• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.1
• /
• pp.114-120
• /
• 2018

In this paper we implemented an EMG sensor system for wearable devices to obtain and analyze of EMG signals. The performance of the implemented sensor system is evaluated by the correlation analysis of muscle fatigue and muscle activation to clinical EMG system and compared with power consumption of the measured power of our system and commercial systems. In experiments with biceps and triceps brachii of 5 objects, The correlation values of muscle fatigue and muscle activation between our system and the clinical EMG system is 1.1~1.4 and about 1.0, respectively. And also the power consumption of our system is 25~50% less than that of some commercial EMG sensor systems.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.10 no.2
• /
• pp.155-162
• /
• 2016

The purpose of this study was to determine the walking imbalance using the EMG(electromyogram). To confirm the effectiveness of the proposed encoder and acceleration, EMG sensor based gait imbalance determination system. This experiment was carried out to evaluation with a healthy adult male to 10 people. The Encoder device is attached to the hip and knee joint in order to measure the gait signal. The Accelerometer sensors are attached on the ankle. The EMG sensors are attached on the vastus lateralis and anterior tibialis. SI(Symmetry Index) was used as an index for determining the gait imbalance. To confirm if the judgment has been made correctly, the heel, regarded as the cause of unbalanced ambulation, was adjusted from 0 cm to 6 cm with intervals of 1.5 cm. In the cases of the encoder and the EMG, the difference of 0 cm and 1.5 cm is determined into normal walk but the other difference is distinguished into gait imbalance. In the case of the accelerometer, the difference of 0 cm, 1.5 cm and 3 cm is determined into normal walk but the other difference is distinguished into gait imbalance.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.4
• /
• pp.108-113
• /
• 2011

In this paper, we propose a new method for robot navigation control through EMG and acceleration sensors which is attached to wrist. The method can remote control with intuitive motion like driving a car. It decide to control whether or not through EMG signal processing. And motion inferring through signal processing from acceleration sensor. Inferred motion is mapped to control command such as 'Forward', 'Backward', 'Left', 'Right'. Accuracy of each motions are over 99%. Control is capable naturally without time delay. Entire system has been implemented and we verified its utility through demonstration.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.6 no.2
• /
• pp.43-48
• /
• 2012

In this paper, we present the method of gait phases detection using multi biomedical signals during normal gait. Electromyogram(EMG) signals, muscle of thigh angle measurement device and resistive sensors are used for experiments. We implemented a test targeting five adult male and identified the pattern of EMG signal of normal gait. For acquiring the EMG signal, subjects attached surface Ag/AgCl electrodes to quadriceps femoris, biceps femoris, tibialis anterior and gastrocnemius medialis. Resistance sensors are attached to the heel toe and soles of the each feet for measuring attachment state of between feet and ground. Infrared sensors are attached on the thigh and thigh angle measurement device has the range from flection 25 degrees to extension 20 degrees. The results of this paper, The stance and swing phase could be confirmed during the normal gait and be classified in detail the eight steps.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.10a
• /
• pp.49-50
• /
• 2009

• Proceedings of the IEEK Conference
• /
• 2006.06a
• /
• pp.815-816
• /
• 2006

In this paper, an EMG (Electromyography) sensor for utilizing an EMGl-based HCI are described. The EMG sensor is a dry type and has high gain (1000-10000). Therefore, this sensor can be properly applied to HCI devices using EMG signals without additional amplification circuit.

• Journal of rehabilitation welfare engineering & assistive technology
• /
• v.11 no.4
• /
• pp.371-376
• /
• 2017

In this paper, we propose an algorithm that can recognize the pattern regardless of the sensor position when performing EMG pattern recognition using circular EMG system equipment. Fourteen features were extracted by using the data obtained by measuring the eight channel EMG signals of six motions for 1 second. In addition, 112 features extracted from 8 channels were analyzed to perform principal component analysis, and only the data with high influence was cut out to 8 input signals. All experiments were performed using k-NN classifier and data was verified using 5-fold cross validation. When learning data in machine learning, the results vary greatly depending on what data is learned. EMG Accuracy of 99.3% was confirmed when using the learning data used in the previous studies. However, even if the position of the sensor was changed by only 22.5 degrees, it was clearly dropped to 67.28% accuracy. The accuracy of the proposed method is 98% and the accuracy of the proposed method is about 98% even if the sensor position is changed. Using these results, it is expected that the convenience of the users using the circular EMG system can be greatly increased.

• Science of Emotion and Sensibility
• /
• v.13 no.1
• /
• pp.207-214
• /
• 2010

The aim of this study was to investigate upper and lower limb muscle activity using EMG(electromyogram) sensors while walking and identify normal gait pattern using FSR(force sensing resistor) sensor. Fifteen college students participated in this study and their EMG and FSR signal were measured during stopping and walking trials. EMG signals from upper(pectoralis major and trapezius) and lower limbs(rectus femoris, biceps femoris, vastus medialis, vastus lateralis, semimembranosus, semitendinosus, soleus, peroneus longus, gastrocnemius medialis, and gastrocnemius lateralis) were obtained using the surface electrodes. FSR measured pressures on 8 areas of the sole of the foot during walking. EMG results showed that all muscle activities except for vastus lateralis and semimembranosus during walking had higher amplitudes than stopping. Additionally, muscle activities associated with stance and swing phase during walking were identified. Results on FSR showed that stance and swing phases were detected by FSR signals during a gait cycle. Eight gait phases－initial contact, loading response, mid stance, terminal stance, pre swing, initial swing, mid swing, and terminal swing- were classified.

• The Journal of Korea Robotics Society
• /
• v.2 no.3
• /
• pp.205-211
• /
• 2007

In this paper, the prototype of surface EMG (ElectroMyoGram) sensor is developed for the robotic rehabilitation applications, and the developed sensor is composed of the electrodes, analog signal amplifiers, analog filters, ADC (analog to digital converter), and DSP (digital signal processor) for coding the application example. Since the raw EMG signal is very low voltage, it is amplified by about one thousand times. The artifacts of amplified EMG signal are removed by using the band-pass filter. Also, the processed analog EMG signal is converted into the digital form by using ADC embedded in DSP. The developed sensor shows approximately the linear characteristics between the amplitude values of the sensor signals measured from the biceps brachii of human upper arm and the joint angles of human elbow. Finally, to show the performance of the developed EMG sensor, we suggest the application example about the real-time human elbow motion acquisition by using the developed sensor.

• Journal of Sensor Science and Technology
• /
• v.25 no.5
• /
• pp.365-370
• /
• 2016

The study about VR (Virtual Reality) has been done from the 1960s, but technical limits and high cost made VR hard to commercialize. However, in recent, high resolution display, computing power and 3D sensing have developed and hardware has become affordable. Therefore, normal users can get high quality of immersion and interaction. However, HMD devices which offer VR environment have high latency, so it disrupts the VR environment. People are usually sensitive to relative latency over 20ms. In this paper, as adding the Electromyogram (EMG) sensors to typical IMU sensor only system, the latency reduction method is proposed. By changing software and hardware components, some cases the latency was reduced significantly. Hence, this study covers the possibility and the experimental verification about EMG sensors for reducing the latency.