Downloads & Free Reading Options - Results

The comorbidity of developmental coordination disorder (DCD) has been reported to be higher in other neurodevelopmental disorders, such as autism spectrum disorders (ASD) (APA, 2013). Motor coordination impairments affect daily activities (e.g., buttoning, use of eating utensils such as chopsticks, and writing). Early intervention is needed to prevent the loss of self-esteem and encourage social participation. Predictive motor control has been postulated as one of the factors contributing to impaired motor coordination in neurodevelopmental disorders (Adams et al., 2017). However, individual differences in motor function in early childhood are much greater than in adulthood, making it difficult to determine predictive motor control problems. The purpose of this exploratory study is to investigate more specific features of predictive motor control deficits and develop new objective assessment indicators for clinical implementation. The study focuses on motor planning in early childhood as predictive motor control. A previous study has reported that children with DCD show problems in force control (i.e., less steady force) (Smits-Engelsman et al., 2008), and another previous study has reported poor ability to integrate somatosensory feedback information during a visually guided precision grip force test in children with ASD (Shafer et al., 2021). This study attempts to measure grasping force control and variance of the force during reach-to-grasp and transfer tasks.

As a biological tissue, muscle adapts to the demands of usage. One traditional way of assessing the extent of this adaptation has been to examine the effects of an altered-activity protocol on the physiological properties of muscles. However, in order to accurately interpret the changes associated with an activity pattern, it is necessary to employ an appropriate control model. A substantial literature exists which reports altered-use effects by comparing experimental observations with those from animals raised in small laboratory cages. Some evidence suggests that small-cage-reared animals actually represent a model of reduced use. For example, laboratory animals subjected to limited physical activity have shown resistance to insulin-induced glucose uptake which can be altered by exercise training. This project concerned itself with the basic mechanisms underlying muscle atrophy. Specifically, the project addressed the issue of the appropriateness of rats raised in conventional-sized cages as experimental models to examine this phenomenon. The project hypothesis was that rats raised in small cages are inappropriate models for the study of muscle atrophy. The experimental protocol involved: 1) raising two populations of rats, one group in conventional (small)-sized cages and the other group in a much larger (133x) cage, from weanling age (21 days) through to young adulthood (125 days); 2) comparison of size- and force-related characteristics of selected test muscles in an acute terminal paradigm.

Fluid logic control circuit operates a pneumatic nutator actuator motor. It has no moving parts and consists of connected fluid interaction devices. The operation of this circuit demonstrates the ability of fluid interaction devices to operate in a complex combination of series and parallel logic sequence.

