One of the useful features of robotic rehabilitation is the possibility of movement quantification, which is currently lacking in conventional rehabilitation therapy. Movement performance measures calculated from this quantitative information serves various purposes — (a) a good supplement to clinical assessment measures, (b) can be more sensitive than many clinical measures which use ordinal scales for scoring, (c) can be used to track a patient’s recovery over time. Our research group has developed a 5 degree-of-freedom wearable exoskeleton robot for upper-extremity rehabilitation (RUPERT); RUPERT provides movement kinematics information in the form of joint angles, and also provides the pressure inside the pneumatic muscle actuators that drive the robot. In this paper we describe some useful robot-measured performance metrics that can be calculated from the sensor information collected from RUPERT. Some of the important performance-metrics described in this paper are — (a) Amount-of-assistance, (b) Smoothness, and (c) Movement synergy. We present a new method for calculating smoothness, which is uses a very different approach from some of the currently available approaches for calculating smoothness.; we call this approach the dasiaspectral methodpsila, which looks at the frequency spectrum of the movement velocity signal to estimate movement smoothness. In addition, we also present a method to analyze the effect of target location and DOF on the performance metrics, and also a method to detect fatigue.

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This paper presents the development of a neuromuscular interface for an exoskeleton to assist the elbow joint. The interface uses electromyographic (EMG) signals obtained from the biceps and triceps to predict elbow flexion and extension movements. These movements occur in the sagittal plane and the effects of forearm weight have been incorporated. The interface uses a physiological-model-based approach to convert the EMG signals to a joint displacement and this is based on Hill-type muscle models that have traditionally been used in clinical applications or for diagnosing and managing neurological and orthopaedic conditions. Simulation results have been obtained for the performance of the interface on pre-recorded data. While the general trend of movement was correctly identified by the interface, further experiments are required to quantify the accuracy and determine real-time performance capabilities.

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We report the use of the amino polysaccharide chitosan for the immobilization and patterning of biomolecules on microfabricated surfaces. Chitosan is a biocompatible and biodegradable substance derived from chitin, which is the structural material in theexoskeleton of crustaceans. Chitosan has two key properties of interest to biologists and engineers alike. First, it has an abundance of primary amine groups which can be covalently coupled to various biomolecules. Second, it possesses pH-dependent network-forming properties. Below pH of 6.5, chitosan’s amine groups become protonated and positively charged, making it soluble in acidic conditions. At pH above 6.5 the amines become deprotonated, and chitosan forms an insoluble polymer network. This allows a film of chitosan to be deposited from solution onto a negatively charged electrode due to a localized region of high pH established near the electrode surface. This electrodeposition is a simple yet robust method of patterning biomolecules with significant advantages over traditional patterning techniques such as microcontact printing. We have demonstrated electrodeposited chitosan as an immobilization agent for DNA and several proteins. Amine-labeled DNA are coupled to chitosan by glutaraldehyde crosslinking and proteins are coupled by enzyme-activated genetically engineered tyrosyl residues. These coupling chemistries can be extended to other biomolecules as well. In addition, the biomolecule attachment can be reversed or completely blocked by passivating chitosan. With its electrodeposition property and its easily accessible amine groups, chitosan is an attractive material from both the microfabrication and biology perspective. Chitosan can be used for a wide range of devices as a spatially controllable interface between organic and inorganic components. The possible applications of chitosan include biosensors, drug delivery devices, and labs on a chip. In our work, we have successfully used chitosan for three — — different BioMEMS applications. One is an optical biosensor, in which chitosan is used to immobilize fluorescently labeled biomolecules on the facets of waveguides. Another is a micromechanical biosensor, in which chitosan immobilizes DNA on the surface of a microcantilever. The third one is a microfluidic device, in which chitosan is used to pattern biomolecules inside sealed microchannels. These devices demonstrate the simplicity and flexibility of chitosan-based biomolecular patterning

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Extracellular recordings of motor cortex (MI) neurons, using a chronically implanted multi-electrode array, promise to yield a high dimensional input signal to external devices such as a computer, exoskeleton or prosthetic arm. For the multi-electrode array to be used as a sensor for a neuromotor prosthesis (NMP), it is important that it continually record movement-related signals over long time periods. Recent studies have demonstrated that it is possible to continually record for up to 1.5 years from a sufficient number of MI neurons in monkeys to enable neural decoding of arm movement. Cyberkinetics Neurotechnology Systems Inc. has initiated an investigational device exemption (TOE) study investigating the safety and efficacy of the BrainGate™ Neural Interface System, a medical device that combines this sensor with data acquisition and processing devices to decode movement intent. This device is currently being investigated as a means for a quadriplegic person to operate a range of assistive technologies. Preliminary results from this case study provide evidence that (1) MI neurons remain active more than 3 years after spinal cord injury, (2) units can be recorded 6 months after surgery. This technology may benefit quadriplegic people by providing a new output pathway from the cortex, to control their muscles.

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We propose an algorithm used to obtain the information on stride length, height difference, and direction based on user’s intent during walking. For exoskeleton robots used to assist paraplegic patients’ walking, this information is used to generate gait patterns by themselves in on-line. To obtain this information, we attach an inertial measurement unit(IMU) on crutches and apply an extended kalman filter-based error correction method to reduce the phenomena of drift due to bias of the IMU. The proposed method is verifed in real walking scenarios including walking, climbing up-stairs, and changing direction of walking with normal.

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