• The Journal of the Korea Contents Association
• /
• v.5 no.4
• /
• pp.94-102
• /
• 2005

In this paper, three axial accelerometer was used to develop a small sensor module, which was attached to human body to calculate the acceleration in gravity direction by human motion, when it was positioned in any direction. To measure its wearer's walking or running motion using the sensor module, the acquired sensor data was pre-processed to enable its quantitative analysis. The acquired digital data was transformed to orthogonal coordinate value in three dimension and calculated to be single scalar acceleration data in gravity direction and normalized to be physical unit value. The normalized sensor data was used to detect walking pattern and calculate their step counts. Developed algorithm was implemented in the form of PDA application. The accuracy of the developed sensor to detect step count was about 97% in laboratory experiment.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.6 no.1
• /
• pp.1-8
• /
• 2012

Estimating algorithm of physical activity energy expenditure was implemented by using a tri-axial accelerometer motion detector of the SVM(Signal Vector Magnitude) of 3-axis(x, y, z). A total of 33 participants(15 males and 18 females) that performed walking and running on treadmill at 2 ~ 11 km/h speeds(each stage increase 1km/h). Algorithm for energy expenditure of physical activities were implemented with $VO_2$ consumption and SVM correlation between the data. Algorithm consists of three kinds and hip, wrist, waist and hip can be used to apply.

• Journal of Sensor Science and Technology
• /
• v.18 no.6
• /
• pp.432-440
• /
• 2009

This paper presents an accelerometer based system for physical activity decision that are capable of recognizing three different types of physical activities, i.e., standing, walking and running, using by rough sets. To collect physical acceleration data, we developed the body sensor node which consists of two custom boards for physical activity monitoring applications, a wireless sensor node and an accelerometer sensor module. The physical activity decision is based on the acceleration data collected from body sensor node attached on the user's chest. We proposed a method to classify physical activities using rough sets which can be generated rules as attributes of the preprocessed data and by constructing a new decision table, rules reduction. Our experimental results have successfully validated that performance of the rule patterns after removing the redundant attribute values are better and exactly same compare with before.

• Safety and Health at Work
• /
• v.4 no.1
• /
• pp.46-51
• /
• 2013

Objectives: Load carrying tasks are recognized as one of the primary occupational factors leading to slip and fall injuries. Nevertheless, the mechanisms associated with load carrying and walking stability remain illusive. The objective of the current study was to apply local dynamic stability measure in walking while carrying a load, and to investigate the possible adaptive gait stability changes. Methods: Current study involved 25 young adults in a biomechanics research laboratory. One tri-axial accelerometer was used to measure three-dimensional low back acceleration during continuous treadmill walking. Local dynamic stability was quantified by the maximum Lyapunov exponent (maxLE) from a nonlinear dynamics approach. Results: Long term maxLE was found to be significant higher under load condition than no-load condition in all three reference axes, indicating the declined local dynamic stability associated with load carrying. Conclusion: Current study confirmed the sensitivity of local dynamic stability measure in load carrying situation. It was concluded that load carrying tasks were associated with declined local dynamic stability, which may result in increased risk of fall accident. This finding has implications in preventing fall accidents associated with occupational load carrying.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.24 no.1
• /
• pp.81-88
• /
• 2011

A real time monitoring system from a PC has been developed which can be accessed through transmitted data, which incorporates an established low powered transport system equipped with a single chip combined with wireless sensor network technology from a three-axis acceleration sensor. In order to distinguish between static posture and dynamic posture, the extracted parameter from the rapidly transmitted data needs differentiation of movement and activity structures and status for an accurate measurement. When results interpret a static formation, statistics referring to each respective formation, known as the K-mean algorithm is utilized to carry out a determination of detailed positioning, and when results alter towards dynamic activity, fuzzy algorithm (fuzzy categorizer), which is the relationship between speed and ISVM, is used to categorize activity levels into 4 stages. Also, the ISVM is calculated with the instrumented acceleration speed on the running machine according to various speeds and its relationship with kinetic energy goes through correlation analysis. With the evaluation of the proposed system, the accuracy level stands at 100% at a static formation and also a 96.79% accuracy with kinetic energy and we can easily determine the energy consumption through the relationship between ISVM and kinetic energy.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.46 no.10
• /
• pp.40-45
• /
• 2009