Ford tri-motor spraying DDT. Western spruce budworm control project. Powder River control unit, Oregon. Photo by: R.B. Pope Date: July 1955 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Portland Station Collection; La Grande, Oregon. Image: PS-1428 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. For further information about historical western spruce budworm activity and control projects see: Dolph, Robert E. 1980. Budworm activity in Oregon and Washington, 1947-1979. R6-FIDM-033-1980. Portland, OR: US Dept. of Agriculture, Forest Service, Pacific Northwest Region, Forest Insect and Disease Management. 54 p. For related historical program information see: archive.org/details/AerialForestInsectAndDiseaseDetection... Johnson, J. 2016. Aerial forest insect and disease detection surveys in Oregon and Washington 1947-2016: The survey. Gen. Tech. Rep. R6-FHP-GTR-0302. Portland, OR: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. 280 p. For additional historical forest entomology photos, stories, and resources see the Western Forest Insect Work Conference site: wfiwc.org/content/history-and-resources Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Ford tri-motor spraying DDT. Western spruce budworm control project. Powder River control unit, Oregon. Photo by: R.B. Pope Date: July 1955 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Portland Station Collection; La Grande, Oregon. Image: PS-1428 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. For further information about historical western spruce budworm activity and control projects see: Dolph, Robert E. 1980. Budworm activity in Oregon and Washington, 1947-1979. R6-FIDM-033-1980. Portland, OR: US Dept. of Agriculture, Forest Service, Pacific Northwest Region, Forest Insect and Disease Management. 54 p. For related historical program information see: archive.org/details/AerialForestInsectAndDiseaseDetection... Johnson, J. 2016. Aerial forest insect and disease detection surveys in Oregon and Washington 1947-2016: The survey. Gen. Tech. Rep. R6-FHP-GTR-0302. Portland, OR: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. 280 p. For additional historical forest entomology photos, stories, and resources see the Western Forest Insect Work Conference site: wfiwc.org/content/history-and-resources Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Ford tri-motor spraying DDT. Western spruce budworm control project. Powder River control unit, Oregon. Photo by: R.B. Pope Date: July 1955 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Portland Station Collection; La Grande, Oregon. Image: PS-1428 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. For further information about historical western spruce budworm activity and control projects see: Dolph, Robert E. 1980. Budworm activity in Oregon and Washington, 1947-1979. R6-FIDM-033-1980. Portland, OR: US Dept. of Agriculture, Forest Service, Pacific Northwest Region, Forest Insect and Disease Management. 54 p. For related historical program information see: archive.org/details/AerialForestInsectAndDiseaseDetection... Johnson, J. 2016. Aerial forest insect and disease detection surveys in Oregon and Washington 1947-2016: The survey. Gen. Tech. Rep. R6-FHP-GTR-0302. Portland, OR: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. 280 p. For additional historical forest entomology photos, stories, and resources see the Western Forest Insect Work Conference site: wfiwc.org/content/history-and-resources Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Ford tri-motor spraying DDT. Western spruce budworm control project. Powder River control unit, Oregon. Photo by: R.B. Pope Date: July 1955 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Portland Station Collection; La Grande, Oregon. Image: PS-1428 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. For further information about historical western spruce budworm activity and control projects see: Dolph, Robert E. 1980. Budworm activity in Oregon and Washington, 1947-1979. R6-FIDM-033-1980. Portland, OR: US Dept. of Agriculture, Forest Service, Pacific Northwest Region, Forest Insect and Disease Management. 54 p. For related historical program information see: archive.org/details/AerialForestInsectAndDiseaseDetection... Johnson, J. 2016. Aerial forest insect and disease detection surveys in Oregon and Washington 1947-2016: The survey. Gen. Tech. Rep. R6-FHP-GTR-0302. Portland, OR: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. 280 p. For additional historical forest entomology photos, stories, and resources see the Western Forest Insect Work Conference site: wfiwc.org/content/history-and-resources Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

Ford tri-motor spraying DDT. Western spruce budworm control project. Powder River control unit, Oregon. Photo by: R.B. Pope Date: July 1955 Credit: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. Collection: Portland Station Collection; La Grande, Oregon. Image: PS-1428 To learn more about this photo collection see: Wickman, B.E., Torgersen, T.R. and Furniss, M.M. 2002. Photographic images and history of forest insect investigations on the Pacific Slope, 1903-1953. Part 2. Oregon and Washington. American Entomologist, 48(3), p. 178-185. For further information about historical western spruce budworm activity and control projects see: Dolph, Robert E. 1980. Budworm activity in Oregon and Washington, 1947-1979. R6-FIDM-033-1980. Portland, OR: US Dept. of Agriculture, Forest Service, Pacific Northwest Region, Forest Insect and Disease Management. 54 p. For related historical program information see: archive.org/details/AerialForestInsectAndDiseaseDetection... Johnson, J. 2016. Aerial forest insect and disease detection surveys in Oregon and Washington 1947-2016: The survey. Gen. Tech. Rep. R6-FHP-GTR-0302. Portland, OR: USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection. 280 p. For additional historical forest entomology photos, stories, and resources see the Western Forest Insect Work Conference site: wfiwc.org/content/history-and-resources Image provided by USDA Forest Service, Pacific Northwest Region, State and Private Forestry, Forest Health Protection: www.fs.usda.gov/main/r6/forest-grasslandhealth

PWM control with servo motor coming soon...

PWM control with servo motor coming soon...

PWM control with servo motor coming soon...

PWM control with servo motor coming soon...

