• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.703-706
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• 2007

The real-time monitoring about the activity of the human provides useful information about the activity quantity and an ability. This study developed a system for human physical activity assessment in ambulatory monitoring using portable sensing device combining a tri-axial accelerometer and wireless sensor node. This real-time system is able to identify several postures, posture transitions and movements with classification algorithm. In addition, this system also features fall detection capability. The results of the assessment for evaluating the performance of the system show high identification accuracy.

• 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.

• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.179-186
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• 2009

This works studied to compare gas analyzer with accelerometer and the estimation of energy expenditure based on different attaching position of tri-axial accelerometer such as waist and top of the foot Based on the fact that oxygen intake increases more radically linearly during walking more than 8.0km/hr. 9 male subjects performed walking and running on the treadmill with speed of $1.5{\sim}8.5km$/hr and $4.5{\sim}13.0km$/hr, respectively. Commercially available Nike + iPod Sports kit was used to compare energy expenditure with sensor module attached to their foot. Actual energy expenditure was determined by a continuous direct gas analyzer and two multiple regression equations of walking and running mode for different attaching position were developed. Results showed that estimation accuracy of energy expenditure using waist mounted accelerometer was higher than that of the top of the foot and Nike + iPod Sports kit. Results of energy expenditure based on waist and top of the foot showed that the crossover state of energy expenditure occurred at 7.5km/hr. But Nike + iPod Sports kit could not find intersection of energy expenditure in all nine subjects. Therefore the sensor module attached to the waist and separate multi regression equation by walking and running mode was the best to estimate more accurate prediction.

• Journal of Sensor Science and Technology
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• v.22 no.1
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• pp.54-64
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• 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 the Institute of Electronics Engineers of Korea CI
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• v.45 no.3
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• pp.135-141
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• 2008

We propose a new method for recognizing the activities of daily living(ADL) based on the state-dependent motion analysis using 3-axial accelerometers and a glove type RFID reader. Two accelerometers are used for the classification of 5 body states based on the decision tree. Classification of the instrumental activities is performed based on the hand interaction with an object ID using an accelerometer and a RFID reader. Object-dependent hand movements are classified into 5 categories in advance and final decision combines the body state and the instrumental activities. Experiment shows that the suggested hierarchical motion analysis provides accuracy rate of over 90% for all 20 ADLs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.781-788
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• 2013

In this paper, we introduce a quadruped robot-based alarm system for monitoring the emergency situation due to falling in the elderly. Quadruped robot includes the FPGA Board(Field Programmable Gate Array) applying a red-color tracking algorithm. To detect a falling of the elderly, a sensor node is worn on chest and accelerations and angular velocities measured by the sensor node are transferred to quadruped robot, and then the emergency signal is transmitted to manager if a fall is detected. Manager controls the robot and then he judges the situation by monitoring the real-time images transmitted from the robot. If emergency situation is decided by the manager, he calls 119. When the fall detection system using only sensor nodes is used, sensitivity of 100% and specificity of 98.98% were measured. Using the combination of the fall detection system and portable camera (robot), the emergency situation was detected to 100 %.