• Journal of Sensor Science and Technology
• /
• v.22 no.1
• /
• pp.54-64
• /
• 2013

In this study, we developed a falling recognition system to transmit SMS data through CDMA communication using a three axises acceleration sensor and a two axises gyro sensor. 5 healthy men were selected into a control group, and the fall recognition system using the three axises acceleration sensor and the two axises gyro sensor was devised to conduct an experiment. The system was attached to the upper of their sternum. According to the experiment protocol, the experiment was carried out 3 times repeatedly divided into 3 specific protocols: falling during gait, falling in stopped state, and falling in everyday life. Data obtained in the falling recognition system and LabVIEW 8.5 were used to decide if falling corresponds to that regulated in an analysis program applying an algorithm proposed in this study. In addition, results from falling recognition were transmitted to designated cellular phone in a SMS (Shot Message Service) form. These research results show that an erroneous detection rate of falling reached 19% in applying an acceleration signal only; 6% in applying an angular velocity; and 2% in applying a proposed algorithm. Such finding suggests that an erroneous detection rate of falling is improved when the proposed algorithm is applied incorporated with acceleration and angular velocity. In this study therefore, we proposed that a falling recognition system implemented in this study can make a contribution to the recognition of falling of the aged or the disabled.

• Journal of Korea Multimedia Society
• /
• v.18 no.4
• /
• pp.565-574
• /
• 2015

Falls are the main cause of serious injuries and accidental deaths in people over the age of 65. Due to widespread adoption of smartphones, there has been a growing interest in the use of smartphones for detecting human behavior and activities. Modern smartphones are equipped with a wide variety of sensors such as an accelerometer, a gyroscope, camera, GPS, digital compass and microphone. In this paper, we introduce a new method that determines the fall direction of human subjects by analyzing the three axis components of acceleration vector.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.6
• /
• pp.1309-1316
• /
• 2018

Cattle estrus detection system based on Internet of Things is designed and implemented by using Arduino pro-mini, gyroscope, acceleration sensor, bluetooth master and slave module. The implemented system measures cattle's moving and the measured data are transmitted to the computer connected to RX module by bluetooth TX module. They are plotted in 2-dimensional graph on the computer monitor and the number of transition at each sensor axis are calculated from the graph. The detected and gathered data from the system are analyzed by the proposed algorithm to decide which cows are in the estrus or not. The method to apply bluetooth scatternet is shown and the proposed system can be used to increase the success rate of artificial insemination in normal estrus by detecting the cow's behaviors such as the number of jumping. In this paper, the implemented cattle behavior detecting the system(TX module) are strapped on cattle's leg and it measures the cattle behaviors for determining where that a cattle is estrus or not by the proposed algorithm.

• Journal of the Korea Safety Management & Science
• /
• v.20 no.2
• /
• pp.37-44
• /
• 2018

The Location Based Service is growing rapidly nowadays due to the universalization of the use for smartphone, and therefore the location determination technology has been placed in a very important position. This study suggests an algorithm that can provide the estimate of users' location by using smartphone sensors. And in doing so we will propose a methodology for the creation and update of indoor map through the more accurate position estimation using smartphone sensors such as acceleration sensor, gyroscope sensor, geomagnetic sensor and rotation sensor.

• Journal of Korea Society of Industrial Information Systems
• /
• v.21 no.2
• /
• pp.31-37
• /
• 2016

Falling situations are extremely critical events for the elderly person who requires timely and adequate emergency service. For the case of emergency, the information of falling and its direction can be used as an important information for the first aid treatment of the injured person. In this paper, a falling detection system which can pinpoint the falling event with the falling direction is implemented. In order to detect the fall situation, a single gyroscope (MPU-6050) is used in the developed system. The fall detection algorithm that can classify 8 different fall directions such as front, back, left, right and in between falls is proposed. The direction of the fall is decided by examining the acceleration values of X and Y directions of the sensor. It is shown that the proposed algorithm successfully detects the falling event and the falling direction with probability of 97% for a selected value of acceleration threshold.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.12 no.6
• /
• pp.1159-1166
• /
• 2017