ETH D-MAVT Prof. Fumiya Iida lecture video 11/10/2011

ETH D-MAVT Prof. Fumiya Iida lecture video 04/10/201

ETH Zurich lecture on Bio-Inspired Motor Control by Dr. Amir Jafari from 2012/11/20 (lecture 10). Topic: Bio-inspired actuators. For more information visit www.birl.ethz.ch.

ETH Zurich lecture on Bio-Inspired Motor Control by Dr. Amir Jafari from 2012/11/20 (lecture 10). Topic: Bio-inspired actuators. For more information visit www.birl.ethz.ch.

This article is from PLoS ONE , volume 9 . Abstract Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

This article is from PLoS ONE , volume 9 . Abstract Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

No Abstract Available

147 p., pag. multiple

ETH D-MAVT Prof. Fumiya Iida lecture video 22/11/2011

ETH D-MAVT Prof. Fumiya Iida lecture video 29/11/2011

ETH D-MAVT Prof. Fumiya Iida lecture video 04/10/2011

Introduction: Low back pain (LBP) is a heterogeneous and multifactorial condition that represents a major health concern.[1,2] Motor control dysfunction is thought to play a significant role in the persistence of pain for a substantial subgroup of LBP patients.[3–5] In fact, the quality of movement of people with LBP differs significantly from that of people without LBP,[6] and accordingly, the examination of lumbopelvic motor control and movement quality serves as a basic component of the physical examination for all patients with LBP.[7–9] Although there are several clinical test batteries and observation lists available to assess motor control and movement quality,[10–14] there is currently no consensus on a standardized and validated approach in clinical practice. To effectively incorporate these aspects into the management of LBP, clear guidance on measurement is needed. Given the absence of a criterion standard method, content validity must be prioritized to ensure that assessment tools comprehensively capture all relevant dimensions.[15] This requires fostering a shared understanding of these constructs to enable rigorous content validity research, avoiding incomplete or misguided treatment interventions. Therefore, this proposed review aims to answer the following research question: What components of lumbo-pelvic motor control and movement quality are included in standardized measures whose validity and reliability have been established in adult populations in the context of LBP? Previous reviews in this area have primarily focused on reliability and construct validity,[10,13,16–18] with little attention given to content validity. Using a newly developed ICF-based framework,[19] this review will be the first to identify existing validated standardized measures and determine which components of MC and MQ are captured by each tool. This structured overview aims to highlight knowledge gaps in the current understanding of these measures and provide a foundation for future research to develop more comprehensive and valid assessment tools. Methods: The proposed scoping review will be conducted in accordance with the JBI methodology for scoping reviews and will follow the PRISMA-ScR guidelines.[20,21] Eligibility criteria: The PCC framework has been used to develop eligibility criteria. At level 1 (title and abstract screening), screening criteria will include descriptive studies (1) focussing on direct measurement of lumbopelvic MC or MQ, (2) in an adult population with NSLBP and (3) published in English or German language (for feasibility reasons). At level 2 (full-text screening), the following more specific criteria will be used for screening: Types of evidence sources: In accordance with Sibley and colleagues,[22] we will be searching for each index publication – a measure’s first publication presenting its development and/or initial psychometric evaluations. Participants/Population: Only studies including adult participants with chronic/recurrent LBP (or pain developers in longitudinal studies) will be considered for inclusion. Studies assessing patients with relevant systemic, neurologic and/or psychiatric diseases will be excluded. Concept: Descriptive studies, having a stated objective or commonly used tool to directly assess lumbo-pelvic MC or MQ, having (1) both a standardized testing protocol and standardized evaluation criteria, and (2) a minimum of 1 psychometric property (validity or reliability) evaluated (for quality assessment purposes, as argued by Sibley and colleagues[22]). Context: The clinical setting is of interest for this review. No methods for long-term measurement of continuous movement and posture behaviour in daily life will be considered. Search strategy & information sources: As recommended, the general search strategy will consist of three steps.