Biotechnology is a science that is growing rapidly. The objective of this project is to advance in the field. Specifically it aims to study applications of kinematics in the field of human-machine interfaces namely exoskeleton and prosthesis designs for the human hand. The methodology used in this project consists of three phases. First a theoretical model of the hand kinematics is defined from medical literature. This is done by synthesizing the hand into its simplified parameters to define a robotic model. The adjustment of the theoretical model to a hand is then done by capturing the movement using computer vision. This is done by using markers on each nail to be able to estimate their poses which consist of their spatial orientation and position. This sequence permits estimating the movement of the entire hand. Dimensional kinematic synthesis is finally applied to adapt the theoretical model to the dataset provided by computer vision. This is done by defining the equations of the movement of the theoretical hand model and is then solved by a numerical solver. This allows the creation of a persona-lized hand model that can then be used for the study of correspondences between electromyography (EMG) and the movements of the hand. In conclusion, this project has designed a robust algorithm for the tracking and estimation of the poses of the nails of the hand. It has also defined the movement equations and created an application to solve them. This has led to the nding of many nonanthropomorphic models that can be of use in the design of exoskeletons.

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The number of people who need rehabilitation increases day by day because of reasons such as laceration, aging, work accidents and etc. Therefore, the need of rehabilitation aids is constantly increasing. There are many research studies about assistive technologies in rehabilitation. Especially, rehabilitation robots have a great importance. Existing rehabilitation robot studies have mostly focused on position and force control. Thus, it is muscular activation that should be evaluated to enhance control results, because the same joint trajectory and/or joint torque can be achieved through different muscular combinations. In this study a muscular activation controlled rehabilitation robot system for lower limbs is proposed. A probabilistic artificial neural network model, which can estimate posteriori probability, was used for discrimination of EMG patterns for robot control with EMG signals.

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Our senses are physiological tools for perceiving environmental information. Humans have at least five senses (as defined and classified by Aristotle). These senses are: sight or vision, hearing or audition, smell or olfaction, touch or taction, and taste or gustation. They are perceived when sensory neurons react to stimuli and send messages to the central nervous system. We actually have more than five senses. For example, Gibson has stated that we have both outward-orientated (exteroceptive) senses and inward-orientated (interoceptive) senses [127]. The sense of equilibrium, also known as proprioception, is one example of these other senses. Each of the sense modalities is characterized by many factors, such as the types of received and accepted data, the sensitivity to the data in terms of temporal and spatial resolutions, the information processing rate or bandwidth, and the capability of the receptors to adapt to the received data.

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This paper presents a survey of existing robotic systems for lower-limb rehabilitation. It is a general assumption that robotics will play an important role in therapy activities within rehabilitation treatment. In the last decade, the interest in the field has grown exponentially mainly due to the initial success of the early systems and the growing demand caused by increasing numbers of stroke patients and their associate rehabilitation costs. As a result, robot therapy systems have been developed worldwide for training of both the upper and lower extremities. This work reviews all current robotic systems to date for lower-limb rehabilitation, as well as main clinical tests performed with them, with the aim of showing a clear starting point in the field. It also remarks some challenges that current systems still have to meet in order to obtain a broad clinical and market acceptance.

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Decoding human motor tasks from single trial electroencephalography (EEG) signals can help scientists better understand cortical neurophysiology and may lead to brain computer interfaces (BCI) for motor augmentation. Spatial characteristics of EEG have been used to distinguish left from right hand motor imagery and motor action. We used independent component analysis (ICA) of EEG to distinguish right knee action from right ankle action. We recorded 264-channel EEG while 5 subjects performed a variety of knee and ankle exercises. An adaptive mixture independent component analysis (ICA) algorithm generated two distinct mixture models from a merged set of EEG signals (including both knee and ankle actions) without prior knowledge of the underlying exercise. The ICA mixture models parsed EEG signals into maximally independent component (IC) processes representing electrocortical sources, muscle sources, and artifacts. We calculated a spatially fixed equivalent current dipole for each IC using an inverse modeling approach. The fit of the models to the single trial EEG signals distinguished knee exercises from ankle exercise with 90% accuracy. For 3 of 5 subjects, accuracy was 100%. Electrocortical current dipole locations revealed significant differences in the knee and ankle mixture models that were consistent with the somatotopy of the tasks. These data demonstrate that EEG mixture models can distinguish motor tasks that have different somatotopic arrangements, even within the same brain hemisphere.

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