• 로봇학회논문지
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• 제19권1호
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• pp.1-7
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• 2024

This paper presents an under-actuated robotic hand inspired by the ligamentous structure of the human hand for a prosthetic application. The joint mechanisms are based on the concept of a tensegrity structure formed by elastic strings. These rigid bodies and elastic strings in the mechanism emulate the phalanx bones and primary ligaments found in human finger joints. As a result, the proposed hand inherently possesses compliant characteristics, ensuring robust adaptability during grasping and when interacting with physical environments. For the practical implementation of the tensegrity-based joint mechanism, we detail the installation of the strings and the routing of the driving tendon, which are related to extension and flexion, respectively. Additionally, we have designed the palm structure of the proposed hand to facilitate opposition and tripod grips between the fingers and thumb, taking into account the transverse arch of the human palm. In conclusion, we tested a prototype of the proposed hand to evaluate its motion and grasping capabilities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2767-2780
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• 2016

Video-based human-activity recognition has become increasingly popular due to the prominent corresponding applications in a variety of fields such as computer vision, image processing, smart-home healthcare, and human-computer interactions. The essential goals of a video-based activity-recognition system include the provision of behavior-based information to enable functionality that proactively assists a person with his/her tasks. The target of this work is the development of a novel approach for human-activity recognition, whereby human-body-joint features that are extracted from depth videos are used. From silhouette images taken at every depth, the direction and magnitude features are first obtained from each connected body-joint pair so that they can be augmented later with motion direction, as well as with the magnitude features of each joint in the next frame. A generalized discriminant analysis (GDA) is applied to make the spatiotemporal features more robust, followed by the feeding of the time-sequence features into a Hidden Markov Model (HMM) for the training of each activity. Lastly, all of the trained-activity HMMs are used for depth-video activity recognition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권6호
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• pp.3182-3198
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• 2019

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• Korean Journal of Radiology
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• 제22권3호
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• pp.384-394
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• 2021

Objective: To quantitatively assess biochemical alterations in the cartilage of the subtalar and midtarsal joints in chronic lateral ankle instability (CLAI) patients with isolated anterior talofibular ligament (ATFL) injuries and combined calcaneofibular ligament (CFL) injuries using MRI T2 mapping. Materials and Methods: This study was performed according to regulations of the Committee for Human Research at our institution, and written informed consent was obtained from all participants. Forty CLAI patients (26 with isolated ATFL injuries and 14 with combined ATFL and CFL injuries) and 25 healthy subjects were recruited for this study. All participants underwent MRI scans with T2 mapping. Patients were assessed with the American Orthopedic Foot and Ankle Society (AOFAS) rating system. The subtalar and midtarsal joints were segmented into 14 cartilage subregions. The T2 value of each subregion was measured from T2 mapping images. Data were analyzed with ANOVA, the Student's t test, and Pearson's correlation coefficient. Results: T2 values of most subregions of the subtalar joint and the calcaneal facet of the calcaneocuboid joint in CLAI patients with combined CFL injuries were higher than those in healthy controls (all p < 0.05). However, there were no significant differences in T2 values in subtalar and midtarsal joints between patients with isolated ATFL injuries and healthy controls (all p > 0.05). Moreover, T2 values of the medial talar subregions of the posterior subtalar joint in patients with combined CFL injuries showed negative correlations with the AOFAS scores (r = -0.687, p = 0.007; r = -0.609, p = 0.021, respectively). Conclusion: CLAI with combined CFL injuries can lead to cartilage degeneration in subtalar and calcaneocuboid joints, while an isolated ATFL injury might not have a significant impact on the cartilage in these joints.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 1994년도 춘계공동학술대회논문집; 창원대학교; 08월 09일 Apr. 1994
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• pp.561-568
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• 1994

A man model can be used as an effective tool to design ergonomically sound products and workplaces, and subsequently evaluate them properly. For a man model to be truly useful, it must be integrated with a posture prediction model which should be capable of representing the human arm reach posture in the context of equipments and workspaces. Since the human movement possesses redundant degrees of freedom, accurate representation or prediction of human movement was known to be a difficult problem. To solve this redundancy problem, a psychophysical cost function was suggested in this study which defines a cost value for each joint movement angle. The psychophysical cost function developed integrates the psychophysical discomfort of joints and the joint range availability concept which has been used for redundant arm manipulation in robotics to predict the arm reach posture. To properly predict an arm reach posture, an arm reach posture prediction model was then developed in which a posture configuration that provides the minimum total cost is chosen. The predictivity of the psychophysical cost function was compared with that of the biomechanical cost function which is based on the minimization of joint torque. Here, the human body is regarded as a two-dimensional multi-link system which consists of four links ; trunk, upper arm, lower arm and hand. Real reach postures were photographed from the subjects and were compared to the postures predicted by the model. Results showed that the postures predicted by the psychophysical cost function closely simulated human reach postures and the predictivity was more accurate than that by the biomechanical cost function.

