Motion Capture is currently a widely used technology for character anima-tion, and can be seen as a common ingredient of many contemporary animated feature films and video games. Compared to traditional character animation, the Motion Capture technology can quickly produce very high quality animation data with high resemblance to real life motion. Unfortunately, the technology is very expensive, and commercial systems with a price tag below $20.000 can today be considered as a bargain. Hence, using Motion Capture as a tool for incorporating high quality live motion in a low budget animation project seems impossible as of today. However, since the underlying theory and principles behind capturing live motion are relatively straightforward, it should be possible to develop a Motion Capture system without having to spend tens of thousands of dollars. This thesis presents a full body Motion Capture system developed with strictly limited resources, at a total cost of $950. The system consists of an electromechanical, exoskeletal suit, constructed with cheap and lightweight materials, and a client-server software solution capable of real time visualization and data export. This diploma work has shown that it is possible to construct a high quality, real time Motion Capture system at a considerably low cost, compared to commercial alternatives. By offering blueprints for mechanics, schematics for electronics and source code for the software solution, this project opens doors for further development by other technical institutions and hobbyists.

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Background: Objective measures of hand function as in-dividuals participate in home and community activities are needed in order to better plan and evaluate re-habilitation treatments. Traditional measures collected in the clinical setting are often not reflective of actual functional performance. Recent advances in technology, however, enable the development of a lightweight, comfortable data collection monitor to measure hand kinematics.
Methods: This paper presents the design analysis of a wearable sensor glove with a specific focus on the sensors selected to measure bend. The most important requirement for the glove is easy donning and removal for individuals with significantly reduced range of motion in the hands and fingers. Additional require-ments include comfort and durability, cost effectiveness, and measurement repeatability. These require-ments eliminate existing measurement gloves from consideration. Glove construction is introduced, and the sensor selection and glove evaluation process are presented.
Results: Evaluation of commercial bend sensors shows that although most are not appropriate for repeatable measurements of finger flexion, one has been successfully identified. A case study for sensor glove repea-tability using the final glove configuration and sensors does show a high degree of repeatability in both the gripped and flat hand positions (average coefficient of variability = 2.96%and 0.10%, respectively).
Conclusion: Measuring functional outcomes in a portable manner can provide a wealth of information impor-tant to clinicians for the evaluation and treatment of movement disorders in the hand and fingers. This de-vice is an important step in that direction as both a research and an evaluation method.

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There is a close relationship between robotics and prosthetic devices (artificial limbs) since both provide human-like motion and prehension. In general, prosthetic devices have benefited from the development of robotic technologies. Interestingly, researchers at the Center for Engineering Design at the University of Utah developed the Utah Arm, a three-degree-of-freedom elbow prosthesis, prior to developing the Utah/MIT Dextrous Hand, a robotics end-effector. These research projects spawned two new companies—Motion Control, Inc., the manufacturer of the Utah Arm, and Sarcos, Inc., a robotics company under the leadership of Stephen C. Jacobsen.

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Two human-robot interactions, including a haptic interaction and a teleoperated interaction, are explored with a new exoskeleton-type masterarm, in which the electric brakes with the torque sensor beams are used for force reflection. In the haptic interaction with virtual environment, the masterarm is used as a haptic device and tested to examine how the resistant torque of the electric brake for the force reflection is implemented in contact regime prior to conducting the teleoperated interaction. Two types of virtual environments, a rigid wall with high stiffness (hard contact with 10 [KN/m]) and a soft wall with low stiffness (soft contact with 0.1 [N/m]), are integrated with the masterarm for the haptic interaction. In hard contact, large force is fed back to the human operator, and makes the human operator hardly move. The electric brake with the torque sensor beam can detect the torque and its direction so that it allows free motion as well as contact motion by releasing or holding the movement of the operator. The experimental results show how the electric brake is switched from contact to free regime to allow the operator to move freely, especially when the operator intends to move toward the free regime in contact. In soft contact, the force applied to the human operator can be increased or decreased proportionally to the torque amount sensed by the torque sensor beam, thus the operator can feel the contact force proportional to the amount of the deformation during the contact. Finally, the masterarm is integrated with the humanoid robot, CENTAUR developed at Korea Institute of Science and Technology to conduct a pick-and-place task through the teleoperated interaction. It is examined that the CENTAUR as a slave robot can follow the movement of the operator.

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In this paper we present a method to calibrate the surface EMG signal-to-force-relationship online. For this, a simple biomechanical model composed of bones and muscles is used. The calibration is based on an online optimization algorithm where the error between the movement of the human and the movement computed with the biomechanical model is minimized. The proposed method is part of a control system for an exoskeleton robot that should aid the wearer in everyday-life situations like walking, standing up and sitting down. In contrast to existing methods for the calculation of the EMG signal-to-force-relationship, we are not interested in the exact force values of every single muscle, but our model groups some muscles together and uses the EMG signal of one of those muscles as a representative for the group to simplify calculations. The performance of the presented method was investigated on the leg movement in sagittal plane without contact to the environment.

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