[20] After an initial preliminary search of the databases MEDLINE (PubMed) and CINAHL, a second search will then be undertaken across four relevant databases (MEDLINE (Pubmed), CINAHL, Web of Science, Scopus). Thirdly, the reference lists of identified reports and articles will be searched for additional sources. In addition, a hand-search of the reference lists of relevant systematic reviews in the field [10,13,16–18,23] and a Google Scholar search using modified search terms (by screening the first 200 results) will be conducted. If further clarification or information is required, the authors will be contacted. Search dates will be documented as well as any contact with authors of the chosen studies. Source of evidence selection: Following the search, titles/abstracts and full-texts will be independently screened in two steps by two reviewers (GP and MK, with conflicts resolved by discussion / a third reviewer). The inclusion criteria at each stage will be pilot tested on a random sample using the online tool “Cadima”, which will aid the entire selection process. The names of all measures identified at abstract screening stage will be recorded for manual cross-checking and hand search for the index publication. At the stage of full-text screening, the reasons for the exclusion of articles will be stated. Data extraction A preliminary draft of a charting table has been created, which will be subjected to a pilot test on a randomly selected sample of five articles. The results of this test will be used to inform any necessary revisions to the table. It includes 1) Name of measure 2) Reference 3) Preliminary psychometric properties evaluated 4) Stated purpose of measure 5) Components of MC/MQ purportedly assessed 6) Development methods 7) Target population 8) Number of items 9) Evaluation parameters 10) Brief description of measurement procedure 11) ICF based components of MC & MQ a) MC functions (MCF) b) MCF specified c) MQ domains Analysis and presentation of results The components of MC and MQ evaluated in each measure will be explored by coding the individual items and tasks of the respective measure according to a newly developed ICF-based framework.[19] Two investigators will independently review the tasks and scoring criteria of each measure and will identify on a binary scale (yes/no) which components of motor control (“motor control functions”) and which components of movement quality (“movement quality domains”) are included in each measure. Individual components will be defined as included if they are inherent to task performance, even if not explicitly part of the measure’s evaluation criteria. Disagreements will be resolved through consensus discussion or a third reviewer. The extracted data will be synthesised and integrated into a series of visual outputs (tables and figure of framework), which will present a comprehensive overview of the standardised MC and MQ measures and their respective components. [1] Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is and why we need to pay attention. The Lancet 2018;391:2356–67. [2] Foster NE, Anema JR, Cherkin D, Chou R, Cohen SP, Gross DP, et al. Prevention and treatment of low back pain: evidence, challenges, and promising directions. The Lancet 2018;391:2368–83. [3] O’Sullivan P. Diagnosis and classification of chronic low back pain disorders: maladaptive movement and motor control impairments as underlying mechanism. Man Ther 2005;10:242–55. https://doi.org/10.1016/j.math.2005.07.001. [4] VAN DIEEN JH, REEVES P, KAWCHUK G, VAN DILLEN LR, HODGES PW. Analysis of Motor Control in Patients With Low Back Pain: A Key to Personalized Care? Journal of Orthopaedic & Sports Physical Therapy 2019;49:380–8. https://doi.org/10.2519/jospt.2019.7916. [5] Cholewicki J, Breen A, Popovich Jr JM, Reeves NP, Sahrmann SA, Van Dillen LR, et al. Can biomechanics research lead to more effective treatment of low back pain? A point-counterpoint debate. Journal of Orthopaedic & Sports