106 research outputs found
Raw Metabolic Data
No full textRaw data collected during the experiment. This is the raw data that goes with "Modeling the Metabolic Reductions of a Passive Back-Support Exoskeleton" by Mohammad Mehdi Alemi, Athulya A. Simon, Jack Geissinger, and Alan T. Asbeck, Journal of Applied Physiology, 2022
Garg PhD Thesis Metabolic Modeling Calculations
No full textThis fits the metabolic model developed by Mehdi Alemi and Alan Asbeck to the raw data from Garg 1976 PhD Thesis, p. 198 and beyond (raw data tables).Citation for Garg 1976:GARG, Arun, 1947- A METABOLIC RATE PREDICTION MODEL FOR MANUAL MATERIALS HANDLING JOBS. The University of Michigan, Ph.D., 1976 Engineering, industrial.</div
Modeling Metabolic Reductions During Repetitive Lifting
No full textBy Mohammad Mehdi Alemi and Alan Asbeck. Assistive Robotics Laboratory at Virginia Tech.Last Update: March 2021This code models the metabolic cost of lifting (both without-exo and with-exoskeleton (i.e., VT-Lowes exo)) by calculating the constant coefficients K_Box and K_Body. These calculations support the paper "Modeling the Metabolic Reductions of a Passive Back-SupportExoskeleton", Mohammad Mehdi Alemi, Athulya A. Simon, Jack Geissinger, and Alan T. Asbeck", submitted to the Journal of Applied Physiology in June 2021. To calculate the K coefficients, the data in Welbergen (1991) and Hagen (1994) were used (references (40) and (77) in the paper). The full citations for Welbergen and Hagen are as follows:Hagen KB, Harms-Ringdahl K, Hallén J. Influence of lifting technique on perceptual and cardiovascular responses to submaximal repetitive lifting. Eur J Appl Physiol Occup Physiol 68: 477–482, 1994. doi: 10.1007/BF00599516. Welbergen E, Kemper HCG, Knibbe JJ, Toussaint HM, Clysen L. Efficiency and effectiveness of stoop and squat lifting at different frequencies. Ergonomics 34: 613–624, 1991. doi: 10.1080/00140139108967340. </div
De Looze 1994 Fitting to Metabolic Cost Model
No full textThis script fits the data in Table 1 in (De Looze et. al 1994) to the metabolic cost model developed by Mehdi Alemi and Alan Asbeck in the paper "Modeling the Metabolic Reductions of a Passive Back-Support Exoskeleton" in the Journal of Applied Physiology, 2021.Full citation for De Looze 1994:De Looze, M.P., Toussaint, H.M., Commissaris, D.A., Jans, M.P. and Sargeant, A.J., 1994. Relationships between energy expenditure and positive and negative mechanical work in repetitive lifting and lowering. Journal of Applied Physiology, 77(1), pp.420-426. </div
Baron Van Asbeck - Twee Kerken - Geïnspireerd door Le Corbusier
No full textGedurende de Wederopbouwperiode, na de Tweede Wereldoorlog, werden veel nieuwe woonwijken gebouwd in Nederland. De oorlog had tot een toenemend verlangen naar religie geleidt. In nieuwe wijken was dan ook vraag naar nieuwe kerkgebouwen. De Nederlandse architect Ir. J.B. baron van Asbeck heeft een belangrijke bijdrage aan deze kerkbouw geleverd. Twee van zijn ontwerpen komen in dit boek aan bod, vanwege hun opvallende gelijkenissen met de wereldberoemde Chapelle Notre Dame du Haut te Ronchamp, ontworpen door Le Corbusier. Het gaat om de Paaskerk in Amstelveen (1963) en de Messiaskerk in Wassenaar (1967).The Institute of History of Art, Architecture and Urbanism (IHAAU)ArchitectureArchitectur
Controlling negative and positive power at the ankle with a soft exosuit
No full textThe soft exosuit is a new approach for applying assistive forces over the wearer's body through load paths configured by the textile architecture. In this paper, we present a body-worn lower-extremity soft exosuit and a new control approach that can independently control the level of assistance that is provided during negative- and positive-power periods at the ankle. The exosuit was designed to create load paths assisting ankle plantarflexion and hip flexion, and the actuation system transmits forces from the motors to the suit via Bowden cables. A load cell and two gyro sensors per leg are used to measure real-time data, and the controller performs position control of the cable on a step-by-step basis with respect to the power delivered to the wearer's ankle by controlling two force parameters, the pretension and the active force. Human subjects testing results demonstrate that the controller is capable of modulating the amount of power delivered to the ankle joint. Also, significant reductions in metabolic rate (11%-15%) were observed, which indicates the potential of the proposed control approach to provide benefit to the wearer during walking
A Machine Learning Approach for Next Step Prediction in Walking using On-Body Inertial Measurement Sensors
No full textThis thesis presents the development and implementation of a machine learning prediction model for concurrently aggregating interval linear step distance predictions before future foot placement. Specifically, on-body inertial measurement units consisting of accelerometers, gyroscopes, and magnetometers, through integrated development by Xsens, are used for measuring human walking behavior in real-time. The data collection process involves measuring activity from two subject participants who travel an intended course consisting of flat, stair, and sloped walking elements. This work discusses the formulation of the ensemble machine learning prediction algorithm, real-time application design considerations, feature extraction and selection, and experimental testing under which this system performed several different test case conditions. It was found that the system was able to predict the linear step distances for 47.2% of 1060 steps within 7.6cm accuracy, 67.5% of 1060 steps within 15.2cm accuracy, and 75.8% of 1060 steps within 23cm. For separated flat walking, it was found that 93% of the 1060 steps have less than 25% error, and 75% of the 1060 steps have less than 10% error which is an improvement over the commingled data set. Future applications and work to expand upon from this system are discussed for improving the results discovered from this work.Master of ScienceThis thesis presents the development and implementation of a machine learning prediction model for determining the stepping distance of future steps in real-time walking before their placement occurs. Specialized sensor units for measuring human motion activity are worn on the body for collecting and characterizing human walking behavior in real-time. Two subject participants are asked to walk a planned course consisting of flat, stair, and sloped walking elements. This work discusses the prediction algorithm voting scheme, real-time application design considerations, descriptive data elements for the algorithm, and experimental testing under which this system performed several different test case conditions. Detailed experimental tests are concluded in order to fully understand the extent of the system’s performance and the behaviors it exhibits throughout. The approach explored in this work enables researchers and roboticists to develop improvements and construct variations which may become superior to this method
Analysis of frequency-smearing models simulating hearing loss
No full textThesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.Includes bibliographical references (leaves 189-191).by Alan T. Asbeck.M.Eng
Scaling hard vertical surfaces with compliant microspine arrays
No full textA new approach for climbing hard vertical surfaces has been developed that allows a robot to scale concrete, stucco, brick and masonry walls without using suction or adhesives. The approach is inspired by the mechanisms observed in some climbing insects and spiders and involves arrays of microspines that catch on surface asperities. The arrays are located on the toes of the robot and consist of a tuned, multi-link compliant suspension. In this paper we discuss the fundamental issues of spine allometric scaling versus surface roughness and the suspension needed to maximize the probability that each spine will find a useable surface irregularity and to distribute climbing tensile and shear loads among many spines. The principles are demonstrated with a new climbing robot that can scale a wide range of exterior walls
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