• 한국정밀공학회지
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• 제26권5호
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• pp.142-149
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• 2009

This paper proposes and demonstrates novel flexible contact force sensing devices for 3-dimensional force measurement. To realize the sensor, polyimide and polydimethylsiloxane are used as a substrate, which makes it flexible. Thin-film metal strain gauges, which are incorporated into the polymer, are used for measuring the three-dimensional contact forces. The force sensor characteristics are evaluated against normal and shear load. The fabricated force sensor can measure normal loads up to 4N. The sensor output signals are saturated against load over 4N. Shear loads can be detected by different voltage drops in strain gauges. The device has no fragile structures; therefore, it can be used as a ground reaction force sensor for balance control in humanoid robots. Four force sensors are assembled and placed in the four corners of the robot's sole. By increasing bump dimensions, the force sensor can measure load up to 20N. When loads are exerted on the sole, the ground reaction force can be measured by these four sensors. The measured forces can be used in the balance control of biped locomotion system.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 추계학술대회 논문집
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• pp.377-378
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• 2009

• 한국정밀공학회지
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• 제32권3호
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• pp.285-292
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• 2015

This paper presents a wireless ground reaction force (GRF) sensing system for ambulatory GRF recording. The system is largely divided into three parts: force sensing modules based on optical sensor, outsole type frame, and embedded system for wireless communication. The force sensing module has advantages of the low height, robustness to the moment interference, and stable response in long term use. In simulation study, the strain and stress properties were examined to satisfy the requirements of the GRF sensing system. Four sensing modules were mounted on the toe, ball, and heel of foot shaped frame, respectively. The GRF signals were extracted using Micrpcontroller unit and transferred to the smart phone via Bluetooth communication. We measured the GRF during the normal walking for the validation of the continuous recording capability. The recorded GRF was comparable to the off the shelf stationary force plate.

• 한국전문물리치료학회지
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• 제15권1호
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• pp.1-11
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• 2008

The purpose of this study was to assess the influence of two shoe size conditions on foot pressure, ground reaction force (GRF), and lower extremity muscle fatigue. Seven healthy men participated. They randomly performed walking and running in two different conditions: proper shoe size and 10 mm greater than proper shoe size. Peak foot pressure, and vertical, anterior and mediolateral force components were recorded with the Parotec system and Kisler force platform. To assess fatigue, the participants performed treadmill running for twenty-five minutes twice, each time wearing a different shoe size. Surface electromyography was used to confirm localized muscle fatigue using power spectral analysis of four muscles (tibialis anterior, gastrocnemius medialis, rectus femoris, and biceps femoris). The results were as follows: 1) In walking conditions, there was a significantly higher peak pressure in the 10 mm greater than proper shoe size insole sensor 1, 2, 14, and 18 (p<.05). 2) In running conditions, there was a significantly higher peak pressure in the 10 mm greater than proper shoe size insole sensor 5, 14, and 15 (p<.05). 3) In walking conditions, there was a significantly higher first maximal vertical GRF in the 10 mm greater than proper shoe size (p<.05). 4) In running conditions, no GRF components were significantly different between each shoe size condition (p>.05). 5) Muscle fatigue indexes of the tibialis anterior and rectus femoris were significantly increased in the 10 mm greater than proper shoe size condition. These results indicate that wearing shoes that are too large could further exacerbate the problems of increased foot pressure, vertical GRF, and muscle fatigue.

• 한국정밀공학회지
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• 제25권7호
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• pp.79-86
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• 2008

This paper describes the development of wearing intelligent shoe system to measure applied forces and moments (ground reaction forces and moments) on the soles of feet during human walking. In order to walk safely, robot must get the intelligent feet with 6-axis force/moment sensors (Fx sensor (x-direction force sensor), Fy sensor, Fz sensor, Mx sensor (Mx : x-direction moment sensor), My sensor, and Mz sensor) and detect the forces and moments data from the sensors. And the feet must be controlled with the data and controllers. While a human is walking, the forces and moments should be measured and analyzed for robot's intelligent feet. Therefore, the wearing intelligent shoe system should be developed. In this paper, four 6-axis farce/moment sensors and two high speed measuring devices were designed and fabricated, and the wearing intelligent shoe system was made using these. The characteristic tests of the wearing intelligent shoe system were performed, and the forces and moments were detected using it.

• 한국정밀공학회지
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• 제32권12호
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• pp.1065-1072
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• 2015

This paper is about the development of an insole sensor system that can determine the model of an exoskeleton robot for lower limb that is a multi-degree of freedom system. First, the study analyzed the kinematic model of an exoskeleton robot for the lower limb that changes according to the gait phase detection of a human. Based on the ground reaction force (GRF), which is generated when walking, to proceed with insole sensor development, the sensing type, location, and the number of sensors were selected. The center of pressure (COP) of the human foot was understood first, prior to the development of algorithm. Using the COP, an algorithm was developed that is capable of detecting the gait phase with small number of sensors. An experiment at 3 km/h speed was conducted on the developed sensor system to evaluate the developed insole sensor system and the gait phase detection algorithm.

