Impaired upper limb function is one of the most common sequelae in central nervous system. In spinal cord injury (SCI) patients, upper limbs are affected in more than 50% of cases (Wyndaele and Wyndaele 2006). Upper limb strength is impaired to some extent in people who have suf-fered cervical SCI making it difficult for them to perform many activities of daily living (ADL) essential for their autonomy such as wheelchair manual propulsion, eating, drinking, and personal hygiene (Parker et al. 1986; Nakayama et al. 1994). In contrast with lower limbs, upper limbs have extensive functionally due to the mobility of numerous joints that can execute fine movements thanks to complex neuromuscular con-trol. Lower limbs movements have been broadly analyzed in biomechanical studies specially regarding gait analysis. Gait analysis has evolved over the last decades as an important technique to assist in the clinical assessment of patients with mobility dysfunction. These techniques are useful for evaluation, treatment, and surgical planning; in addition, sequential assessments help to provide a functional outcome evaluation. Motion analysis offers an objective method for quantifying movement and is considered a gold standard for evaluating lower limb function during gait in different types of patients.

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Over the past few decades, an enormous amount of research effort has been put into developing exoskeletons and robotic assistive devices. Primary aims generally being rehabilitation use, these devices have variety of other application areas including rescue, military and recreational activities. Although the supplementary technologies such as measurement and control have advanced vastly in recent times, there are still many challenges related to the exoskeleton and robotic assistive device development for the progress in their improved and widespread usage. The natural and versatile human body structure and its multifaceted movement convey a huge potential together with the challenges for the improvement of exoskeletons and robotic devices for mobility assistance and therapy and, demand for the usage of such devices progressively growing. The associated challenges and the solutions to the problems can be virtually inestimable considering also the variety of circumstances of patients in need. In this paper, we summarize and review a number of researches performed in the field to provide a broad perspective for the current and potential future work relevant to the area of robotic assistive devices for mobility and their therapeutic usage.

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The ongoing pilot clinical trial of the BrainGate neural interface system aims in part to assess the feasibility of using neural activity obtained from a small-scale, chronically implanted, intracortical microelectrode array to provide control signals for a neural prosthesis system. Critical questions include how long implanted microelectrodes will record useful neural signals, how reliably those signals can be acquired and decoded, and how effectively they can be used to control various assistive technologies such as computers and robotic assistive devices, or to enable functional electrical stimulation of paralyzed muscles. Here we examined these questions by assessing neural cursor control and BrainGate system characteristics on five consecutive days 1000 days after implant of a 4 × 4 mm array of 100 microelectrodes in the motor cortex of a human with longstanding tetraplegia subsequent to a brainstem stroke. On each of five prospectively-selected days we performed time-amplitude sorting of neuronal spiking acti-vity, trained a population-based Kalman velocity decoding filter combined with a linear discriminant click state classifier, and then assessed closed-loop point-and-click cursor control. The participant performed both an eight-target center-out task and a random target Fitts metric task which was adapted from a human-computer interaction ISO standard used to quantify performance of computer input devices. The neural interface system was further characterized by daily measurement of electrode impedances, unit waveforms and local field potentials. Across the five days, spiking signals were obtained from 41 of 96 electrodes and were succes-sfully decoded to provide neural cursor point-and-click control with a mean task performance of 91.3% ± 0.1% (mean ± s.d.) correct target acquisition. Results across five consecutive days demonstrate that a neural interface system based on an intracortical micro-electrode array can provide repeatable, accurate point-and-click control of a computer interface to an individual with tetraplegia 1000 days after implantation of this sensor.

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Background: Current guidelines for rehabilitation of arm and hand function after stroke recommend that motor training focus on re-alistic tasks that require reaching and manipulation and engage the patient intensively, actively, and adaptively. Here, we inves-tigated the feasibility of a novel robotic task-practice system, ADAPT, designed in accordance with such guidelines. At each tri-al, ADAPT selects a functional task according to a training schedule and with difficulty based on previous performance. Once the task is selected, the robot picks up and presents the corresponding tool, simulates the dynamics of the tasks, and the patient in-teracts with the tool to perform the task. Methods: Five participants with chronic stroke with mild to moderate impairments (> 9 months post-stroke; Fugl-Meyer arm score 49.2 ± 5.6) practiced four functional tasks (selected out of six in a pre-test) with ADAPT for about one and half hour and 144 trials in a pseudo-random schedule of 3-trial blocks per task. Results: No adverse events occurred and ADAPT successfully presented the six functional tasks without human intervention for a total of 900 trials. Qualita-tive analysis of trajectories showed that ADAPT simulated the desired task dynamics adequately, and participants reported good, al-though not excellent, task fidelity. During training, the adaptive difficulty algorithm progressively increased task difficulty lea-ding towards an optimal challenge point based on performance; difficulty was then continuously adjusted to keep performance around the challenge point. Furthermore, the time to complete all trained tasks decreased significantly from pretest to one-hour post-test. Finally, post-training questionnaires demonstrated positive patient acceptance of ADAPT. Conclusions: ADAPT successfully provided adaptive progressive training for multiple functional tasks based on participant’s performance. Our encouraging results es-tablish the feasibility of ADAPT; its efficacy will next be tested in a clinical trial.

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The portability, ease of use and improved accuracy of miniature inertial sensors brought by current microelectromechanical system (MEMS) technology has inspired researchers to develop human movement monitoring system with body-fixed sensors. Although a large number of studies have attempted to explore the use of miniature inertial sensors in estimating walking speed for the past two decades, there still remain some questions regarding applying inertial sensors in estimating walking speed under dierent walking conditions and for dierent subject populations. In this thesis, I focus on evalua-ting and improving the performance of a shank-mounted mounted inertial measurement unit (IMU) based walking speed estimation method. My research can be divided into four parts. The first part was a systematic review regarding the state of the art of current development of the inertial sensor based walking speed estimation method. A total of 16 articles were fully reviewed in terms of sensor specifica-tion, sensor attachment location, experimental design and spatial parameter estimation algorithm. In the second part, a comprehensive performance evaluation was conducted, which included the treadmill and overground walking experiments with constraint on the walking speed, stride length and stride frequency. A systematic error was observed in the error analysis of this study, which was adjusted by subtracting the bias by linear regression. In the third part, a post-stroke subject overground walking experiment was carried out with an improved walking speed estimation method that reduced the systematic error caused by previous false initial speed assumption. In addition to walking speed estimation, the gait asymmetry for post-stroke hemiparetic gait was also evaluated with the proposed method. The last part was the sensor error model analysis. We elaborately analyzed and discussed the estimation errors involved in this method in order to completely understand the sensor error compensation in walking speed estimation algorithm design. Two existing sensor error models and one newly developed sensor error model were compared with the treadmill walking experiment, which demonstrated the eect of each sensor error component on the estimation result and the importance of the sensor error model selection.

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