• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.11a
• /
• pp.1029-1030
• /
• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.985-986
• /
• 2013

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.6
• /
• pp.650-657
• /
• 2013

This study uses a muscle activation sensor and elbow joint model to develop an estimation algorithm for human elbow joint torque for use in a human-robot interface. A modular-type MVS (Muscle Volume Sensor) and calibration algorithm are developed to measure the muscle activation signal, which is represented through the normalization of the calibrated signal of the MVS. A Hill-type model is applied to the muscle activation signal and the kinematic model of the muscle can be used to estimate the joint torques. Experiments were performed to evaluate the performance of the proposed algorithm by isotonic contraction motion using the KIN-COM$^{(R)}$ equipment at 5, 10, and 15Nm. The algorithm and its feasibility for use as a human-robot interface are verified by comparing the joint load condition and the torque estimated by the algorithm.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.8
• /
• pp.870-877
• /
• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.05a
• /
• pp.951-952
• /
• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2013.05a
• /
• pp.983-984
• /
• 2013

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2009.10a
• /
• pp.655-656
• /
• 2009

• Journal of IKEEE
• /
• v.24 no.3
• /
• pp.754-765
• /
• 2020

Currently, Intelligent femoral prostheses that support the corresponding mode in walking and specific movements are being studied. Certain controls such as upstairs, sitting, and standing require a technique to classify control commands based on the user's intention because the mode must be changed before the operation. Therefore, in this paper, we propose a technique that can classify various control commands based on the user's intention in the intelligent thigh prosthesis system. If it is determined that the EMG signal needs to be compensated, the proposed technique compensates the EMG signal using the correlation between the strength and frequency components of the normal EMG signal and the muscle volume estimated by the pressure sensor. Through the experiment, it was confirmed that the user's intention was accurately detected even in the situation where muscle fatigue was accumulated. Improved intention detection techniques allow five control modes to be distinguished based on the number of muscle contractions within a given period of time. The results of the experiment confirmed that 97.5% accuracy was achieved through muscle tone compensation even if the strength of the muscle signal was different from normal due to muscle fatigue after exercise.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.39 no.6
• /
• pp.617-624
• /
• 2015

This paper describes the development of a wearable robot to assist the elbow muscle for use by elderly workers in aging societies. Various previously developed wearable robots have drawbacks in terms of their price, portability, and slow recognition of the wearer's intention. In this paper, emphasis is placed on the following features to minimize these drawbacks. The first feature is that an actuator is attached only at the elbow joint that withstands the highest moment during arm motion to reduce the weight, volume, and price of the robot and increase its practicality. The second is that operation of the wearable robot is divided into two modes, a tracking mode and a muscle strengthening mode, and the robot can automatically switch between these modes by analyzing the wearer's intention through the brachial muscle strength measuring device developed in this study. The assistive performance of the developed wearable robot is experimentally verified by motion tracking experiments without an external load and muscle strengthening experiments with an external load. During the muscle strengthening experiments, the power of the muscle of the upper arm is measured by a commercial electromyography (EMG) sensor. Motion tracking performance at a speed of $120^{\circ}/s$ and muscle assistance of over 60 % were obtained using our robot.

• Advances in nano research
• /
• v.16 no.2
• /
• pp.155-164
• /
• 2024

The rapid evolution of intelligent sports equipment and gadgets has led to the transformation of smartphones into personalized coaching devices. This transformative role is central in today's technologically advanced landscape, addressing the needs of individuals with contemporary lifestyles. The development of intelligent sports gadgets is geared towards elevating overall quality of life by facilitating sports activities, workouts, and promoting health preservation. This categorization yields two primary types of devices: smart sports devices for exercise and smart health control devices, which encompass functionalities such as blood pressure monitoring and muscle volume measurement. Illustrative examples include smart headbands, smart socks, smart wristbands, and smart shoe soles. Significantly, the global market for smart sports devices has garnered substantial popularity among enthusiasts. Moreover, the integration of sensors within these devices has instigated a revolution in group and professional sports, facilitating the calculation of impact intensity and ball speed. The utilization of various types of smart sports equipment has proliferated, encompassing applications in both sports' performance and health monitoring across diverse demographics. This article conducts an assessment of the application of nanotechnology in the continuous modeling of the magnetic electromechanical sensor integrated within smart shoe soles, with a specific emphasis on its implementation in soccer training. The exploration delves into the nuanced intersection of nanotechnology and sports equipment, elucidating the intricate mechanisms that underlie the transformative impact of these advancements.