• 대한기계학회논문집A
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• 제33권11호
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• pp.1326-1331
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• 2009

The sensor data measured by the legged robot are used to recognize the physical environment or information that controls the robot's posture. Therefore, a robot's ambulation can be advanced with the use of such sensing information. For the precise control of a robot, highly accurate sensor data are required, but most sensors are expensive and are exposed to excessive load operation in the field. The seriousness of these problems will be seen if the prototype's practicality and mass productivity, which are closely related to the unit cost of production and maintenance, will be considered. In this paper, the use of a virtual sensor technology was suggested to address the aforementioned problems, and various ways of applying the theory to a walking robot obtained through training with an actual sensor, and of various hardware information, were presented. Finally, the possibility of the replacement of the ground reaction force sensor of legged robot was verified.

• 재활복지공학회논문지
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• 제11권1호
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• pp.81-89
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• 2017

본 논문은 대퇴절단 환자의 다양한 환경에서의 보행을 가능하게 하는 지능형 의족의 보행노면 및 보행단계 판별 기법을 제안한다. 제안하는 보행모드 변경 기법은 스트레인게이지 센서 만으로 보행노면 및 보행단계 판별이 가능한 단일 센서 기반의 알고리즘으로 기존 지능형 의족의 다중센서 기반 알고리즘의 단순화와 의족 시스템의 저가화가 가능하게 고안하였다. 보행노면 판별 알고리즘을 위해 정상인의 보행 중 발생하는 지면반발력의 특징을 분석하여 보행단계 세분화와 보행노면 검출 조건을 정의하였고, 대퇴절단 환자와 유사한 환경에서의 보행 실험을 위해 보행분석 장치를 제작하였다. 정의된 검출 조건과 제작된 기구를 통해 논문의 효용성 검증을 진행하였으며, 정상인 대상의 실험결과 단일 센서 기반 알고리즘의 정확도는 약 95%를 나타냈다. 제안하는 단일 센서 기반의 알고리즘을 통해 지능형 의족 시스템의 저가화가 가능할 것으로 판단되며 사용자가 직접 보행노면 상태를 파악하고 보행모드를 전환하는 수동 보행모드 변경 방식에서 벗어나 의족이 현재 보행 노면 상태를 파악하고 상황에 맞는 보행모드를 전환하는 자동보행 모드 변경이 가능할 것으로 확인되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2627-2642
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• 2023

Parkinson's disease (PD) is a typical, chronic neurodegenerative disease involving the concentration of dopamine, which can disrupt motor activity and cause different degrees of gait disturbance relevant to PD severity in patients. As current clinical PD diagnosis is a complex, time-consuming, and challenging task that relays on physicians' subjective evaluation of visual observations, gait disturbance has been extensively explored to make automatic detection of PD diagnosis and severity rating and provides auxiliary information for physicians' decisions using gait data from various acquisition devices. Among them, wearable sensors have the advantage of flexibility since they do not limit the wearers' activity sphere in this application scenario. In this paper, an attention-based temporal network (ATN) is designed for the time series structure of gait data (vertical ground reaction force signals) from foot sensor systems, to learn the discriminative differences related to PD severity levels hidden in sequential data. The structure of the proposed method is illuminated by Transformer Network for its success in excavating temporal information, containing three modules: a preprocessing module to map intra-moment features, a feature extractor computing complicated gait characteristic of the whole signal sequence in the temporal dimension, and a classifier for the final decision-making about PD severity assessment. The experiment is conducted on the public dataset PDgait of VGRF signals to verify the proposed model's validity and show promising classification performance compared with several existing methods.

• 로봇학회논문지
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• 제17권3호
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• pp.296-302
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• 2022

This paper proposes a center of plantar foot pressure (CoP) trajectory estimation method based on Gaussian process regression, with the aim to show robust results regardless of the regions and numbers of FSRs of the insole sensor. This method can bring an interpolation between the measurement points inside the wearable insole sensor, and two experiments are conducted for performance evaluation. For this purpose, the input data used in the experiment are generated in three types (13 FSRs, 8 FSRs, 5 FSRs) according to the regions and numbers of FSRs. First, the estimation results of the CoP trajectory are compared using Gaussian process regression and weighted mean. As a result of each method, the estimation results of the two methods were similar in the case of 13 FSRs data. On the other hand, in the case of the 8 and 5 FSRs data, the weighted mean varies depending on the regions and numbers of FSRs, but the estimation results of Gaussian process regression showed similar results in spite of reducing the regions and numbers. Second, the estimation results of the CoP trajectory based on Gaussian process regression during several gait cycles are analyzed. In five gait cycles, the previous cycle and the current estimation results are compared, and it was confirmed that similar trajectories appeared in all. In this way, the method of estimating the CoP trajectory based on Gaussian process regression showed robust results, and stability was confirmed by yielding similar results in several gait cycles.