• 한국소음진동공학회논문집
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• 제22권6호
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• pp.502-508
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• 2012

An analytical model for a human body is important to predict muscle and joint forces. Because it is difficult to estimate muscle or joint forces from a human body, the objective of this study is the development of a reliable analytical model for a human body to evaluate the lower extremity muscle and joint forces. The musculoskeletal system of the human lower extremity is modeled as a multibody system employing the Hill-type muscle model. Muscle forces are determined to minimize energy consumption, and we assume that motion is constrained in the sagittal plane. Muscle forces are calculated through an equilibrium analysis while rising from a seated position. The musculoskeletal model consists of four segments. Each segment is a rigid body and connected by frictionless revolute joints. Muscles of the lower extremity are simplified to seven muscles with those that are not related to the sagittal plane motion are ignored. Muscles that play a similar role are combined together. The results of the present study are compared with experimental results to validate the lower extremity model and the assumptions of the present study.

• 한국산업융합학회 논문집
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• 제24권6_1호
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• pp.693-698
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• 2021

As technology advances, there is increasing need for research in different fields where this technology is applied. On of the most researched topic in computer vision is Human activity recognition (HAR), which has widely been implemented in various fields which include healthcare, video surveillance and education. We therefore present in this paper a human activity recognition system based on scale and rotation while employing the Kinect depth sensors to obtain the human skeleton joints. In contrast to previous approaches that use joint angles, in this paper we propose that each limb has an angle with the X, Y, Z axes which we employ as feature vectors. The use of the joint angles makes our system scale invariant. We further calculate the body relative direction in the egocentric coordinates in order to provide the rotation invariance. For the system parameters, we employ 8 limbs with their corresponding angles each having the X, Y, Z axes from the coordinate system as feature vectors. The extracted features are finally trained and tested with the Long short term memory (LSTM) Network which gives us an average accuracy of 98.3%.

• 한국인터넷방송통신학회논문지
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• 제23권6호
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• pp.89-95
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• 2023

인간 행동 인식 (Human action recognition, HAR) 기술은 스포츠 분석, 인간과 로봇 간의 상호작용, 대형 사이니지 콘텐츠 등의 애플리케이션에 활용되는 핵심 기술 중 하나이다. 본 논문에서는 몰입형 대형 사이니지 콘텐츠를 위한 STAGCN (Spatial temporal attention graph convolutional network) 기반 인간 행동 인식 시스템을 제안한다. STAGCN은 attention mechanism을 통해 스켈레톤 시퀀스의 시공간적 특징에 서로 다른 가중치를 부과하여, 동작 인식에 중요한 관절 및 시점을 고려할 수 있다. NTU RGB+D 데이터셋을 사용한 실험 결과, 제안된 시스템은 기존 딥러닝 모델들에 비해 높은 분류 정확도를 달성한 것을 확인했다.

• 대한전자공학회논문지SP
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• 제47권6호
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• pp.39-47
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• 2010

영상 기반의 모션 캡처에 대한 연구는 인체의 특징 영역 검출, 정확한 자세 추정 및 실시간 성능 등의 문제를 풀기 위해 많은 연구가 진행되고 있다. 특히, 인체의 많은 관절 정보를 복원하기 위해 다양한 방법이 제안되고 있다. 본 논문에서는 수치적인 역운동학 방법의 단점을 개선한 실시간 모션 캡처 방법을 제안한다. 기존의 수치적인 역운동학 방법은 많은 반복 연산이 필요하며, 국부최소치 문제가 발생할 수 있다. 본 논문에서는 이러한 문제를 해결하기 위해 기존의 수치적인 역운동학 해법과 UKF를 결합하여 중간관절을 복원하는 방법을 제안한다. 수치적인 역운동학의 해와 UKF를 결합함으로써, 중간 관절 추정 시 최적값에 보다 안정적이고 빠른 수렴이 가능하다. 모션 캡처를 위해 먼저, 배경 차분과 피부색 검출 방법을 이용하여 인체의 특징 영역을 추출한다. 다수의 카메라로부터 추출된 2차원 인체 영역 정보로부터 3차원 정보를 복원하고, UKF와 결합된 수치적인 역운동학 해법을 통해 동작자의 중간 관절 정보를 추정한다. 수치적인 역운동학의 해는 UKF의 상태 추정 시 안정적인 방향을 제시하고, UKF는 다수의 샘플을 기반으로 최적 상태를 찾음으로써, 전역해에 보다 빠르게 수렴한다.