acceleration and the change of acceleration per unit time are proposed as new features. Secondly, combining new features with traditional features to form a new feature is set for training alternative feature set, from which the training time and recognition effect of multiple classifiers are screened. Finally, a new multiclassifier fusion method, Multiple Scale Voting (MSV), is proposed to fuse the results of Multiple classifiers to obtain the final classification results. In order to verify the performance of the proposed method, gait data of 32 testers are collected. The final experimental results show that the new feature has good separability, and the recognition rate of fusion feature set after MSV algorithm is 98.42%.

A real-time fall detection system based on the acceleration sensor of smartphone

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone...

User's static and dynamic posture determination method using smartphone acceleration sensor

User's static and dynamic posture determination method using smartphone acceleration sensor

A Real-time Fall Detection System Based on the Acceleration Sensor of Smartphone

Falls are a major problem for the elderly people leading to injury, disability, and even death. In this article, we propose a fall detection system that uses the acceleration sensor of a smartphone...

A driving behavior detection system based on a smartphone's built‐in sensor

SummaryTraffic accidents resulting from driving behavior and road conditions are crucial problems for drivers. The causes and responses to traffic accidents have been widely studied by researchers. Whereas several approaches have been proposed to ease these problems, most works entail high computational costs or rigid hardware conditions. To address these challenges, we propose Health Driving, a smartphone‐based system for detecting driving events and road conditions solely with a built‐in smartphone acceleration sensor. More specifically, we first collect acceleration data from the acceleration sensor of a smartphone on a vehicle, and then utilize an acceleration reorientation calibration algorithm to convert the obtained acceleration data from the smartphone to acceleration data of the vehicle. Finally, we exploit Health Driving to detect driving events and road conditions, and evaluate the seriousness of the road conditions and driving events by using an efficient scoring mechanism based on the ISO 2631 standard. An extensive evaluation demonstrates that Health Driving operates successfully with an ordinary smartphone, and operates with a low computational cost compared with other methods. Copyright © 2016 John Wiley & Sons, Ltd.

스마트폰의 가속도 센서를 이용한 정적균형능력 측정의 신뢰도 연구

스마트폰의 가속도 센서를 이용한 정적균형능력 측정의 신뢰도 연구

The study of two-dimensional oscillations using a smartphone acceleration sensor: example of Lissajous curves

A smartphone acceleration sensor is used to study two-dimensional harmonic oscillations. The data recorded by the free android application, Accelerometer Toy, is used to determine the periods of oscillation by graphical analysis. Different patterns of the Lissajous curves resulting from the superposition of harmonic motions are illustrated for three experiments. This work introduces an example of how two-dimensional oscillations can be easily studied with a smartphone acceleration sensor.

스마트폰 센서 기반 상황인식 시스템 연구 및 설계

This paper describes the design and implementation of situation recognition system with smart phone sensors, which recognizes the dangerous situation at anytime, anywhere through intuitive data analysis of the combination of the sensor. The implemented system consists of wearable heart rate sensor and acceleration sensor of smart phone instead of existing sensor that is attached to the body. It is also designed to get more effective results of recognition about the dangerous situation using merged displacement values of acceleration sensor and heart rate sensor which are measured in the process of recognizing dangerous situations. This research, in accordance with the wide penetration of smartphones, achieves the fast status determination through the combination of an acceleration sensor and a heart rate sensor applied to its own status perception algorithm for anyone who needs the stable perception of risk without the need for a separate provision of the sensor.

Using a smartphone acceleration sensor to study uniform and uniformly accelerated circular motions

The acceleration sensor of a smartphone is used for the study of the uniform and uniformly accelerated circular motions in two experiments. Data collected from both experiments are used for obtaining the angular velocity and the angular acceleration, respectively. Results obtained with the acceleration sensor are in good agreement with alternative methods, like using video recordings of both experiments and a physical model of the second experiment.

The spinning disc: studying radial acceleration and its damping process with smartphone acceleration sensors

Here, we show the possibility of analysing circular motion and acceleration using the acceleration sensors of smartphones. For instance, the known linear dependence of the radial acceleration on the distance to the centre (a constant angular frequency) can be shown using multiple smartphones attached to a revolving disc. As a second example, the decrease of the radial acceleration and the rotation frequency due to friction can be measured and fitted with a quadratic function, in accordance with theory. Finally, because the disc is not set up exactly horizontal, each smartphone measures a component of the gravitational acceleration that adds to the radial acceleration during one half of the period and subtracts from the radial acceleration during the other half. Hence, every graph shows a small modulation, which can be used to determine the rotation frequency, thus converting a ‘nuisance effect’ into a source of useful information, making additional measurements with stopwatches or the like unnecessary.

Analyzing collision processes with the smartphone acceleration sensor

It has been illustrated several times how the built-in acceleration sensors of smartphones can be used gainfully for quantitative experiments in school and university settings (see the overview in Ref. 1). The physical issues in that case are manifold and apply, for example, to free fall,2 radial acceleration,3 several pendula,4,5 or the exploitation of everyday contexts.6 This paper supplements these applications and presents an experiment to study elastic and inelastic collisions. In addition to the masses of the two impact partners, their velocities before and after the collision are of importance, and these velocities can be determined by numerical integration of the measured acceleration profile.

Analyzing elevator oscillation with the smartphone acceleration sensors

It has often been reported in this column that smartphones are very suitable tools for exploring the physical properties of everyday phenomena. A very good example of this is an elevator ride. In addition to the acceleration processes, oscillations of the cabin are interesting. The present work responds to the second aspect.

A quantitative analysis of coupled oscillations using mobile accelerometer sensors

In this paper, smartphone acceleration sensors were used to perform a quantitative analysis of mechanical coupled oscillations. Symmetric and asymmetric normal modes were studied separately in the first two experiments. In the third, a coupled oscillation was studied as a combination of the normal modes. Results indicate that acceleration sensors of smartphones, which are very familiar to students, represent valuable measurement instruments for introductory and first-year physics courses.

Analyzing radial acceleration with a smartphone acceleration sensor

This paper continues the sequence of experiments using the acceleration sensor of smartphones (for description of the function and the use of the acceleration sensor, see Ref. 1) within this column, in this case for analyzing the radial acceleration.

Analyzing spring pendulum phenomena with a smart-phone acceleration sensor

This paper describes two further pendulum experiments using the acceleration sensor of a smartphone in this column (for earlier contributions concerning this topic, including the description of the operation and use of the acceleration sensor, see Refs. 1 and 2). In this paper we focus on analyzing spring pendulum phenomena. Therefore two spring pendulum experiments will be described in which a smartphone is used as a pendulum body and SPARKvue3 software is used in conjunction with an iPhone or an iPod touch, or the Accelogger4 app for an Android device.1,2 As described in Ref. 1, the values measured by the smartphone are subsequently exported to a spreadsheet application (e.g., MS Excel) for analysis.