• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.27-32
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• 2012

Balance measurement is widely used in fields of physiotherapy, neurology, sport medicine and rehabilitation medicine. This paper presents the development of balance measurement and rehabilitation training system. To this end, we have developed a prototype system using an 3-axis accelerometer sensor attached to the under surface of balance board. As a results, the system is stable and shows a good degree of balance function. Also, the various patterns for rehabilitation training can be added easily. This system can be used for daily balance monitoring and will contribute to increasing the effectiveness of training.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.24 no.1
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• pp.81-88
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• 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 rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.1-8
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• 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 Power System Engineering
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• v.18 no.4
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• pp.43-51
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• 2014

In this paper, transient temperature and vibration characteristics of a brushless DC (BLD(c) motor are studied for external load (165W~495W) and rotational speed (2000 rpm~4000 rpm). For experiment, a simple measurement system is developed to allow a change in load and speed for measuring transient temperature and vibration simultaneously. Temperature and vibration were also measured under the conditions of natural convection and forced convection. Vibrations in the directions of x-axis (#Ch1), y -axis (#Ch2) and z -axis (#Ch3) were obtained by three accelerometers and temperature was obtained by a thermo-couple with respect to time until the motor is steady. Experimental results show that the amplitude of vibration is higher in the order of z-axis (#Ch3), x -axis (#Ch1) and y-axis (#Ch2) and the amplitude of vibration at the forced convection conditions is 10.6% to 17.8% lower than that of vibration at the natural convection. However, the ratio of the vibration value is similar on average regardless of external convection condition.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.12
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• pp.1167-1172
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• 2015

This paper describes the implementation of wearable AHRS for an electromagnetic motion capture system that can trace and analyze human motion on the principal nine axes of inertial sensors. The module provides a three-dimensional (3D) attitude and heading angles combining MEMS gyroscopes, accelerometers, and magnetometers based on the extended Kalman filter, and transmits the motion data to the 3D simulation via Wi-Fi to realize the unrestrained movement in open spaces. In particular, the accelerometer in AHRS is supposed to measure only the acceleration of gravity, but when a sensor moves with an external linear acceleration, the estimated linear acceleration could compensate the accelerometer data in order to improve the precision of measuring gravity direction. In addition, when an AHRS is attached in an arbitrary position of the human body, the compensation of the axis of rotation could improve the accuracy of the motion capture system.

• Journal of Sensor Science and Technology
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• v.20 no.6
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• pp.420-427
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• 2011

The population of elderly people increases rapidly as our society moves towards the aged one. Healthcare for the elderly becomes an important issue and falling down is one of the critical problems although not well recognized. In this study, a fall detection system was developed using a 3-axis accelerometer. Analyzing fall patterns, we took into account the degree of impact, posture angle, the repetitions of similar movements and the activities after a potential fall and proposed an algorithm of fall detection. Information of the fall sensor was sent to a remote healthcare server through the wireless networks of Zigbee and WLAN. Our system was designed to monitor multiples users. 12 persons participated in experiment and each one performed 24 different movements. Our proposed algorithm was compared with other reported ones. Our method produced the excellent results having a sensitivity of 96.4 % and a specificity of 100 % whereas other methods had a sensitivity range between 87.5 % and 94.8 % and a specificity range between 63.5 % and 83.3 %.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.391-393
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• 2021

In this paper, we introduce a long short-term memory (LSTM)-based fall detection system using TensorFlow that can detect falls occurring in the elderly in daily living. 3-axis accelerometer data are aggregated for fall detection, and then three types of parameter are calculated. 4 types of activity of daily living (ADL) and 3 types of fall situation patterns are classified. The parameterized data applied to LSTM. Learning proceeds until the Loss value becomes 0.5 or less. The results are calculated for each parameter θ, SVM, and GSVM. The best result was GSVM, which showed Sensitivity 98.75%, Specificity 99.68%, and Accuracy 99.28%.

• Journal of Biomedical Engineering Research
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• v.30 no.3
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• pp.213-220
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• 2009

In this study, we have developed a novel step detection algorithm for gait evaluation of patients with hemiplegia based on trunk accelerometry device. For this, we have used a bandpass filter and a least square acceleration (LSA) filter which is characterized by emphasizing the peak or valley point of the acceleration signals for each 3-axis accelerometer signals. To evaluate the algorithm, the detected steps by developed algorithm and real steps by the motion analysis system were compared. As a result, we could obtain the sensitivity of 96.44%, the specificity of 99.94% and the accuracy of 99.90% for the patients' data sets and the sensitivity of 100%, the specificity of 99.93% and the accuracy of 99.93% for the normal data sets. In conclusion, the developed algorithm is useful for the step detection for patients with hemiplegia as well as normal subjects.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.11
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• pp.1330-1338
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• 2011

Social activities of the elderly have been increasing as our society progresses toward an aging society. As their activities increase, so does the occurrence of falls that could lead to fractures. Falls are serious health hazards to the elderly. Therefore, development of a device that can detect fall accidents and prevent fracture is essential. In this study, we developed a portable fall detection system for the fracture prevention system of the elderly. The device is intended to detect a fall and activate a second device such as an air bag deployment system that can prevent fracture. The fall detection device contains a 3-axis acceleration sensor and two 2-axis tilt sensors. We measured acceleration and tilt angle of body during fall and activities of daily(ADL) living using the fall detection device that is attached on the subjects'. Moving mattress which is actuated by a pneumatic system was used in fall experiments and it could provide forced falls. Sensor data during fall and ADL were sent to computer and filtered with low-pass filter. The developed fall detection device was successful in detecting a fall about 0.1 second before a severe impact to occur and detecting the direction of the fall to provide enough time and information for the fracture preventive device to be activated. The fall detection device was also able to differentiate fall from ADL such as walking, sitting down, standing up, lying down, and running.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.4
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• pp.19-24
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• 2013

The problem of elderly people with weak physical health has become a very important issue in the aging society. Elderly people with very low judgment and decision-making skills often falls because of the degradation of the strength and balance. Due to the fall triggered off fractures, parenchyma damage, and casualties, generally fast emergency treatment is needed. In this paper, an automatic fall detection system consisting of a triaxial accelerometer and tilt sensor. Using the fall system, the performance of the system was analyzed in many situations. The experimental results showed more than 92% analytical skills.