• 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%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1810-1814
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• 2017

This paper develops an approach to the algorithm of Gait pattern Analysis and step measurement with Multi-Pressure Sensors. The process of gait consists of 8 steps including stance and swing phase. As 3 parts of foot is supporting most of human weight, multiple pressure sensors are attached on the parts of foot: forefoot, big toe, heel. As 3 parts of foot is supporting most of human weight, multiple pressure sensors are attached on the parts of foot: forefoot, big toe, heel. normal gait proceed from heel, forefoot and big toe over time. While normal gait proceeds, values of heel, forefoot and big toe can be changed over time. So Each values of pressure sensors over time could discriminate whether it is normal or abnormal gait. Measuring Device consists of non-inverting amplifiers and low pass filter. Through timetable of values, normal gait pattern can be analyzed, because of supported weight of foot. Also, the peak value of pressure can judge whether it is walking or running. While people are running, insole of shoes is floating in the air on moment. Using this algorithm, gait analysis and step count can be measured.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.420-422
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• 2006

This paper proposes a new method to count the number of steps for personal navigation systems. The proposed method resolves the mis-counting problem caused by the variation of the ground and walking behavior. To this end, a 2-axis accelerometer is utilized and a reliable step counting algorithm is developed. Experimental test was carried out to show the effectiveness of the proposed method. Test results show that the proposed method gives a robust performance for several types of ground and walking behavior.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.2
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• pp.127-137
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• 2011

We have developed a wearable device that can convert sensor data into real-time step counts and activity levels. 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 a portable gas analyzer (K4B2), 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). A regression equation estimating the energy expenditure (EE) was derived by using data from the accelerometer and information on the participants. The recognition rate of our algorithm was 97.34%, and the performance of the activity conversion algorithm was better than that of the Actical device by 1.61%.

• Journal of KIISE
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• v.44 no.4
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• pp.374-382
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• 2017

As the walking exercise is emphasized in personalized healthcare, numerous services demand walking information. Along with the propagation of smartphones nowadays, many step-counter applications have been released. But these applications are error-prone to abnormal movements such as simple shaking or vibrations; also, different step counts are shown when the phone is positioned in different locations of the body. In this paper, the proposed method accurately counts the steps regardless of the smartphone position by using an accelerometer and a proximity sensor. A threshold is set on each of the six positions to minimize the error of undetection and over-detection, and the cut-off section is set to eliminate any noise. The test results show that the six position type were successfully identified, and through a comparison experiment with the existing application, the proposed technique was verified as superior in terms of accuracy.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.8 no.2
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• pp.133-138
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• 2014

In this paper, a simple but efficient gait recognition method using spatial-temporal silhouette analysis is proposed. For each image sequence, a background subtraction algorithm and a PBAS(pixel based adaptive segmenter) procedure are first used to segment the moving silhouettes of a walking figure. Then, to identify people, the step count and stride length of walking figure is obtained in silhouette images. Experimental results on a CASIA dataset including 124 subjects demonstrate the validity of the proposed method. Also, the proposed system are believed to have a sufficient feasibility for the application to gait recognition.

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

• Archives of Obesity and Metabolism
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• v.1 no.2
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• pp.74-77
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• 2022

Treatment of obesity includes diet therapy, exercise therapy, cognitive behavioral therapy, drug therapy, and bariatric surgery. Most obese patients lose weight by combining diet, exercise, cognitive behavioral therapy or medication. But, in some cases, only one of these treatments is preferred. A 56-year-old male patient had a body mass index (BMI) of 33.1 kg/m² and a waist circumference of 108 cm. He had been treated for hypertension; diabetes and dyslipidemia were diagnosed but not treated. However, at the initial visit to treat obesity, he was diagnosed with type 2 diabetes mellitus and dyslipidemia again. So he decided to treat these two diseases with drugs first and modify his lifestyle. He started walking more than 20,000 steps every day and then he really walked about 15,000 steps every day during 5 months, although diet calorie or alcohol drinking amount was not significantly decreased. After about 6 months, the patient's weight decreased by 10.1 kg, the BMI decreased by 4.1 kg/m², the waist circumference decreased by 10 cm, the glycated hemoglobin (HbA1c) decreased by 4.59%, the visceral fat area decreased by 115 cm², and the subcutaneous fat decreased by 38 cm². As a result of body composition analysis, muscle mass increased by 1.2 kg, and the percentage of body fat decreased by 10.4%. The walking exercise does not have any space restrictions and has high accessibility by using a mobile phone app. Therefore, considering the patient's situation, it would be better to treat obese patients by first recommending walking exercises and increasing the number of steps to lose weight and improve the comorbidities.

• Journal of the Korea Society of Computer and Information
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• v.17 no.8
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• pp.107-114
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• 2012

We have developed a textile capacitive pressure sensor for smart gait analysis. The proposed system can convert sensor signal into step counts and pressure levels by different posture. To evaluate the performance of insole type textile capacitive sensor, we measured capacitance change by increment of weights from 10 kg to 100 kg with 10 kg increment using M1 class rectangular weights (four 20 kg weights and two 10 kg weights) which have ${\pm}10%$ tolerance. The result showed non-linearity characteristic of a general capacitive pressure sensor. The test was performed according to a test protocol for four different postures (sitting, standing, standing on a left leg and standing on a right leg) and different walking speeds (1 km/h and 4 km/h). Five healthy male subjects were participated in each test. As we expected, the pressure level was changed by pressure distribution according to posture. Also, developed textile pressure sensor showed higher recognition rate (average 98.06 %) than commercial pedometer at all walking speed. Therefore, the proposed step counts and posture monitoring system using conductive textile capacitive pressure sensor proved to be a reliable and useful tool for monitoring gait parameters.

• Journal of Life Science
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• v.19 no.10
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• pp.1368-1373
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• 2009

The purpose of this study was to assess the influence of waist size on the reliability and validity of pedometers to count steps in children and youth. The participants for this study were 20 children and youth, composed of 14 Hispanic and 6 Caucasian children. Ten children and youth had waist circumferences greater than the $85^{th}$ percentile (Body Mass Index (BMI)=$28.91\pm3.07$), and 10 children and youth had waist circumferences smaller than the $50^{th}$ percentile (BMI=$18.05\pm1.55$). To examine pedometer reliability, each child completed 3 ascent and descent trials up a set of 15 stairs while wearing a Yamax SW-701 pedometer. The main effect of trials was not statistically significant for the stair ascent trials F (2, 36)=2.575 or for the descent trials F (2, 36)=0.235. The trial by group interaction was also not statistically significant. To examine the influence of waist circumference on the validity of the pedometer in counting walking steps at a self-selected walking pace, the children and youth in the two groups completed a 400-m course. The main effect on the groups was statistically significant, F (1, 18)=7.489. The main effect of counting techniques was not statistically significant, F (1, 18)=2.983 (hand-counted vs. pedometer counted). Overall, the trial and trial by group interaction comparisons for the 400-m walk were not statistically significant, suggesting that the pedometer was equally valid as a tool for assessing walking steps in high waist circumference (HWC) and low waist circumference (LWC) in children and youth.