Physical Therapy 2019;49:425–36. [6] Laird RA, Gilbert J, Kent P, Keating JL. Comparing lumbo-pelvic kinematics in people with and without back pain: a systematic review and meta-analysis. BMC Musculoskelet Disord 2014;15:229. https://doi.org/10.1186/1471-2474-15-229. [7] Biely SA, Silfies SP, Smith SS, Hicks GE. Clinical observation of standing trunk movements: what do the aberrant movement patterns tell us? J Orthop Sports Phys Ther 2014;44:262–72. https://doi.org/10.2519/jospt.2014.4988. [8] Dankaerts W, Sullivan PO, Burnett A, Straker L, Davey P, Gupta R. Discriminating Healthy Controls and Two Clinical Subgroups of Nonspecific Chronic Low Back Pain Patients Using Trunk Muscle Activation and Lumbosacral Kinematics of Postures and Movements A Statistical Classification Model 2009;34:1610–8. [9] Van Dillen LR, Sahrmann SA, Norton BJ, Caldwell CA, Fleming DA, McDonnell MK, et al. Reliability of physical examination items used for classification of patients with low back pain. Phys Ther 1998;78:979–88. [10] Schulz J, Vitt E, Niemier K. A systematic review of motor control tests in low back pain based on reliability and validity. J Bodyw Mov Ther 2022;29:239–50. https://doi.org/10.1016/j.jbmt.2021.11.005. [11] Skjaerven LH, Gard G, Sundal MA, Strand LI. Reliability and validity of the Body Awareness Rating Scale (BARS), an observational assessment tool of movement quality. The European Journal of Physiotherapy 2015;17:19–28. [12] Kaarbo MB, Haugstad GK, Stubhaug A, Wojniusz S. The Standardised Mensendieck Test as a tool for evaluation of movement quality in patients with nonspecific chronic low back pain. Scand J Pain 2018;18:203–10. https://doi.org/10.1515/sjpain-2018-0028. [13] van Dijk MJH, Smorenburg NTA, Heerkens YF, Mollema J, Kiers H, Nijhuis-van der Sanden MWG, et al. Assessment instruments of movement quality in patients with non-specific low back pain: A systematic review and selection of instruments. Gait Posture 2020;76:346–57. https://doi.org/10.1016/j.gaitpost.2019.12.027. [14] van Dijk MJH, van der Wal AM, Mollema J, Visser B, Kiers H, Heerkens Y, et al. The Observable Movement Quality scale for patients with low back pain (OMQ-LBP): validity and reliability in a primary care setting of physical therapy. BMC Musculoskelet Disord 2023;24:1–16. https://doi.org/10.1186/s12891-023-06784-1. [15] Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: An international Delphi study. Quality of Life Research 2010;19:539–49. https://doi.org/10.1007/s11136-010-9606-8. [16] Abdelnaeem AO, Rehan Youssef A, Mahmoud NF, Fayaz NA, Vining R. Psychometric properties of chronic low back pain diagnostic classification systems: a systematic review. Eur Spine J 2021;30:957–89. https://doi.org/10.1007/s00586-020-06712-0. [17] Ferrari S, Manni T, Bonetti F, Vanti C. Reliability and diagnostic accuracy of the clinical tests for lumbar instability: A systematic review. European Spine Journal 2014;23:S575. https://doi.org/10.1007/s00586-014-3446-0. [18] Ehrenbrusthoff K, Ryan CG, Gruneberg C, Martin DJ. A systematic review and meta-analysis of the reliability and validity of sensorimotor measurement instruments in people with chronic low back pain. Musculoskelet Sci Pract 2018;35:73–83. https://doi.org/10.1016/j.msksp.2018.02.007. [19] Pfluegler G, Klinger M, Ley C, Luedtke K, Schüßler B, Sturma A. Assessing motor control and movement quality in patients with non-specific low back pain: a scoping review and development of an ICF-based conceptual framework. BMJ Open 2025;15:e091206. [20] Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: scoping reviews. JBI Manual for Evidence Synthesis 2020;169:467–73. [21] Tricco AC, Lillie E, Zarin W, O’Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018;169:467–73. [22] Sibley KM, Beauchamp MK, Van Ooteghem K, Straus SE, Jaglal SB. Using the systems framework for postural control to analyze the components of balance evaluated in standardized balance measures: A scoping review. Arch Phys Med Rehabil 2015;96:122-132.e29. https://doi.org/10.1016/j.apmr.2014.06.021. [23] Villafañe JH, Zanetti L, Isgrò M, Cleland JA, Bertozzi L, Gobbo M, et al. Methods for the assessment of neuromotor capacity in non-specific low back pain: Validity and applicability in everyday clinical practice. J Back Musculoskelet Rehabil 2015;28:201–14. https://doi.org/10.3233/BMR-140533.

