• Proceedings of the IEEK Conference
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• 2003.11b
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• pp.235-238
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• 2003

Conventional stride predictor is useful for predicting data values which vary by a constant value. However, when the data values of shift, multiplication, and division instructions are predicted, the stride predictor can't show the best performance. Thus, we propose predictor using stride and shift to improve predictability. The predictor using stride and shift takes advantage of shift values as well as stride values, so that the overall coverage of prediction increases.

• Korean Journal of Applied Biomechanics
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• v.27 no.1
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• pp.53-58
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• 2017

Objective: The purpose of this study was to compare the accuracy of stride time and stride length provided by a commercial APDM inertial sensor system (APDM) with the results of three dimensional motion capture system (3D motion) during treadmill walking. Method: Five healthy men participated in this experiment. All subjects walked on the treadmill for 3 minutes at their preferred walking speed. The 3D motion and the APDM were simultaneously used for extracting gait variables such as stride time and stride length. Mean difference and root mean squared (RMS) difference were used to compare the measured gait variables from the two measurement devices. The regression equation derived from the range of motion of the lower limb was also applied to correct the error of stride length. Results: The stride time extracted from the APDM was almost the same as that from the 3D motion (the mean difference and RMS difference were less than 0.0001 sec and 0.0085 sec, respectively). For stride length, mean difference and RMS difference were less than 0.1141 m and 0.1254 m, respectively. However, after correction of the stride length error using the derived regression equation, the mean difference and the RMS difference decreased to 0.0134 m and 0.0556 m or less, respectively. Conclusion: In this study, we confirmed the possibility of using the temporal variables provided from the APDM during treadmill walking. By applying the regression equation derived only from the range of motion provided by the APDM, the error of the spatial variable could be reduced. Although further studies are needed with additional subjects and various walking speeds, these results may provide the basic data necessary for using APDM in treadmill walking.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.331-336
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• 2016

Multi-bit tries have been proposed to improve the search performance of a binary trie by providing flexibility in stride values, which identify the number of bits examined at a time. However, constructing a variable-stride multi-bit trie is challenging since it is not easy to determine a proper stride value that satisfies the required performance at each node. The aim of this paper is to identify several desired characteristics of a trie for IP address lookup problems, and to propose a multi-stride decision trie that has these characteristics. Simulation results using actual routing sets with about 30,000 to 220,000 prefixes show that the proposed multi-stride decision trie has the desired characteristics and achieves IP address lookup using 33% to 47% of the 2-bit trie in the average number of node accesses, while requiring a smaller amount of memory.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.35-43
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• 2013

Exercise such as walking is helpful to manage one's own weight and to counter life habit diseases such as obesity. Calorie consumption is usually calculated based on the distance walked. One way to measure the distance is by using steps and stride length. Most pedometers, including some applications in smart devices, are inaccurate, because they use a common value as the average stride length, even though each person has a different stride length. Moreover, the stride length differs depending on the walking pace, which will further increase the error. To address this, in this paper, I classify paces into three categories. Following that, I introduce a customized measurement of stride length, which is calculated based on the stride length corresponding to each pace category after obtaining x, y, z values from a 3-axis accelerometer in the smart device. In addition to this, I developed an application running on the smart device designed for the proposed measurement of stride. I have conducted three experiments for the assessment of the proposed measurement. In conclusion, I confirmed the effectiveness of this system.

• PNF and Movement
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• v.19 no.2
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• pp.261-267
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• 2021

Purpose: This study aimed to investigate changes in gait variables depending on whether a task was performed using a smartphone while walking on a ramp. Methods: The participants of this study were 41 college students attending U University located in Gyeongju City, Gyeongsangbuk-do. In this study, gait variables were measured during ramp gait while using a smartphone to perform a task and during ramp gait without performing such tasks. In other words, four walking conditions were used: 1) walking up a ramp, 2) walking up a ramp while using a smartphone to perform a task, 3) walking down a ramp, and 4) walking down a ramp while using a smart phone to perform a task. Gait variables were measured using a gait analysis tool (Legsys; BioSensics, USA), and stride time, stride length, stride velocity, cadence, and double support were analyzed. The order of measurements was randomized to control for order effects due to repeated measurements. Results: The comparative analysis of gait variables according to the presence or absence of smartphone use during ramp gait showed that there were significant differences in stride time, stride length, and stride velocity during both ramp ascent and ramp descent (p < 0.05). In both ramp ascent and ramp descent, stride time increased when walking using a smartphone, compared to when walking without using a smartphone (p < 0.05). However, in both ramp ascent and ramp descent, stride length and stride velocity were decreased when walking using a smartphone compared to when walking without using a smartphone (p < 0.05). Conclusion: The study results showed that the use of a smartphone during walking can affect safety. Therefore, it is necessary to improve the awareness of risks associated with walking while using a smartphone, and further research needs to be conducted in various environments and with different ramps.

