• Journal of the Ergonomics Society of Korea
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• v.18 no.2
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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.

• Journal of the Korean Society of Safety
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• v.19 no.4 s.68
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.73-74
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• 2009

• KIPS Transactions on Software and Data Engineering
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• v.6 no.1
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• pp.29-36
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• 2017

In this paper, we propose a method for recognizing eating behavior with almost no motion acceleration. First, by using the acceleration of gravity acting on the wrist direction, we calculate the angle between the gravity and the wrist direction. After that, detect wrist reciprocating motion when peak and vally exist in specific angle band. And then, when accumulate the number of wrist reciprocating motion occurrences are up to 10, then regard as the meal time 5 minutes before the detection time. Also, estimate the meal time only if its duration is more than 7 minutes. Using the data of 2128 minutes, which was collected from four graduate student, the result of the meal time estimation shows 95.63% accuracy.

• Journal of Biomedical Engineering Research
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• v.33 no.2
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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).

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.18-26
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• 2015

Measuring the heart rate during exercise is important to properly control the amount of exercise. With the recent advent of smart device usage, there is a dramatic increase in interest in devices for the real-time measurement of the heart rate during exercise. During intensive exercise, accurate heart rate estimation from wrist-type photoplethysmography (PPG) signals is a very difficult problem due to motion artifact (MA). In this study, we propose an efficient algorithm for an accurate estimation of the heart rate from wrist-type PPG signals. For the twelve data sets, the proposed algorithm achieves the average absolute error of 1.38 beat per minute (BPM) and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.9922. The proposed algorithm presents the strengths in an accurate estimation together with a fast computation speed, which is attractive in application to wearable devices.

• 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 Industrial Technology
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• v.21 no.B
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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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.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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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.