• 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.

• The Journal of Korean Physical Therapy
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• v.22 no.6
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• pp.59-63
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• 2010

Purpose: Driving a car is an essential component of daily life. For safe driving, each driver must perceive sensory information and respond rapidly and accurately. Brake response time (BRT) is a particularly important factor in the total stopping distance of a vehicle, and therefore is an important factor in traffic accident prevention research. The purpose of the current study was (1) to compare accelerometer. BRTs analyzed by three different methods and (2) to investigate possible correlations between accelerometer-BRTs and foot switch-BRTs, which are measured method using a foot switch. Methods: Eighteen healthy subjects participated in this study. BRT was measured with either a tri-axial accelerometer or a footswitch. BRT with a tri-axial accelerometer was analyzed using three methods: maximum acceleration time, geometrical center, and center of maximum and minimum acceleration values. Results: Both foot switch-BRTs and accelerometer-BRTs were delayed. ANOVA for accelerometer BRTs yielded significant main effects for axis and analysis, while the interaction effect between axis and analysis was not significant. Calculating the Pearson correlation between accelerometer-BRT and foot switch-BRT, we found that maximum acceleration time and center of maximum and minimum acceleration values were significantly correlated with foot switch-BRT (p<0.05). The X axis of the geometrical center was significantly correlated with foot switch-BRTs (p<0.05), but Y and Z axes were not (p>0.05). Conclusion: These findings suggest that the maximum acceleration time and the center of maximum and minimum acceleration value are significantly correlated with foot switch-BRTs.

• Physical Therapy Korea
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• v.23 no.1
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• pp.87-93
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• 2016

Background: Posture balance control is the ability to maintain the body's center of gravity in the minimal postural sway state on a supportive surface. This ability is obtained through a complicated process of sensing the movements of the human body through sensory organs and then integrating the information into the central nervous system and reacting to the musculoskeletal system and the support action of the musculoskeletal system. Motor function, including coordination, motor, and vision, vestibular sense, and sensory function, including proprioception, should act in an integrated way. However, more than half of stroke patients have motor, sensory, cognitive, and emotional disorders for a long time. Motor and sensory disorders cause the greatest difficulty in postural control among stroke patients. Objects: The purpose of this study is to determine the effect of visual and somatosensory information on postural sway in stroke patients and carrying out a kinematic analysis using a tri-axial accelerometer and a quantitative assessment. Methods: Thirty-four subjects posed four stance condition was accepted various sensory information for counterbalance. This experiment referred to the computerized dynamic posturography assessments and was redesigned four condition blocking visual and somatosensory information. To measure the postural sway of the subjects' trunk, a wireless tri-axial accelerometer was used by signal vector magnitude value. Ony-way measure analysis of variance was performed among four condition. Results: There were significant differences when somatosensory information input blocked (p<.05). Conclusion: The sensory significantly affecting the balance ability of stroke patients is somatosensory, and the amount of actual movement of the trunk could be objectively compared and analyzed through quantitative figures using a tri-axial accelerometer for balance ability.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.289-292
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• 2010

Recently human activity recognition using accelerometer has become a prominent research area in proactive computing. In this paper, we present a real-time activity recognition system using a single tri-axial accelerometer. Our system recognizes four primary daily human activities: namely walking, going upstairs, going downstairs, and sitting. The system also computes extra information from the recognized activities such as number of steps, energy expenditure, activity duration, etc. Finally, all generated information is stored in a database as daily log.

• 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.

• PNF and Movement
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• v.11 no.2
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• pp.31-40
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• 2013

Purpose : The purpose of this study was to measure the center of mass in body with stroke patients using a tri-axial accelerometer during walking. Methods : Twenty-eight patients were recruited and divided into two groups for this study. To measure their walking ability, Timed Up & Go (TUG) test and Fucntioanl Gait Assessment (FGA) were conducted and acceleration at rotation of center of mass (COM) in body were measure for each group. Results : In the comparisons between the two groups, the TUG and FGA were not significant differences and acceleration at rotation of COM was not significant differences also. Conclusion : Our research results suggesting that the accelerometer may be used as a testing tool and ongoing assessment tool for stroke patients during effects of intervention in walking.

• 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.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.59-64
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• 2008

D. W. KANG, J. S. CHOI, and G. R. TACK, A Study on Real-Time Sports Activity Classification & Monitoring Using a Tri-axial Accelerometer. Korean Jouranl of Sport Biomechanics, Vol. 18, No. 2, pp. 59-64, 2008. This study was conducted to study the real-time sports activity classification and monitoring using single waist mounted tri-axial accelerometer. This monitoring system detects events of sports activities such as walking, running, cycling, transitions between movements, resting and emergency event of falls. Accelerometer module was developed small and easily attachable on waist using wireless communication system which does not constrain sports activities. The sensor signal was transferred to PC and each movement pattern was classified using the developed algorithm in real-time environment. To evaluate proposed algorithm, experiment was performed with several sports activities such as walking, running, cycling movement for 100sec each and falls, transition movements(sit to stand, lie to stand, stand to sit, lie to sit, stand to lie and sit to lie) for 20 times each with 5 healthy subjects. The results showed that successful detection rate of the system for all activities was 95.4%. In this study, through sports activity monitoring. it was possible to classify accurate sports activities and to notify emergency event such as falls. For further study, the accurate energy consumption algorithm for each sports activity is under development.

• 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.