• The Journal of Korean Physical Therapy
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• v.29 no.6
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• pp.303-306
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• 2017

Purpose: Gait variability is defined as the intrinsic fluctuations which occur during continuous gait cycles. Increased gait variability is closely associated with increased fall risk in older adults. This study investigated the influence of attention-demanding tasks on gait variability in elderly healthy adults. Methods: We recruited 15 healthy elderly adults in this study. All participants performed two cognitive tasks: a subtraction dual-task (SDT) and working memory dual-task (WMDT) during gait plus one normal gait. Using the $LEGSys^+$ system, we measured the coefficient of variation (CV %=$100{\times}$[standard deviation/mean]) for participants' stride time, stride length, and stride velocity. Results: SDT gait showed significant increment of stride time variability compared with usual gait (p<0.05), however, stride length and velocity variability did not difference between SDT gait and usual gait (p>0.05). WMDT gait showed significant increment of stride time and velocity variability compared with usual gait (p<0.05). In addition, stride time variability during WMDT gait also significantly increased compared with SDT gait (p<0.05). Conclusion: We reported that SDT and WMDT gait can induce the increment of the gait variability in elderly adults. We assume that attention demanding task based on working memory has the most influence on the interference between cognitive and gait function. Understanding the changes during dual task gait in older ages would be helpful for physical intervention strategies and improved risk assessment.

• Journal of Internet Computing and Services
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• v.23 no.6
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• pp.27-37
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• 2022

Virtual private network (VPN) services are used in various environments related to national security, such as defense companies and defense-related institutions where digital communication environment technologies are diversified and access to network use is increasing. However, the number of cyber attacks that target vulnerable points of the VPN has annually increased through technological advancement. Thus, this study identified security requirements by performing STRIDE threat modeling to prevent potential and new vulnerable points that can occur in the VPN. STRIDE threat modeling classifies threats into six categories to systematically identify threats. To apply the proposed security requirements, this study analyzed functions of the VPN and formed a data flow diagram in the VPN service process. Then, it collected threats that can take place in the VPN and analyzed the STRIDE threat model based on data of the collected threats. The data flow diagram in the VPN service process, which was established by this study, included 96 STRIDE threats. This study formed a threat scenario to analyze attack routes of the classified threats and derived 30 security requirements for each element of the VPN based on the formed scenario. This study has significance in that it presented a security guideline for enhancing security stability of the VPN used in facilities that require high-level security, such as the Ministry of National Defense (MND).

• Korean Journal of Applied Biomechanics
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• v.17 no.4
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• pp.1-8
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• 2007

The purpose of this study was to investigate the difference in the stride-to-stride variability between two treadmill conditions; traditional treadmill and special treadmill whose speed can be adjusted automatically by subject's walking speed. Eight male subjects (25.1 years, 172.7 cm, 66.6 kg) were participated in treadmill walking experiment. First, preferred walking speed (PWS) of each subject was determined. Second, each subject performed walking experiment with fixed PWS condition and with free PWS condition for 10 minutes. 3D motion capture system (Motion analysis Corp., USA) with 6 cameras was used to collect motion data with sampling frequency of 120Hz. Temporal and spatial variables for stride-to-stride variability were calculated. Coefficient of variance (CV) which quantifies the amount of variability and Detrended Fluctuation Analysis (DFA) which explains the structure (self-similarity) of the variability were used for analysis. Results showed that the amount of variability during free PWS condition was greater than that of fixed PWS condition. DFA results showed that there was a statistical difference between two treadmill conditions for the variables of step length, stance time, and double support time. From these results, it is possible that traditional treadmill study might give incorrect conclusion about gait variability study. Further study is necessary to clarify these matters by considering the number of subjects, experimental time, and gait variables for the study of stride-to-stride variability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2007.06a
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• pp.215-216
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• 2007

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.6
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• pp.1116-1122
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• 2008

Since conventional methods for measuring stride length(distance) are many weaknesses, optical methods have been developed to measure stride length(distance) of human pedestrians. IR(Infrared) elements and Power LED(Light Emitting Diode) with two types of lens were used to correlate detected light intensity with stride length(distance). The suggested methods in this study are simple, convenient, and cost effective. The results can be used to analyze walking patterns of normal and disabled men, and to monitor the recovering processes of the disabled patients.