Cattle behavior detection system based on Internet of Things is designed and implemented by using gyroscope and acceleration sensor, Arduino pro-mini and bluetooth module. The implemented system measures cattle's moving and the measured data are transmitted to smart phone by bluetooth module. They are displayed by 2-dimensional graph on the smart phone and the number of cattle's step are calculated from the graph. The detected and gathered data from the system are analyzed by the proposed algorithm to decide which cows are in the estrus or not, and the proposed system can be used to increase the success rate of artificial insemination in normal estrus by detecting cow's behaviors such as the number of steps and jumping. In this paper, the implemented cattle behavior detecting system are strapped on cattle's leg and it measures cattle behaviors for determining that a cattle is estrus or not by the proposed algorithm. In the future research, the system which lengthens communication distance and increases the number of cattle under the test will be considered and also the measured data will be database for cattle research.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.2
• /
• pp.133-140
• /
• 2008

In this paper, a small and effective attitude estimation system for a humanoid robot was developed. Four small inertial sensors were packed and used for inertial measurements(3D accelerometer and three 1D gyroscopes.) An effective 3D pose estimation algorithm for low cost DSP using an extended Kalman filter was developed and evaluated. The 3D pose estimation algorithm has a very simple structure composed by 3 modules of a linear acceleration estimator, an external acceleration detector and an pseudo-accelerometer output estimator. The algorithm also has an effective switching structure based on probability and simple feedback loop for the extended Kalman filter. A special test equipment using linear motor for the testing of the 3D pose sensor was developed and the experimental results showed its very fast convergence to real values and effective responses. Popular DSP of TMS320F2812 was used to calculate robot's 3D attitude and translated acceleration, and the whole system were packed in a small size for humanoids robots. The output of the 3D sensors(pitch, roll, 3D linear acceleration, and 3D angular rate) can be transmitted to a humanoid robot at 200Hz frequency.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.21 no.9
• /
• pp.1725-1730
• /
• 2017

The acceleration sensor and the gyroscopic sensor are used as representative sensors to detect repetitive motion and have been used to analyze various sporting components. However, both sensors have problems with noise sensitivity and accumulation of errors. There have been attempts to use two sensors together to overcome hardware problems. The complementary filter has shown successful results in mitigating the problems of both sensors by minimizing the disadvantages of accelerometer and gyroscope sensors and maximizing their advantages. In this paper, we proposed a modified method using neural network to reduce variable. The neural network is an algorithm that can precisely measure even in unexpected environments or situations by pre-learning the number of various cases. The proposed method applies a Neural Network by dividing the repetitive motion into three sections, the first, the middle and the end. As a result, the recognition rate is 96.35%, 98.77%, 96.92% and the accuracy is 97.18%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.666-671
• /
• 2019

The flight controller(FC) used in small-sized drone was developed as simple structure does not perform complex operations because it uses different MCU with large-sized drone. Also, the balance control of small-sized drone should be simpler than Kalman filter using complex filter and the method using Complementary filter has relatively more operations. So, the method to realize the balance control on small-sized drone effectively using two-track control operating as proper method for above is suggested in this research. This method is a system maintaining effective balance with simple structure and less operations by operating adaptively for the unbalance of the drone with the acceleration sensor with the advantage which performing accurate correction by data processing for long term change and gyroscope sensor maintaining the balance of the drone by data processing for short term change. It is confirmed that stable operation was performed mostly based on the test result for repeatable test more than 100 times using two-track control and it maintained normal state operation more than 98% excluding the difficulty of maintaining normal operation when meets sudden and rapid wind yet.

• Journal of Multimedia Information System
• /
• v.2 no.2
• /
• pp.233-240
• /
• 2015

Mobile devices are becoming increasingly sophisticated and the latest generation of smartphones now incorporates many diverse and powerful sensors. These sensors include acceleration sensor, magnetic field sensor, light sensor, proximity sensor, gyroscope sensor, pressure sensor, rotation vector sensor, gravity sensor and orientation sensor. The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper, we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity that a user is performing. To implement our system, we collected labeled accelerometer data from 10 users as they performed daily activities such as "phone detached", "idle", "walking", "running", and "jumping", and then aggregated this time series data into examples that summarize the user activity 5-minute intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users-just by having them carry cell phones in their pockets.