• The Journal of the Korea Contents Association
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• v.12 no.5
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• pp.22-31
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• 2012

A Smartphone is an intelligent device combined mobile phone and pc's support functions, and can perform multiple functions to satisfy the demands of users. It has excellent processing power and communication modules(DMB, Wi-Fi, Bluetooth, NFC etc) to carry out the demands of users. But continuous using of battery power on processor and equipped modules causes acceleration of battery consumption. This means that effective power management in devices like smartphone is important. Therefore, the management of power consumption on system execution and communication module is a serious issue in this field of study. In this paper, we would like to propose a communication module selection algorithm based on energy consumption parameter of each communication module and data transfer time. Our scheme automatically select appropriate communication system to reduce high energy consumption on bluetooth sleep mode so that this scheme is more efficient and effective thus improving user convenience in longer usage time. Experimental results showed the 20% energy saving.

• Journal of The Korean Ophthalmological Society
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• v.58 no.11
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• pp.1313-1316
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• 2017

Purpose: To report fundus photography using a smartphone in an infant with abusive head trauma. Case summary: An 8-month-old male infant presented to the emergency room with decreased consciousness and epileptic seizures that the parents attributed to a fall from a chair. He had no external wounds or fractures to the skull or elsewhere. However, computerized tomography of the brain revealed an acute subdural hematoma in the right cranial convexity and diffuse cerebral edema, leading to a midline shift to the left and effacement of the right lateral ventricle and basal cistern. The attending neurosurgeon promptly administered a decompressive craniectomy. Immediately after the emergency surgery, a fundus examination revealed numerous multi-layered retinal hemorrhages in the posterior pole extending to the periphery in each eye. He also had white retinal ridges with cherry hemorrhages in both eyes. We acquired retinal photographs using the native camera of a smartphone in video mode. The photographer held the smartphone with one hand, facing the patient's eye at 15-20 cm, and held a 20 diopter condensing lens at 5 cm from the eye in the other hand. Our documentation using a smartphone led to a diagnosis of abusive head trauma and to obtain the criminal's confession, because the findings were specific for repetitive acceleration-deceleration forces to an infant's eye with a strong vitreoretinal attachment. Conclusions: This ophthalmic finding had a key role in the diagnosis of abusive head trauma. This case presented the diagnostic use of a smartphone for fundus photography in this important medicolegal case.

• Journal of the Korea Convergence Society
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• v.11 no.5
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• pp.9-17
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• 2020

Human activity recognition(HAR) using smartphones is a hot research topic in computational intelligence. Smartphones are equipped with a variety of sensors. Fusing the data of these sensors could enable applications to recognize a large number of activities. However, these devices have fewer resources because of the limited number of sensors available, and feature selection and classification methods are required to achieve optimal performance and efficient feature extraction. This paper proposes a smartphone-based HAR scheme according to these requirements. The proposed method in this paper extracts time-domain features from acceleration sensors, gyro sensors, and barometer sensors, and recognizes activities with high accuracy by applying KDA and SVM. This approach selects the most relevant feature of each sensor for each activity. Our comparison results shows that the proposed system outperforms previous smartphone-based HAR systems.

• Physical Therapy Rehabilitation Science
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• v.11 no.3
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• pp.311-320
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• 2022

Objective: To investigate the reliability and validity of static balance measurements using an acceleration sensor and a gyroscope sensor in smart phone inertial sensors. Design: Equivalent control group pretest-posttest. Methods: Subjects were forty five healthy adults aged twenty to fifty-years-old who had no disease that could affect the experiment. After pre-test, all participants wore a waist band with smart phone, and conducted six static balance measurements on the force plate twice for 35 seconds each. To investigate the test-retest reliability of both smart phone inertial sensors, we compared the intra-correlation coefficient (ICC 3, 1) between primary and secondary measurements with the calculated root mean scale-total data. To determine the validity of the two sensors, it was measured simultaneously with force plate, and the comparision was done by Pearson's correlation. Results: The test-retest reliability showed excellent correlation for acceleration sensor, and it also showed excellent to good correlation for gyroscope sensor(p<0.05). The concurrent validity of smartphone inertial sensors showed a mostly poor to fair correlation for tandem-stance and one-leg-stance (p<0.05) and unacceptable correlation for the other postures (p>0.05). The gyroscope sensor showed a fair correlation for most of the RMS-Total data, and the other data also showed poor to fair correlation (p<0.05). Conclusions: The result indicates that both acceleration sensor and gyroscope sensor has good reliability, and that compared to force plate, acceleration sensor has unacceptable or poor correlation, and gyroscope sensor has mostly fair correlation.

• Advances in Computational Design
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• v.4 no.1
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• pp.1-13
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• 2019

Recent generations of smartphones offer accelerometer sensors as a standard feature. While this has led to the development of a number of related applications (apps), there has been no study on their comparative or individual performance against a benchmark. This paper investigates the comparative performance of a number of smartphone accelerometer apps amongst themselves and to a calibrated benchmark accelerometer. A total of 12 apps were selected for testing out of 90 following an initial review. The selected apps were subjected to sinusoidal vibration testing of varying frequency and the response of each compared against the calibrated baseline accelerometer. The performance of apps was quantified using analysis of variance (ANOVA) and test of significance was carried out. The apps were then compared for a realistic dynamic scenario of measuring the acceleration response of a bridge due to the passage of a French Train $\grave{a}$ Grande Vitesse (TGV) in a laboratory environment.

