• Journal of Sensor Science and Technology
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• v.25 no.2
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• pp.79-85
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• 2016

This paper proposes a threshold-based fall recognition algorithm to discriminate between falls and activities of daily living (ADL) using a tri-axial accelerometer and a bi-axial gyroscope sensor mounted on the upper sternum. The experiment was executed ten times according to the proposed experimental protocol. The output signals of the tri-axial accelerometer and the bi-axial gyroscope were measured during eight falls and eleven ADL action sequences. The threshold values of the signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) parameter were calculated using MATLAB. From the preliminary study, three thresholds (TH1, TH2, and TH3) were set so that the falls could be distinguished from ADL. When the parameter SVM_Acc is greater than 2.5 g (TH1), ${\omega}_{res}$ is greater than 1.75 rad/s (TH2), and ${\theta}_{res}$ is greater than 0.385 rad (TH3), these action sequences are recognized as falls. If at least one or more of these conditions is not satisfied, the sequence is classified as ADL.

• Journal of Sensor Science and Technology
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• v.26 no.1
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• pp.7-14
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• 2017

A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accelerometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vector magnitude (SVM_Acc), angular velocity (${\omega}_{res}$), and angular variation (${\theta}_{res}$) were calculated using MATLAB. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ${\omega}_{res}$ was larger than 1.75 rad/s (TH2), and ${\theta}_{res}$ was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied, the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problem in distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ${\omega}_{res}$, and ${\theta}_{res}$ were applied to the sequential processing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the specificity was determined to be 100% for the eleven ADL action sequences.

• Journal of Korean Association for Spatial Structures
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• v.8 no.6
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• pp.75-83
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• 2008

The R.C Building will be superannuated as time passes by heavy load and serviceability. Methods of damage detection are used a visual angle of human or non-destructive test in the R.C Building. In case of the latter, Problems of damage detection are occurred to directions of steel bar. Elastic waves are difficult to assaying test using 1 axial type of accelerometer in reinforced concrete. In this study, fundamental studies for estimations using 3 axial type of accelerometer are discussed oscillator of elastic waves when embedded glass tube pipe or steel bar in flexible concrete specimens.

• The Journal of the Korea Contents Association
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• v.8 no.8
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• pp.253-260
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• 2008

The research for a 3-axial accelerometer sensor has increased dramatically in the fields of cellular phone, PDA, etc. In this paper, we develop a human walking detection algorithm using 3-axial accelerometer sensor and a user interface system to show the activity expenditure in real-time. To measure a walking number more correctly in a variety of walking activities including walking, walking in place, running, slow walking, we propose a new walking number detection algorithm using adaptive threshold value. In addition, we calculate the activity expenditure base on counted walking number and display calculated activity expenditure on UI in real-time. From the experimental results, we could obtain that the detection rate of proposal algorithm is higher than that of existing algorithm using a fixed threshold value about $5{\sim}10%$. Especially, it could be found out high detection rate in walking in place.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.131-136
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• 2011

In this study, a compact wireless vibration measurement system was developed using a 3-axial accelerometer in order to evaluate the vibration stimulation system. A low power microprocessor chip integrated with 2.4 GHz RF transceiver was used for the wireless data communication. To evaluate the system, the frequencies and accelerations from the vibration stimulation system were measured using an LVDT sensor and a vibration measurement system. The average frequency difference by the measurement system was less than 0.1 Hz, and the standard deviation of frequencies estimated by the LVDT sensor and the accelerometer was below 0.08 Hz. The developed system was applied to access a vibration stimulation system for the future study. The average acceleration difference of the central and peripheral point of the stimulation system was less than 0.0005 g(1 g=9.8 $m/s^2$), and the standard deviation of the acceleration was below 0.004 g, which shows the usefulness of the wireless vibration measurement system.

• The KIPS Transactions:PartD
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• v.18D no.2
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• pp.143-148
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• 2011

In this paper developed a wearable activity device and algorithm which can be converted into the real-time activity and monitoring by acquiring sensor row data to be occurred when a person is walking by using a tri-axial accelerometer. Test was proceeded at various step speeds such as slow walking, walking, fast walking, slow running, running and fast running, etc. for 36 minutes in accordance with the test protocol after wearing a metabolic test system(K4B2), Actical and the device developed in this study at the treadmill with 59 participants of subjects as its target. To measure the activity of human body, a regression equation estimating the Energy Expenditure(EE) was drawn by using data output from the accelerometer and information on subjects. As a result of experiment, the recognition rate of algorithm being proposed was shown the activity conversion algorithm was enhanced by 1.61% better than the performance of Actical.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.87-93
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• 2012

The purpose of this study suggests a personalized algorithm of physical activity energy expenditure prediction through comparison and analysis of individual physical activity. The research for a 3-axial accelerometer sensor has increased the role of physical activity in promoting health and preventing chronic disease has long been established. 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 10 participants(5 males and 5 females aged between 20 and 30 years). The activities protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.103-111
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• 2011

The research has increased the role of physical activity in promoting health and preventing chronic disease. 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). COUNT method has been proven through experiments of validity Freedson, Hendelman, Leenders, Yngve was implemented by applying the SVM method. A total of 10 participants(5 males and 5 females aged between 20 and 30 years). The activity protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). These activities were repeated four weeks. Customized estimating algorithm for energy expenditure of physical activities were implemented with COUNT and SVM correlation between the data.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.27-33
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• 2011

Estimating algorithm of physical activity energy expenditure and physical activity intensity 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 10 participants(5 males and 5 females aged between 20 and 30 years). The ActiGraph(LLC, USA) and Fitmeter(Fit.life, korea) was positioned anterior superior iliac spine on the body. The activity protocol consisted of three types on treadmill; participants performed three treadmill activity at three speeds(3, 5, 8 km/h). Each activity was performed for 7 minutes with 4 minutes rest between each activity for the steady state. These activities were repeated four weeks. Algorithm for METs, kcal and intensity of activities were implemented with ActiGraph and Fitmeter correlation between the data.

• Journal of Internet Computing and Services
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• v.12 no.3
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• pp.17-26
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts. Sensor data on gait were acquired using a triaxial accelerometer. A test was performed according to a test protocol for different walking speeds, e.g., slow walking, walking, fast walking, slow running, running, and fast running. Each test was carried out for 36 min on a treadmill with the participant wearing an Actical device, and the device developed in this study. The signal vector magnitude (SVM) was used to process the X, Y, and Z values output by the triaxial accelerometer into one representative value. In addition, for accurate step-count detection, we used three algorithms: an heuristic algorithm (HA), the adaptive threshold algorithm (ATA), and the adaptive locking period algorithm (ALPA). The recognition rate of our algorithm was 97.34% better than that of the Actical device(91.74%) by 5.6%.