In this paper, we introduce a mobile-type tiny sensor device that can classify the activities of daily living based on the state-dependent motion analysis using a 3-axial accelerometer in real-time. The device consists of an accelerometer, GPS module, 32bit micro-controller for sensor data processing and activity classification, and a bluetooth module for wireless data communication. The size of device is 50*47*14(mm) and lasts about 10 hours in operation-mode and 160 hours in stand-by mode. Up to now, the device can recognize three user activities ("Upright", "Running", "Walking") based on the decision tree. This tree is constructed by the pre-learning process to activities of subjects. The accuracy rate of recognizing activities is over 90% for various subjects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2008.10a
• /
• pp.265-269
• /
• 2008

Home health care with compact wearable units sounds to be a convenient solution for the elderly people living independently. This paper presents a method to detect fall from the other activities of daily living and also to classify those activities. This kind of ambulatory monitoring enables them to get an emergency help in the case of the fatal fall event and can provide their general health status by observing the activities being performed in daily life. A tri-axial accelerometer sensor is used to get the acceleration anomalies associated with the user's movements. The three axis acceleration data are transferred to the base station sensor node via an IEEE 802.15.4 compliant zigbee module. The base station sensor node sends the data to base station PC for an offline processing. This work shows the feature set preparation using the principal component analysis (PCA) for the designing of neural network. The work includes the most common activities of daily living (ADL) like Rest, Walk and Run along with the detection of fall events from ADL. The angle from the vertical is found to be the most significant feature parameter for classification of fall while mean, standard deviation and FFT coefficients were used as the feature parameter for classifying the other activities under consideration. The accuracy for detection of fall events is 86%. The overall accuracy for ADL and fall is 94%.

• The Journal of the Korea Contents Association
• /
• v.6 no.12
• /
• pp.96-104
• /
• 2006

In this study, data transmission ratio and power consumption issues of Zigbee based sensor module and personal digital assistant(PDA) were addressed to develop ECG telemetry device. PDA processes the data transmitted through serial port using non-blocking method. The transmission rate was dependent on the packet structure. It was 300 ECG samples/sec, when each packet was composed of 2 ECG data and 3-axial acceleration vector. Using two AAA batteries in series, operating time of the wireless sensor module was above 28 hours in average. Power consumption of PDA was dependent on screen ON/OFF condition and serial port usage. In this application, operating time of PDA was 5 hours in average. In conclusion, there was no problem in the power consumption of wireless sensor module and transmission rate, when the developed device was used as 24 hour Holter device. But, PDA has the problem of power consumption, which should be solved.

• Journal of IKEEE
• /
• v.22 no.4
• /
• pp.953-958
• /
• 2018

In this study, a portable detection system was developed that can detect harmful gas and signals simultaneously in an enclosed space of industrial sites and underground facilities. The developed system is a sensor module for gas detection, a patch type 1 channel small ECG sensor, a module for three-axial acceleration detection sensor, and a system for statistics. In order to verify the performance of the system modules, the digital resolution, signal frequency, output voltage, and ultra-small modules were evaluated. As a result of the performance of the developed system, the digital resolution was 300 (rps) and the signal amplification gain was 500 dB or more, and the ECG module was manufactured with $50mm{\times}10mm{\times}10mm$ to increase patch utilization. It is believed that the product of this research will be valuable if it is used as an IoT-based management system for real-time monitoring of industrial workers.

• Wind and Structures
• /
• v.34 no.2
• /
• pp.199-213
• /
• 2022

The current study investigates the dynamic effects in the tornado-structure response of an aeroelastic self-supported lattice transmission tower model tested under laboratory simulated tornado-like vortices. The aeroelastic model is designed for a geometric scale of 1:65 and tested under scaled down tornadoes in the Wind Engineering, Energy and Environment (WindEEE) Research Institute. The simulated tornadoes have a similar length scale of 1:65 compared to the full-scale. An extensive experimental parametric study is conducted by offsetting the stationary tornado center with respect to the aeroelastic model. Such aeroelastic testing of a transmission tower under laboratory tornadoes is not reported in the literature. A multiaxial load cell is mounted underneath the base plate to measure the base shear forces and overturning moments applied to the model in three perpendicular directions. A three-axis accelerometer is mounted at the level of the second cross-arm to measure response accelerations to evaluate the natural frequencies through a free-vibration test. Radial, tangential, and axial velocity components of the tornado wind field are measured using cobra probes. Sensitivity analyses are conducted to assess the variation of the structural dynamic response associated with the location of the tornado relative to the lattice transmission tower. Three different layouts representing the change in the orientation of the tower model relative to the components of the tornado-induced loads are considered. The structural responses of the aeroelastic model in terms of base shear forces, overturning moments, and lateral accelerations are measured. The results are utilized to understand the dynamic response of self-supported transmission towers to the tornado-induced loads.