• 대한인간공학회지
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• 제18권2호
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• pp.103-120
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• 1999

The purpose of this study was to evaluate the manual workload in repetitive wrist and finger motion. To evaluate manual workload, angular displacement of the joint, EMG of the muscle and subjective rating were studied. Both wrist motion and finger motion were studied. A screw-driving task was used for the wrist motion experiment. A keyboard typing task was used for the finger motion experiment. All finger joint angles and wrist angles were measured by an angle-measuring glove($CyberGlove^{TM}$, Virtual Technologies, Inc.). Surface EMG was recorded from FCU muscle and FDS muscle simultaneously with the angle measurement. Subjective ratings of exertion were also recorded using the modified Borg's CR-10 scale. Repetition rates of 0.5, 1, 2 motions per second were used with each task. As a result, manual workload increased with increasing repetitiveness. Peak spectral magnitude and frequency components corresponded closely with joint angular displacement amplitudes and repetition rates. Results of the correlation analysis showed that there were significant correlation among EMG, frequency-weighted motion and subjective measurement. Both EMG and frequency-weighted filtering showed consistent workload estimation with increasing task frequency. Subjective ratings showed slight over-estimation of the workload as the task frequency is increased.

• 한국안전학회지
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• 제19권4호
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• pp.123-128
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• 2004

This study investigated effect of arm posture, repetition of wrist motion and external load on perceived discomfort. The arm postures were controlled by shoulder flexion, elbow flexion, and ist motions such as flexion, extension, radial deviation and ulnar deviation. An experiment was conducted to measure discomfort scores for experimental treatments using the magnitude estimation, in which the L16 orthogonal array was adopted for reducing the size of experiment. The results showed that while the effect of the shoulder flexion, repetition of wrist motion and external load was statistically significant at $\alpha=0.05$or 0.10, that of the elbow and wrist motions was not. Discomfor ratings increased linearly as levels of wrist repetition and external load increased. This implies that the existing posture classification schemes such as OWAS, RULA, which do not properly consider effect of motion repetition and external load, may underestimate postural load. Based on the regression equation for wrist repetition and external load, isocomfort region indicating the region within which discomfort scores were expected to be the same was proposed. It is recommended that when assessing risk of postures or developing new posture classification schemes, motion repetition and external load as well as posture itself be fully taken into consideration for precisely evaluating postural stress.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2009년도 춘계학술대회 논문집
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• pp.73-74
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• 2009

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제6권1호
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• pp.29-36
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• 2017

본 논문에서는 손목형 가속도계를 이용하여 운동 가속도가 거의 없는 식사 행동을 인식하기 위한 방법을 제안한다. 먼저 손목 방향에 작용하는 중력 가속도를 이용하여 중력 방향과 손목 방향 간의 각도를 구하고, 특정 각도 영역에서 첨두값과 첨미값이 존재하는 경우 손목이 왕복하는 동작을 검출한다. 손목 왕복 동작 발생 횟수를 누적하여 그 횟수가 10회 이상일 경우 검출 시점으로 5분 전까지 식사 시간으로 간주하며, 그 지속시간이 7분 이상인 경우에만 식사 시간으로 추론한다. 대학원생 4명으로부터 수집한 2128분 데이터를 통해 식사 시간을 추론한 결과 95.63%의 정확도를 보인다.

• 대한의용생체공학회:의공학회지
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• 제33권2호
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• pp.65-71
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• 2012

In this paper, the Gaussian Mixture Model(GMM) which is very robust modeling for pattern classification is proposed to classify wrist motions using surface electromyograms(EMG). EMG is widely used to recognize wrist motions such as up, down, left, right, rest, and is obtained from two electrodes placed on the flexor carpi ulnaris and extensor carpi ulnaris of 15 subjects under no strain condition during wrist motions. Also, EMG-based feature is derived from extracted EMG signals in time domain for fast processing. The estimated features based in difference absolute mean value(DAMV) are used for motion classification through GMM. The performance of our approach is evaluated by recognition rates and it is found that the proposed GMM-based method yields better results than conventional schemes including k-Nearest Neighbor(k-NN), Quadratic Discriminant Analysis(QDA) and Linear Discriminant Analysis(LDA).

