• Physical Therapy Korea
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• v.25 no.3
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• pp.27-38
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• 2018

Background: At present time, smartphones have become very popular and powerful devices, and smartphone applications with the good validity have been designed to assess human balance ability. Objects: The purpose of this study is to evaluate the feasibility of smartphone acceleration in the assessment of postural control ability for six different conditions. Methods: Twenty healthy college-aged individuals volunteered. Static balance ability was measured twice with one-day interval using smartphone application and 3D motion capture system under the six different conditions. Results: Dominant frequencies for each test condition did not show significant differences except for two conditions. The intra-rater correlation coefficient between the first and second tests showed high correlations in six conditions(r>.70, p<.05). Smartphone acceleration and the acceleration calculated from the 3D marker position data showed high correlation coefficient(r>.80, p<.001). Conclusion: Acceleration recorded from a smartphone could be useful assessment variables for balance test in the clinical field.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.54-62
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• 2017

Currently, falling to industrial field workers is causing serious injuries. Therefore, many researchers are actively studying the fall by using acceleration sensor, gyro sensor, pressure sensor and image information.Also, as the spread of smartphones becomes common, techniques for determining the fall by using an acceleration sensor built in a smartphone are being studied. The proposed method has complexity due to fusion of various sensor data and it is still insufficient to develop practical application. Therefore, in this paper, we use acceleration sensor module built in smartphone to collect acceleration data, propose a simple falling algorithm based on accelerometer sensor data after normalization and preprocessing, and implement an Android based app.

• International Journal of Advanced Culture Technology
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• v.5 no.2
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• pp.63-73
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• 2017

In this paper, we propose algorithm for determining the static and dynamic posture using the acceleration sensor of smartphone. The measured acceleration values are then analyzed according to a preprocessing to the respective axis (X, Y, Z) and posture (standing, sitting, lying) presents static posture determination criterion. The proposed static posture determination condition is used for static posture determination and dynamic posture determination. The dynamic posture is determined by using regression linear equations. In addition, transition state can be grasped by SVM change in dynamic posture determination. Experimental results are presented using data and app. Experiments were performed using data collected from 10 adults.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1165-1171
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• 2022

Human behavior recognition using an accelerometer has been applied to various fields. As smartphones have become used commonly, a method for human behavior recognition using the acceleration sensor built into the smartphone is being studied. In the case of the elderly, falling often leads to serious injuries, and falls are one of the major causes of accidents at construction fields. In this article, we proposed recognition method for human falling direction using built-in acceleration sensor and orientation sensor in the smartphone. In the past, it was a common method to use the magnitude of the acceleration vector to recognize human behavior. These days, deep learning has been actively studied and applied to various areas. In this article, we propose a method for recognizing the direction of human falling by applying the deep learning multilayer technique, which has been widely used recently.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.4
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• pp.23-28
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• 2010

Recently, as the number of people using Smartphones increased, several researches has been launched basing it's subject on this issue. In this circumstance, the fusion of sensor technologies and Smartphone offers a variety of functions. The system introduced in the current paper uses a controller which extracts information about accelerating movements of an user. This information is then sent to the Smartphone through Bluetooth communication. The input method proposed in this paper differs from the existing methods such as touch typing or button input in Smartphones. It rather uses the 3-D acceleration sensor which enables users to control their Smartphone anywhere and anytime without directly touching the device. Furthermore, because it is developed for various applications, it can be applied in many different fields.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.486-487
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• 2019

Smartphones have various sensors built in. In particular, acceleration sensors are used to analyze human behavior because they can detect movement of objects. Previous studies have analyzed the behavior of people by analyzing the magnitude of acceleration sensor values. In this study, we proposed a method of detecting the motion by applying the K-average of the acceleration sensor value built in the smartphone. We proposed a method of recognizing walking and running, which is basic human behavior, by applying K-average of acceleration sensor value of smartphone.

• Journal of Information Technology Services
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• v.11 no.sup
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• pp.113-121
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• 2012

In this study, a real-time fall detection system based on a smartphone equipped with three-axis accelerometer and magnetometer was proposed and evaluated. The proposed system provides a service that detects falls in real time, triggers alarm sound, and sends emergency SMS(Short Message Service) if the alarm is not deactivated within a predefined time. When both of the acceleration magnitude and angle displacement of the smartphone attached to waist belt are greater than predefined thresholds, it is detected as a fall. The proposed system was evaluated against activities of daily living(walking, jogging, sitting down, standing up, ascending stairs, and descending stairs) and unintended falls induced by a proprietary pneumatic-powered mattress. With the thresholds of acceleration magnitude 1.7g and angle displacement $80^{\circ}$, it showed 96.5% accuracy to detect the falls while all the activities of daily living were not detected as fall.

• Journal of the Korea Convergence Society
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• v.10 no.6
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• pp.179-184
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• 2019

Recently, various problems have arisen due to the popularization and aging of urban railway transit, which is the key transportation of large cities. In this study, the vibrational accelerations for the Daegu Metropolitan City Urban Railway(Line 1) were measured and evaluated using the smartphone built-in acceleration sensor and the approved application. For this purpose, the three axes running accelerations were measured according to the domestic standard (KS R 9160), and the acceleration data along the 32 stations (3 directions) were analyzed and compared. In addition, the increasing of acceleration values caused by the change of vibrational environment was monitored along the main stations between the time in 1997 and 2017. It was found that there are considerable increase of lateral and vertical directional accelerations due to the aging of railway facility environment for the last 20 years. The results of this study have valuable means for evaluating the ride quality of urban railway and the vibration influence on surrounding structures.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.5
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• pp.773-782
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• 2022

Estimation of cable tension through proper measurements is one of the essential tasks in evaluating the safety of cable structures. In this paper, a study on cable tension estimation using the built-in accelerometer and camera in a smartphone was conducted. For the experimental study, visual displacement measurement using a smartphone camera and acceleration measurement using a built-in accelerometer were performed in the cable-stayed bridge model. The estimated natural frequencies and transformed tensions from these measurements were compared with the theoretical values and results from the normal visual displacement method. Through comparison, it can be seen that the error between the method using the smartphone and the normal visual displacement is sufficiently small to be acceptable. It has also been shown that those errors are much smaller than the difference between the values calculated by the theoretical model. These results show that the deviation according to the type of measurement method is not large and it is rather important to use an appropriate mathematical model. In conclusion, in the case of cable tension estimation, it can be said that the visual displacement measurement and acceleration using a smartphone can be a sufficiently applicable method, just like the normal visual displacement method. It is also noteworthy that the smartphone accelerometer has a larger magnitude error and has more limitations such as high-frequency sampling instability compared to the visual displacement method, but shows almost the same performance as the visual displacement method in this cable tension estimation.

• Journal of Digital Contents Society
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• v.18 no.3
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• pp.559-565
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• 2017

A lot of studies have been conducted to recognize the motion state or behavior of the user using the acceleration sensor built in the smartphone. In this paper, we applied the neural networks to the 3-axis acceleration information of smartphone to study human behavior. There are performance issues in applying time series data to neural networks. We proposed a multi-temporal neural networks which have trained three neural networks with different time windows for feature extraction and uses the output of these neural networks as input to the new neural network. The proposed method showed better performance than other methods like SVM, AdaBoot and IBk classifier for real acceleration data.