A scoping review protocol

A closed-loop control system for controlling the operation of one or more servo motors or other controllable devices is described. The system employs a fiber optics link immune to electromagnetic interference, for transmission of control signals from a controller or controllers at a remote station to the power electronics located in proximity to the motors or other devices at the local station. At the remote station the electrical control signals are time-multiplexed, converted to a formatted serial bit stream, and converted to light signals for transmission over a single fiber of the fiber optics link. At the local station, the received optical signals are reconstructed as electrical control signals for the controlled motors or other devices. At the local station, an encoder sensor linked to the driven device generates encoded feedback signals which provide information as to a condition of the controlled device. The encoded signals are placed in a formatted serial bit stream, multiplexed, and transmitted as optical signals over a second fiber of the fiber optic link which closes the control loop of the closed-loop motor controller. The encoded optical signals received at the remote station are demultiplexed, reconstructed and coupled to the controller(s) as electrical feedback signals.

The comorbidity of developmental motor coordination impairments has been reported to be higher in neurodevelopmental disorders, such as autism spectrum disorders (ASD) (APA, 2013). Motor coordination impairments affect daily activities (e.g., buttoning, use of eating utensils such as chopsticks, and writing). Early intervention is needed to prevent the loss of self-esteem and encourage social participation. Predictive motor control has been postulated as one of the factors contributing to impaired motor coordination in neurodevelopmental disorders (Adams et al., 2017). However, individual differences in motor function in early childhood are much greater than in adulthood, making it difficult to determine predictive motor control problems. The purpose of this exploratory study is to investigate more specific features of predictive motor control deficits and develop new objective assessment indicators for clinical implementation. The study focuses on motor simulation in early childhood as predictive motor control. Several previous studies have reported poor performance in novel and meaningless imitation tasks, which is an indicator of motor simulation, in children with ASD (Pittet et al., 2022; Kilroy et al., 2019, 2021). This study attempts to measure visual attention during imitation tasks and assess the quality of motor performance, such as motor units.

Enclosure for a 12V Motor Controller. Can be resized with changes to the Openscad code.

CONTENTS: Upper extremity prosthetics research: Experimental investigation of the Heidelberg Pneumatic Arm; experimental investigation of the Northwestern Attitudinal Controlled Elbow; experimental investigation of the French Electric Hand; exploration of minor surgical methods for developing new body control sites; exploration of new harnessing techniques for transducers; evaluation of tentative design criteria for externally powered prostheses. Research on sensory motor control: Experimental investigations of the AIPR pneumatic Arm; experimental investigations of functional muscle isolation by training; development of an external logic system for prosthetic motion control. Research on the performance of human operators of tracking system: Function generator; reports; experimentation. Research on myoelectric control systems: Unidimensional EMG control to a self-paced input; unidimensional EMG control to a step input.

From Case: 802914DN  E-19 Electrical Maintenance Training Motors and Motor Controllers AC Motor Control Maintenance 60 Minutes

From Case: 802914DN  E-19 Electrical Maintenance Training Motors and Motor Controllers AC Motor Control Maintenance 60 Minutes

From Case: 802914DN  E-19 Electrical Maintenance Training Motors and Motor Controllers AC Motor Control Maintenance 60 Minutes

Study 1 - Adaptation of jaw kinematics to a perturbing force-field during speech production in adults who do and do not stutter.

Combination of very good chain fastening with Z alloy and X-axis motor support bearing. the bearing should use 625ZZ. My friend Christian designed it for me.

Made this to hold my Peco control box in. I cut the screw holder off of the bottom of the control unit but it's up to you (be careful you don't cut the strip wire). It's a loose fit for the control because my first effort was not wide enough (the box wouldn't go in).

ETH D-MAVT Prof. Fumiya Iida lecture video 18/10/2011

ETH D-MAVT Prof. Fumiya Iida lecture video 15/11/2011

An English version of an in-house seminar given at: IRP Systems, Ness Ziona, Israel

ETH Zurich lecture on Bio-Inspired Motor Control by Prof. Fumiya Iida from 2012/09/18 (lecture 1). For more information visit www.birl.ethz.ch.