• 전자공학회논문지
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• 제52권8호
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• pp.18-26
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• 2015

올바른 운동량 조절을 위해선 운동중의 심박수 측정이 중요하다. 최근 스마트 디바이스가 활발하게 사용됨에 따라, 운동중의 실시간 심박수 측정에 대한 관심이 급격하게 증가하고 있다. 고강도 운동 중에는 동잡음으로 인하여 손목 밴드 유형의 광혈류 (PPG : photoplethysmography) 측정기 신호로부터 정확한 심박수를 추정하는 것이 매우 어렵다. 본 논문에서는 손목밴드 유형의 광혈류 측정기 신호로부터 정확한 심박수 추정을 위한 효율적인 알고리즘을 제안하였다. 12개의 데이터 세트에 대하여 제안하는 알고리즘을 적용한 결과, 1.38의 분당심박수(BPM) 평균 절대 오차를 기록하였고, 0.9922의 추정 심박수와 실제 심박수간의 Pearson 상관계수를 얻었다. 제안하는 알고리즘은 웨어러블 디바이스에 적합한 빠른 연산속도와 정확한 추정을 가능케 한다.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2000년도 춘계 학술대회 및 국제 감성공학 심포지움 논문집 Proceeding of the 2000 Spring Conference of KOSES and International Sensibility Ergonomics Symposium
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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.

• 산업기술연구
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• 제21권B호
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• pp.265-272
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• 2001

The human arm is modeled by three rigid bodies(the upper arm, the forearm and the hand)with seven degree of freedom(three in the shoulder, two in the elbow and two in the wrist). The objective of this work is to present a method to determine the three-dimensional kinematics of the human elbow joint using a magnetic tracking device. Euler angle were used to determine the elbow flexion-extension, and the pronation-supination. The elbow motion for the various driving conditions is measured through the driving test using a simulator. Discomfort levels of elbow joint motions were obtained as discomfort functions, which were based on subjects' perceived discomfort level estimated by magnitude estimation. The results showed that the discomfort posture of elbow joint motions occurred in the driving motion.

• 전기학회논문지
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• 제61권5호
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• pp.757-762
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• 2012

In this paper, the multi feature extraction algorithm for estimation of wrist movements based on Electromyogram(EMG) is proposed. For the extraction of precise features from the EMG signals, the difference absolute mean value(DAMV), the mean absolute value(MAV), the root mean square(RMS) and the difference absolute standard deviation value(DASDV) to consider amplitude characteristic of EMG signals are used. We figure out a more accurate feature-set by combination of two features out of these, because of multi feature extraction algorithm is more precise than single feature method. Also, for the motion classification based on EMG, the linear discriminant analysis(LDA), the quadratic discriminant analysis(QDA) and k-nearest neighbor(k-NN) are used. We implemented a test targeting twenty adult male to identify the accuracy of EMG pattern classification of wrist movements such as up, down, right, left and rest. As a result of our study, the LDA, QDA and k-NN classification method using feature-set with MAV and DASDV showed respectively 87.59%, 89.06%, 91.75% accuracy.

• 대한임베디드공학회논문지
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• 제17권1호
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• pp.1-7
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• 2022

The number of patients with many complications grows with the increase of aging population. As the elders and severely ill patients spend most of their time in bed, it leads to Pressure Injuries (PI) such as bedsores. Unfortunately, there is no method to automatically detect changes in patient's posture which leads to the need for a caregiver every set of times when the patient needs to be moved. Many studies are conducted to solve this inefficient problem. Yet, these studies require costly devices or use methods that disturb patient's sleeping environment. Those methods are mostly hard to implement in practice due to these reasons. We propose a method to detect posture using a three-axis acceleration sensor from the wrist band. We developed a wearable watch that measures sleep-related data. We analyzed 40 people's sleep data with a wearable module and watch to measure their postures such as supine, left-side, and right-side. Then, we compared the classified posture from the watch with the wearable module and achieved 90% accuracy. Therefore, we concluded that only by using the wearable watch, we can detect the sleeping position without any new equipment or system to diagnose the patients without discomfort during their daily lives.