• KIPS Transactions on Computer and Communication Systems
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• v.2 no.6
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• pp.257-264
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• 2013

In this study, we have designed the variable amplitude threshold algorithm that can enhance the accuracy of step count using variable amplitude. This algorithm converts the x, y, z sensor values into a single energy value($E_t$) by using SVM(Signal Vector Magnitude) algorithm and can pick step count out over 99% of accuracy through the peak data detection algorithm and fixed peak threshold. To prove the results, We made the noise filtering with the fixed amplitude threshold from the amplitude of energy value that found out the detection error was increasing, and it's the key idea of the variable amplitude threshold that can be adapted on the continuous data evaluation. The experiment results shows that the variable amplitude threshold algorithm can improve the average step count accuracy up to 98.9% at 10 Hz sampling rate and 99.6% at 20Hz sampling rate.

• Journal of IKEEE
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• v.23 no.4
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• pp.1457-1460
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• 2019

In this paper, we proposed the development of a mixed sensor parts for integrated radiation exposure protection fireman's life-saving alarm that can be location-tracked and irradiated. To measure radiation exposure dose, we use the PIN-Diode radiation measurement sensor module, a semi-conductive radiation measurement sensor that can minimize size and weight. The design for removing leakage current is carried out to enhance the characteristics of the radiation measurement sensor using PIN-Diode. The IMU sensor module is used to estimate the location of the current fireman at the same time as the accident estimate by adding together the data and the values for acceleration on the three axis. Experiments were conductied by an authorized testing agency to determine the efficiency of the proposed mixed sensor parts for integrated radiation exposure protection fireman's life-saving alarms. The cumulative dose measurement range was measured in the range of 10 μSv to 10 mSv, the highest level in the world. The accuracy was measured from ±6.3% to ±9.0% (137 Cs) and normal operation was found at the international standard of ±15%. In addition, positional accuracy was measured within ±10%, resulting in a high level of results, demonstrating its effectiveness. Therefore, it is expected that more firemen will be able to provide with superior performance integrated radiation exposure protection fireman life-saving alarm.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.384-391
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• 2023

Among the elderly, fatal injuries and deaths are significantly attributed to falls. Therefore, a pre-impact fall detection system is necessary for injury prevention. In this study, a robust threshold-based algorithm was proposed for pre-impact fall detection, reducing false positives in highly dynamic daily-living movements. The algorithm was validated using public datasets (KFall and FARSEEING) that include the real-world elderly fall. A 6-axis IMU sensor (Movella Dot, Movella, Netherlands) was attached to S2 of 20 healthy adults (aged 22.0±1.9years, height 164.9±5.9cm, weight 61.4±17.1kg) to measure 14 activities of daily living and 11 fall movements at a sampling frequency of 60Hz. A 5Hz low-pass filter was applied to the IMU data to remove high-frequency noise. Sum vector magnitude of acceleration and angular velocity, roll, pitch, and vertical velocity were extracted as feature vector. The proposed algorithm showed an accuracy 98.3%, a sensitivity 100%, a specificity 97.0%, and an average lead-time 311±99ms with our experimental data. When evaluated using the KFall public dataset, an accuracy in adult data improved to 99.5% compared to recent studies, and for the elderly data, a specificity of 100% was achieved. When evaluated using FARSEEING real-world elderly fall data without separate segmentation, it showed a sensitivity of 71.4% (5/7).

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.49-56
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• 2018

As the era of the 4th industrial revolution comes, there is a growing interest in the use of UAVs. While various technologies are being developed using drones, controlling flight of drones is the most basic. Hovering control is essential in order to enable autonomous flight, especially during flight control of drones. In this paper, we design drones based on ATmega2560, Sonar, Optical Flow, and acceleration / gyro 6 axis sensor for drones hovering control, and developed horizontal control, altitude control, position tracking and fixed algorithm based on PID control. In this research, in order to measure the objective result of the drone, keeping the altitude immediately after the drone takes off according to the time, measure the movement value until the position is fixed and stable hovering is maintained and compared analyzed. Experimental results show that the drones can stably hover within 4cm horizontal and 2cm vertical from 50cm above the reference coordinates.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.6
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• pp.569-575
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• 2013

This paper proposes a method for PDR (Pedestrian Dead-Reckoning) using a low cost IMU. Generally, GPS has been widely used for localization of pedestrians. However, GPS is disabled in the indoor environment such as in buildings. To solve this problem, this research suggests the PDR scheme with an IMU attached to the pedestrian's waist. However, despite the fact many methods have been proposed to estimate the pedestrian's position, but their results are not sufficient. One of the most important factors to improve performance is, a new calibration method that has been proposed to obtain the reliable sensor data. In addition to this calibration, the PDR method is also proposed to detect steps, where estimation schemes of step length, attitude, and heading angles are developed. Peak and zero crossings are detected to count the steps from 3-axis acceleration values. For the estimation of step length, a nonlinear step model is adopted to take advantage of using one parameter. Complementary filter and zero angular velocity are utilized to estimate the attitude of the IMU module and to minimize the heading angle drift. To verify the effectiveness of this scheme, a real-time system is implemented and demonstrated. Experimental results show an accuracy of below 1% and below 3% in distance and position errors, respectively, which can be achievable using a high cost IMU.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.4
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• pp.191-197
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• 2005

Biped walking is the main feature of a humanoid robot. In a biped walking robot, there are many actuators to be controlled and many sensors to be interfaced. In this paper, we propose a DSP-based controller for a miniature biped walking robot with 21 RC servo motors. The proposed controller has a hierarchical structure; a host PC, a DSP-based main controller, and an auxiliary controller with an FPGA chip. The host PC generates and transmits the robot walking data for given walking parameters such as stride, walking period, etc. The main controller implemented with a TMS320LF2407A controls 21 RC servo motors via the auxiliary controller. We also perform some experiments for balancing motion and walking on a slope terrain with interfacing a 2-axis acceleration sensor and a TMS320LF2407A.

• Journal of Internet of Things and Convergence
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• v.2 no.2
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• pp.1-6
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• 2016

Recently, various types of wearable computers have been studied. In this paper, we analyze the characteristics of head motion information for the operation of the motion classifier produced motion headset that the user can use while listening to music. The prototype receives music from smart phone over bluetooth communications, and transmits the motion information measured by the acceleration sensor to the smart phone. And the smartphone classifies the motion of the head through a motion classifier. we implemented a prototype for our experiment. The user's head motion "up", "down", "left" and "right" were classified using a Bayesian classifier. As a result, in case of the movement of the head "up" and "down", there are a large changes in the x, z-axis values. In future we have a plan to perform a user study to find suitable variables for creating motion classifier.