To support an Oak Ridge National Laboratory programs exoskeleton project, a unique multi-axis foot force/torque sensor was constructed and tested that has biomedical application such as clinical gait analysis. The challenging aspect of this multi-axis force sensor is that it had to conform to the bending of the human foot, withstand high impact loads, have high force sensitivity, feel comfortable to the human wearer, be integrated into a military style boot, measure the forces on the human foot when either the ball or the heel of the foot is in contact with the ground, respond to both positive and negative loading and have a low overall height and weight. This paper describes the design and testing of this unique sensor.

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The skills of humans performing tasks can be evaluated using Fitts’ law, which is described by a straight line in the coordinate system of execution time and task difficulty index. The inverse of the slope of the straight line is called the execution capacity of a human. The aforementioned variables can be calculated from experimental data collected for a task. Experimental data were collected in the task in which a peg attached to the hand of an exoskeleton guided by a human arm was placed into a hole. Indeed, we have determined the mean and standard deviations for individual performances and also for the performances of a group of individuals. Thus, the numerical values calculated to describe the skills of an individual form a basis for comparison of the skills of the individuals. Our study introduces a novel probabilistic skill index to evaluate skills of operators performing tasks characterized by two or more variables. The probabilistic skill index provides a broader basis for the skill evaluations of the individuals. Moreover, the skills of the individuals performing tasks can be compared objectively by using the probabilistic skill index introduced

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Two adaptively combined gyro and triaxial accelerometer arrays of commercial-grade Micro Electro-Mechanical Systems (MEMS) inertial sensors and a real-time embedded algorithm suite significantly enhance accuracy over that of single commercial-grade devices. Denoised sensor data is combined with a Kalman-filter bias compensation algorithm to achieve better than 0.1 degree/hour in-run gyro bias stability (standard deviation). One denoise-filtered triaxial accelerometer sensor array provides better than 30µg accelerometer bias stability. The hardware is embedded in an exoskeleton that maintains accurate fine alignment adjustments and provides protection when in rugged environments. Analysis of results measured from Allan variance computations of real-time data from several different IMU hardware units demonstrates that each of three orthogonally positioned precision gyro arrays, and their associated processing, achieve less than 0.03 deg integrated angle drift over 5 hours. New IMU hardware is now in fabrication for delivery in early 2010, when received, we expect to overcome the limits of the present hardware design to produce additional significant improvements. The size, weight, and power of these new IMU’s are suitable for man-portable, hand-held systems, UAVs, and munitions systems. Early work for this technology was first published at ION/PLANS 2008.

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The present paper describes a power-assist robot arm that uses pneumatic artificial rubber muscles (PARMs) covered withexoskeleton suits to mimic the motion of biarticular muscles. One robot arm uses a single PARM, which assists the wrist and elbow joint of the user. This robot arm is linked to a waist belt through a suspender-like structure of three PARMs. PI control (Proportional Integral control), which is based on the pressure value from a balloon sensor, is used to achieve accurate power-assist motion, and the present study focuses on the motions associated with lifting an object. In order to evaluate the validity of the proposed power-assist robot arm, skin surface electromyography (EMG) signals of forearm muscles are measured during a lifting task and a follow-up questionnaire survey is conducted.

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We are attempting to develop therapeutic technology that individuals with substantial arm weakness and minimal hand function after stroke can use to improve their movement ability without the continuous presence of a rehabilitation therapist. We have developed a device called Therapy WREX (T-WREX) that is comprised of four main features: 1) a passive, gravity-balancing arm support (based on WREX ¿Wilmington Robotic Exoskeleton) that allows a wide range of arm motion 2) a grip sensor that detects even trace amounts of grasp 3) virtual reality exercises that simulate activities of daily living, and 4) software that provides feedback about task performance. Initial clinical testing indicates that chronic stroke patients can significantly improve their movement ability with T-WREX. We have also found that patients strongly prefer using T-WREX for therapy compared to conventional, self-directed, tabletop exercises. This paper reports the results of a follow-up survey conducted with 11 subjects to gain further understanding about the basis for this preference. The results suggest that subjects prefer T-WREX because it is more interesting, and because it allows them to be more successful with their movement attempts. Further, each of the four features of TWREX had significant value to the subjects, suggesting that their combination is synergistic.

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