The present experiment is designed to test the Motor-Cognitive Model (MCM) of motor imagery, as opposed to the Functional Equivalence Model (FEM). The FEM argues that motor imagery and overt action share the same internal processes, leading to a general correspondence in their timing. In contrast, the MCM argues that, although they use the same internal representation, motor imagery and overt action differ in their execution phase. Indeed, during overt action, sensory feedback allows for online control, that is one can adjust and correct the movement in flight. During motor imagery, the absence of online feedback hinders such process: the mental image is instead monitored at the cost of executive resources (Glover et al., 2020; Glover & Baran, 2017; Martel & Glover, 2023). Accordingly, one major tenet of the MCM is that motor imagery and physical actions should not be similarly affected by factors that influence online control. Specifically, we focus here on the emphasis on planning or online control. For any given physical action, there is a balance between pre-movement planning and online control, manifest as an inverse relationship between how much each process ultimately contributes to a given physical action. For example, a movement may be entirely ballistic (i.e., pre-planned), such as tapping a keyboard with a finger. In this instance, movement duration is too short for online feedback loops to operate, and as such the action is executed entirely as planned. In contrast, a movement may have very little planning but a heavy emphasis on online control, such as when trying to catch a ball on a windy day. In this case, the movement of the target is unpredictable, and successfully catching it requires a continuous updating of the information used to control the ongoing movement of the catching limb and hand. Thus, for physical actions, the relative emphasis on online control is a factor that can vary markedly from task to task; movements may fall anywhere along a continuum between being entirely pre-planned and entirely controlled online. For a physical movement, when planning representations are weaker, such as during an unfamiliar action, there is an increased emphasis on online control, as movement planning will bear less fidelity with the final output of the action. This often results in slowing of the movement and increases in kinematic indices of online control such as the number of online adjustments. For motor imagery, a weak planning representation cannot be compensated by online control, but instead the motor image must be elaborated using executive functions. Executive functions are ill-equipped to simulate actions in the absence of a strong motor representation; as a consequence the imagined movement takes longer. To examine the above prediction, right-handed participants will either reach to grasp a small disc with their left/right hand, then place it through a slot into a small cylinder, or imagine doing so, a paradigm similar to one we have used before (see Figure 1; Glover et al., 2020; Glover & Baran, 2017; Martel & Glover, 2023). The opening of the cylinder will be narrowed into a slot (see Figure 1) in order to increase the precision requirements of the task, emphasizing online control. As before, participants will index the beginning and the end of their movement using keypresses (with their right/left index), allowing a comparison of the different conditions. One group of participants will perform the overt action, with their dominant right hand, and with their left hand. The use of the non-dominant hand is intended to force the use of a weaker motor representation based on the idea that the dominant hand will be much more practiced at this type of task than the non-dominant hand; the use of a weaker motor representation will concomitantly increase the emphasis on online control. The second group will imagine grasping and placing the disc in the same two conditions. Participants in the motor imagery group will be instructed to imagine as rich a visual and kinesthetic image as possible. Each group will complete 12 trials per condition for a total of 24 trials, presented in two counterbalanced blocks across participants. A small number of practice trials will precede each block to familiarize participants with the task. The experimental setup consists of a 50 × 90 cm table, on which will sit a computer keyboard, a wooden box (dimensions: 15.7 × 42 × 22 cm) with a grey plastic tube inside (dimensions: 15 × 4.2 cm), covered with black tape apart from a centrally-located 1.2 × 4.2 cm slot and a small white plastic disc (the slot being 2mm larger than the disc; Figure 1). Both the keyboard and the wooden box will be aligned on the left/right side of the table. The wooden box will be placed in the top left/right corner of the table, with 7cm being outside of the table surface, while the keyboard will be placed adjoining it. The computer keyboard will be placed in the bottom left/right corner of the table, so that the participant can use her left/right index finger to press on the ‘L’ key without the keyboard being in the way. The starting position will be a yellow square sticker (0.5 × 0.5 cm), positioned 52 cm away from the left/right side of the table, close to the participants. Participants will sit with their body midline aligned to the starting position. The disc (dimensions: 1 × 4 cm) will be positioned with its centre 23.5 cm to the right and 22 cm forward of the starting position. The grey cylinder will be located inside the box, with its opening reduced in width so that it fits the disc in one position only. Participants in the overt action group will have their movements recorded using the Polhemus motion tracking system, with markers placed on the dorsal surface of the fingernails of the thumb and index finger of their moving hand. The Polhemus tracks the position of the markers at 120Hz, and stores data of the instantaneous x, y, and z coordinates of each marker to PC for later analysis offline.

