• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2209-2212
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• 2003

In this paper, we propose the method to extract the important frequency bands from the EMG signal, and for generation of feature vector using the important frequency bands. The EMG signal is measured with 4 sensor and is recorded as 4 channel’s time series data. The same frequency bands from 4 channel’s frequency components are selected as the important frequency bands. The feature vector is calculated by the function formed using the combination of selected same important frequency bands. The EMG signals acquired from seven wrist motion type are recognized by changing into the feature vector formed. Then, the extraction and generation is performed by using the double combination of the genetic algorithm (GA) and the neural network (NN). Finally, in order to illustrate the effectiveness of the proposed method, computer simulations are done.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.109-112
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• 2000

The purpose of the present study is to evaluate the basic upper-limb involved in products manipulation. Upper-limb muscular deformations and electromyography (EMG) measurements are used as indexes for estimated motion: hand opening and closing, wrist extending and flexing, pronation and supination, grasping conditions. Measured values are analyzed by multivariate analysis and a regression equation is obtained for estimating the characteristics of upper-limb performance. Muscular deformation is defined as a change in shape, such as a pressure changes when the hand or wrist moves. hand opening and closing can be discriminated at a higher percentage of accuracy by muscular deformation data than by EMG data. Muscular deformation measurements using air-pack pressure sensors were verified to be effective in motion estimation applications.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.8
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• pp.769-776
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• 2007

This paper proposes the human arm motion tracking algorithm based on the signal processing for surface EMG (electromyogram) sensors attached on both upper arm and shoulder. The signals acquired by using surface EMG sensors are processed with choosing the maximum in a short period, taking the absolute value, and filtering noises out with a low-pass filter. The processed signals are directly used for the motion generation of virtual arm in real time simulator. The virtual arm of simulator has two degrees of freedom and complies with the flexion and extension motions of elbow and shoulder. Also, we show the validity of the suggested algorithms through the experiments.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.05a
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• pp.194-195
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• 2015

Methods for measuring the physical workload of construction workers are classified into posture assessment techniques (i.e., OWAS, RULA, etc.) and physiological measurement techniques (i.e., EMG, heart rate, etc.). The one does not quantify the workload on a specific body part of a worker by considering the weight of the hand tools or materials on hand and time for holding a particular posture. This paper presents a procedure for evaluating a physical demand using the electromyography (EMG) sensor. This study compares the EMG measurement and the posture assessment. The case study is carried out on a masonry operation.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.2
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• pp.75-84
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• 2013

In this study, the patterns and characteristics according to gait cycle were analyzed using to EMG signals during walking, and analyzed in the time domain and frequency domain. The experiments was performed divide to level-ground walking and stair walking, and gait cycle was analysis by stance and swing. In the sagittal plane by using the tilt sensor measures the angle of the lower leg, and EMG was measured from the quadriceps and biceps femoris. The tilt of the lower leg was showed the biggest tilt at HS, and showed lowest value at TO. All in walking according to the gait cycle IEMG showed a specific pattern, and is expected useful to determine the gait cycle and kind. In the frequency domain analysis was using STFT on able to frequency analysis according to time, and using the tilt sensor was identify gait cycle. We analyzed also spectrum of the results of the STFT in all gait types, and recognized that stance had broad bandwidth than that of swing. Through this study, it was confirmed the possibility of judgment and analysis of the gait cycle using EMG and the tilt in the sagittal plane of the lower leg. When used it, can improve the quality of life of amputation patients

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.3
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• pp.176-181
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• 2016

In this paper, the autonomous mobile robot control system for detecting fire was proposed using the wearable device based on EMG(Electromyogram) signal. Myo armband is used for detecting the user's EMG signal. The gesture was classified after sending the data of EMG signal to a computer using Bluetooth communication. Then the robot named 'uBrain' was implemented to move by received data from Bluetooth communication in our experiment. 'Move front', 'Turn right', 'Turn left', and 'Stop' are controllable commands for the robot. And if the robot cannot receive the Bluetooth signal from a user or if a user wants to change manual mode to autonomous mode, the robot was implemented to be in the autonomous mode. The robot flashes the LED when IR sensor detects the fire during moving.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.313-315
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• 2004

Urodynamic study system is widely used for neurogenic bladder patients in various clinical setting. Generally they include 2 pressure sensors from bladder and rectum, and 1 EMG sensor. The rectal pressure catheter is often the source of data error because of gas passage and the fall out of the catheter from anus, and source of discomfort in ambulatory urodynamic system. This study is to design and calibrate the ambulatory digital urodynamic study system that can discard the rectal pressure catheter, which can make patients more comfortable and doctors can get more physiologic data. As a first step, we compared our new system with Dantec $Duet^{(r)}$ urodynamic system (Dantec, Denmark) and wanted to see the possibility of our new system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.9
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• pp.5440-5445
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• 2014

Many studies have examined robot control using human bio signals but complicated signal processing and expensive hardware are necessary. This study proposes a method to recognize a human's hand motion using a low-cost EMG sensor and Flex sensor. The method to classify movement of the hand and finger is determined from the change in output voltage measured through MCU. The analog reference voltage is determined to be 3.3V to increase the resolution of movement identification through experiment. The robotic hand is designed to realize the identified movement. The hand has four fingers and a wrist that are controlled using pneumatic cylinders and a DC servo motor, respectively. The results show that the proposed simple method can realize human hand motion in a remote environment using the fabricated robotic hand.

• Journal of Sensor Science and Technology
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• v.16 no.5
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• pp.369-376
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• 2007

Recently, obesity that is increasingly becoming a major cause of various diseases is emerging as a serious social problem. In order to solve this problem, the necessity of measurement systems for overweight management has increased. This paper is a study on the measurement system for obesity management that can offer right medical services everywhere and allways by analyzing EMG (electromyograph) of the abdomen and then checking one's health state. For analyzing EMG signals of the abdomen, algorithms for energy detection, signal feature extraction, classification and recognition are presented. This paper proposes a system that provides an appropriate an estimation on the health status by evaluating the obesity degree and muscular strength of the abdomen through the system applying these algorithms.

• Journal of International Academy of Physical Therapy Research
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• v.10 no.4
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• pp.1868-1872
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• 2019

Background: Lower trapezius muscle function is important for the prevention and treatment of shoulder injuries. However, scapular posterior tilt movement has been overlooked in lower trapezius strengthening exercise programs. Objective: To examine the effects of prone arm lifting with scapular posterior tilt (PALSPT) on trapezius muscles. Design: Crossover study Methods: 17 healthy males were recruited for participation in this study. Participants performed backward rocking diagonal arm lifting (BRDAL) and PALSPT. To train participants in scapular posterior tilt movements for PALSPT, visual biofeedback of scapular movements was provided using a motion sensor. Electromyography (EMG) activities of the middle and lower trapezius were recorded using a surface EMG system. Differences in middle and lower trapezius muscle activity between BRDAL and PALSPT exercises were analyzed. Results: Lower trapezius muscle activity was significantly greater during PALSPT than during BRDAL (p=.006). Although greater EMG activity was observed in the middle trapezius during PALSPT than during BRDAL, this difference was not significant (p=.055). Conclusions: The results of the present study indicate that scapular posterior tilt movements must be considered in lower trapezius muscle strengthening programs.