This paper presents a 1-DOF robot for hand rehabilitation exercises for hemiplegic patients. The robot provides the cylindrical grasp movement, which is one of the dominant Activities of Daily Living. Linkage Sliding Mechanism allows the proposed robot to be a simple and lightweight structure. Motion test for the healthy subjects are carried out to verify the performance of the robot. Consequently, it was confirmed that the proposed robot was suitable for performing hand rehabilitation treatment.

목적 : 본 연구의 목적은 로봇(Amadeo®)을 이용한 왼손의 능동 보조 운동이 뇌졸중 환자의 편측무시에 미치는 영향을 알아보는 것이다. 연구방법 : 본 연구는 만성 뇌졸중 환자 3명을 대상으로 하였으며, 다중 기초선 개별실험연구 설계를 사용 하였다. 연구기간 동안 대상자별 실험설계는, 대상자 1은 기초선 3회, 중재기 12회, 대상자 2는 기초선 5회, 중재기 10회, 대상자 3은 기초선 7회, 중재기 8회로, 대상자간 기초선과 중재기 회기 수는 다르나 총 회기 수는 15회로 일정하였다. 각 중재기간 동안 로봇을 이용하여 회기 당 30분씩 왼손의 능동보조 운동을 시행하였다. 기초선과 중재기간에는 편측무시 평가를 위해 선 나누기 검사(Line Bisection Test; LBT)와 알버트 검사(Albert’s Test)를 시행하였고 일상생활에서의 편측무시 정도를 평가하기 위해 중재 전, 후에 Catherine Bergego Scale (CBS)을 사용하였다. 결과 : 중재 전과 비교하여 모든 대상자에서 로봇보조치료 후에 선 나누기 검사, 알버트 검사, CBS 점수 가 향상되었다. 결론 : 본 연구 결과, 로봇을 이용한 왼손의 능동 보조 운동은 뇌졸중 환자의 편측무시 감소에 비교적 긍 정적인 영향을 준 것으로 확인되었다.

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

The versatility of a human hand is what the researchers eager to mimic. As one of the attempt, the redundant degree of freedom in the human hand is considered. However, in the force domain the redundant joint causes a control issue. To solve this problem, the force control method for a redundant robotic hand which is similar to the human is proposed. First, the redundancy of the human hand is analyzed. Then, to resolve the redundancy in force domain, the artificial minimum energy point is specified and the restoring force is used to control the configuration of the finger other than the force in a null space. Finally, the method is verified experimentally with a commercial robot hand, called Allegro Hand with a force/torque sensor.

This paper describes the development of a hand module of NREX (National Rehabilitation Center Robotic Exoskeleton) designed to assist individuals with sustained neurological impairments such as stroke and spinal cord injuries. To construct a simple and lightweight hand module, the robotic hand adopts a mechanism driven by a motor and moved by two four-bar linkages. The motor facilitates the flexion-extension movements of the thumb and the other four fingers simultaneously. Thus, an individual using the robotic hand module can effectively grip and release objects related to daily life activities. The robotic hand module has been designed to cover the range of motion with respect to its link distance. This hand module can be used in therapeutic rehabilitation as well as for daily life assistance. In addition, this hand module can either be mounted on an NREX or used as a standalone module.

An adaptive robot hand (AR-Hand) has a stable grasp of different objects in unstructured environments. In this study, we propose an AR-Hand based on a tendon-driven mechanism which consists of 4 fingers and 12 DOFs. It weighs 0.5 kg and can grasp an object up to 1 kg. This hand based on the adaptive grasp mechanism is able to provide a stable grasp without a complex control algorithm or sensor system. The fingers are driven by simple tendon structures with each finger capable of adaptively grasping the objects. This paper presents a method to decide the joint stiffness. The adaptive grasping is verified by various grasping experiments involving objects with different shapes and sizes.

The purpose of this study was to investigate effect of robot-assisted hand rehabilitation(AmadeoⓇ) on hand motor function in chronic stroke patients. This study used a single-subject experimental design with multiple baselines across individuals. Three chronic stroke survivors with mild to sever motor impairment took part in study. Each participants had 2 weeks interval of starting intervention. Participants received robot-assisted therapy(45min/session. 3session/wk for 6wks). Finger active range of motion(AROM) was assessed by Range of Assessment program in AmadeoⓇ, and test-retest reliability was verified using Pearson correlation analysis. To investigate effect of AmadeoⓇ, finger AROM was measured immediately after each sessions and Fugl-Meyer Assessment of Upper extremity, Motor Activity Log, Nine hole peg board test and Jebsen-Taylor hand motor function test were assessed at pre-post intervention. Results were analyzed by visual analysis and comparison of pre-post tests. The test-retest reliability of Range of Assessment was good(r=.99). After robot-assisted therapy, finger AROM of participant 1, 2, and 3 was respectively improved by 18%, 3.6%, and 6% each. Hand motor function of participant 1, 3 was improved on all four tests, but not effect in participant 2. Robot-assisted hand rehabilitation could improve finger AROM and effect on hand motor function in chronic stroke patients.

A humanoid robot hand with one thumb and two fingers has been developed. Each finger has the specially designed compact joints, called "MEC Joint", which convert the rotation of a motor to the swing motion of a pendulum. The robot hand with the MEC Joints is compact and relatively light but strong enough to grasp objects in the same manner as human being does in daily activities. In this paper the kinematic model and the torque characteristics of the MEC Joint are presented and compared with the results of the dynamic simulation and the dynamometer test. The dynamic behavior of the thumb and two fingers with MEC Joints are also presented by computer simulation.

This paper focuses on a development of an anthropomorphic robot hand. Human hand is able to dexterously grasp and manipulate various objects with not accurate and sufficient, but inaccurate and scarce information of target objects. In order to realize the ability of human hand, we develop a robot hand and introduce a control scheme for stable grasping by using only kinematic information. The developed anthropomorphic robot hand, KITECH Hand, has one thumb and three fingers. Each of them has 4 DOF and a soft hemispherical finger tip for flexible opposition and rolling on object surfaces. In addition to a thumb and finger, it has a palm module composed the non-slip pad to prevent slip phenomena between the object and palm. The introduced control scheme is a quitely simple based on the principle of virtual work, which consists of transposed Jacobian, joint angular position, and velocity obtained by joint angle measurements. During interaction between the robot hand and an object, the developed robot hand shows compliant grasping motions by the back-drivable characteristics of equipped actuator modules. To validate the feasibility of the developed robot hand and introduced control scheme, collective experiments are carried out with the developed robot hand, KITECH Hand.

In this study, an anthropomorphic robot Hand, called “SKKU Hand III” is presented. The hand has thirteen DOF(Degree-Of-Freedom) and is designed based on the skeletal structure of the human hand. Each finger module(except thumb module) has three DOF and four joints with a saddle joint mechanism which has two DOF at the base joint. Two distal joints of the finger module are mechanically coupled by a timing belt and pulleys. The thumb module is composed of a finger module and an additional actuator, which makes it possible to realize the opposition between the thumb and the other fingers. In addition, the palm DOF of the human hand is mimicked with a spatial link mechanism between the index finger and the thumb. Thus, it can grasp objects more stably and more strongly. For the modularization of the robotic hand all the driving circuits are embedded in the hand, and only the communication lines supporting CAN protocol with DC power cable are given as an interface. Therefore, it is possible to apply it to any robot system the interface. To validate the feasibility of the SKKU Hand III, a series of the representative grasp experiments such as power, precision, intermediate grasp etc. are carried out with the object around us and its operation is demonstrated.