• Smart Structures and Systems
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• v.31 no.2
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• pp.141-154
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• 2023

After an earthquake, information regarding potential damage to buildings close to the epicenter is very important during the initial emergency response. This study proposes the use of crowdsourced measured acceleration response data collected from smartphones located within buildings to perform system identification of building structures during earthquake excitations, and the feasibility of the proposed approach is studied. The principal advantage of using crowdsourced smartphone data is the potential to determine the condition of millions of buildings without incurring hardware, installation, and long-term maintenance costs. This study's goal is to assess the feasibility of identifying the lowest fundamental natural frequencies of buildings without knowing the orientations and precise locations of the crowds' smartphones in advance. Both input-output and output-only identification methods are used to identify the lowest fundamental natural frequencies of numerical finite element models of a real building structure. The effects of time synchronization and the orientation alignment between nearby smartphones on the identification results are discussed, and the proposed approach's performance is verified using large-scale shake table tests of a scaled steel building. The presented results illustrate the potential of using crowdsourced smartphone data with the proposed approach to identify the lowest fundamental natural frequencies of building structures, information that should be valuable in making emergency response decisions.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1148-1153
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• 2016

The number of cyclists is on the steady growing for leisure and transportation with the increasing interest in health and environment. However, the number of cycling accidents is also increasing steadily due to the lack of safety awareness and regulations. Focusing on this issue, we propose and develop a smart LED bicycle helmet in order to reduce a risk of cycling accident. The main idea is to change status of the LED on the helmet based on the bicycle's movement and provide motion information of the bicycle for others. To control the LED lights on the helmet, we use the Arduino board which communicates with the LED module through serial connection. We decide motion information by using the values from acceleration and GPS sensors of the smartphone. To receive this information from the smartphone, the control board and the smartphone are connected by Bluetooth.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.11
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• pp.537-542
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• 2017

Aggressive driving is a major cause of car accidents. Previous studies have mainly analyzed young driver's aggressive driving tendency, yet they were only done through pure clustering or classification technique of machine learning. However, since elderly people have different driving habits due to their fragile physical conditions, it is necessary to develop a new method such as enhancing the characteristics of driving data to properly analyze aggressive driving of elderly drivers. In this study, acceleration data collected from a smartphone of a driving vehicle is analyzed by a newly proposed ECA(Enhanced Clustering method for Acceleration data) technique, coupled with a conventional clustering technique (K-means Clustering, Expectation-maximization algorithm). ECA selects high-intensity data among the data of the cluster group detected through K-means and EM in all of the subjects' data and models the characteristic data through the scaled value. Using this method, the aggressive driving data of all youth and elderly experiment participants were collected, unlike the pure clustering method. We further found that the K-means clustering has higher detection efficiency than EM method. Also, the results of K-means clustering demonstrate that a young driver has a driving strength 1.29 times higher than that of an elderly driver. In conclusion, the proposed method of our research is able to detect aggressive driving maneuvers from data of the elderly having low operating intensity. The proposed method is able to construct a customized safe driving system for the elderly driver. In the future, it will be possible to detect abnormal driving conditions and to use the collected data for early warning to drivers.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.781-790
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• 2024

As modern people's interest in exercise and health increases, the demand for exercise-related information and devices is increasing, and exercising in the wrong posture can lead to body imbalance and injury. Therefore, in this study, the purpose of this study is to correct the posture for health promotion and injury prevention through the correct exercise posture of users. It was developed using Arduino Uno R3, a pressure sensor, and an acceleration sensor as the main memory device of the system. The pressure sensor was used to determine the squat posture, and the acceleration sensor was used to determine three types of gait: normal step, nasolabial step, and saddle step. Data is transmitted to a smartphone through a Bluetooth module and displayed on an app to guide the user in the correct exercise posture. The gait was determined based on the 20˚ angle at which the foot was opened, and the correct squat posture was compared with the ratio of the pressure sensor values of the forefoot and hindfoot based on the data of the skilled person. Therefore, based on an experiment with about 90% accuracy when determining gait and 95% accuracy based on a 7:3 ratio of pressure sensor values in squat posture, a system was established to guide users to exercise in the correct posture by checking in real time through a smartphone application and correcting exercise in the wrong posture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.6
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• pp.233-238
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• 2016

The purpose of this study is to investigate the reliability of static balance measurements using a smartphone. Thirty subjects were selected among university students who had no fractures, history of operation, or inflammatory arthritis, and they had not started regular exercise during the past three months. The smartphone used in this study was a Galaxy S5LTE (SM-G900F, Samsung, Korea, 2014), and the application was a Sensor Kinetics Pro (Ver. 2.1.2, INNOVENTIONS Inc., US, 2015). Static balance ability was measured three times at one-day intervals between tests and retests. The first and second measurements used the same process. Analysis was done using the Wilcoxon signed rank test and intraclass correlation coefficient (ICC (2,1)). The results were as follows. With eyes opened, there was no significant difference (p>0.05), a high volume of correlation (r>0.75, p<0.05), and very high reliability (ICC>0.80) between the first measurement and second measurement. With eyes closed, there was also no significant difference (p>0.05), a high volume of correlation (r>0.75, p<0.05), and very high reliability (ICC>0.80) between the measurements. The results show that the smartphone is likely accurate for measuring static balance. This study will look forward to being the only basis for measuring future application development and the ability to balance.