ETH Zurich lecture on Bio-Inspired Motor Control by Prof. Fumiya Iida from 2012/09/25 (lecture 2). Topic: Underactuated robotics/ conceptual models. For more information visit www.birl.ethz.ch.

ETH Zurich lecture on Bio-Inspired Motor Control by Prof. Fumiya Iida from 2012/09/25 (lecture 2). Topic: Underactuated robotics/ conceptual models. For more information visit www.birl.ethz.ch.

The NASA Glenn Research Center (GRC) has been working to advance the technology necessary for a flywheel energy storage system for the past several years. Flywheels offer high efficiency, durability, and near-complete discharge capabilities not produced by typical chemical batteries. These characteristics show flywheels to be an attractive alternative to the more typical energy storage solutions. Flywheels also offer the possibility of combining what are now two separate systems in space applications into one: energy storage, which is currently provided by batteries, and attitude control, which is currently provided by control moment gyroscopes (CMGs) or reaction wheels. To date, NASA Glenn research effort has produced the control algorithms necessary to demonstrate flywheel operation up to a rated speed of 60,000 RPM and the combined operation of two flywheel machines to simultaneously provide energy storage and single axis attitude control. Two position-sensorless algorithms are used to control the motor/generator, one for low (0 to 1200 RPM) speeds and one for high speeds. The algorithm allows the transition from the low speed method to the high speed method, but the transition from the high to low speed method was not originally included. This leads to a limitation in the existing motor/generator control code that does not allow the flywheels to be commanded to zero speed (and back in the negative speed direction) after the initial startup. In a multi-flywheel system providing both energy storage and attitude control to a spacecraft, speed reversal may be necessary.

Environmental testing and static firing of pitch control motor for Apollo spacecraft launch escape system

Effects of cobalt 60 gamma radiation on silicon control rectifiers used in switching mode

This paper deals with the design of different control schemes for sensorless vector control of induction motor.Induction motor is most widely used A.C. Motor but the major draw back is flux and torque cannot be controlled individually.This can be obtained by implementing sensorless vector control methods.The control strategy of induction motor is by different controllers like conventional control methods and artificial intelligence control methods.The conventional control methods are sensitive to parameter changes and will not be accurate. This paper proposes to design a controller to over come the above draw backs by using intelligent control techniques like fuzzy logic, artificial neutral networks (ANN) and genetic algorithm (GA). The above conventional control methods are compared with intelligent control techniques. The simulation studies are carried out using Matlab/Simulink and the wave forms for speed, torque and voltage components for various controlles are plotted. Numerical analysis for speed and torque components considering parameters like peakover shoot and peak time are presented.

Available from National Technical Information Service, Springfield, Va

This paper presents archimedes optimization algorithm (AOA) and dispersive flies optimization (DFO) to optimally tune gain parameters of PID control scheme in order to regulate DC motor’s speed. These suggested techniques tune the controller by the minimization of the fitness function represented by the integral of time multiplied by absolute error (ITAE). The modelling and simulation are carried out in MATLAB/Simulink. The transient response of unit step input obtained from AOA-PID-ITAE and DFO-PID-ITAE controllers were compared to those obtained from Ziegler-Nichols (ZN) method and particle swarm optimization (PSO). The results indicate that AOA-PID-ITAE and DFO-PID-ITAE are more efficient than ZN method and PSO in reducing rise time and settling time. Likewise, DFO converge faster to the optimal solution with lower overshoot than AOA and PSO.

This article is from Frontiers in Neural Circuits , volume 7 . Abstract How is the cerebellum capable of efficient motor learning and control despite very low firing of the inferior olive (IO) inputs, which are postulated to carry errors needed for learning and contribute to on-line motor control? IO neurons form the largest electrically coupled network in the adult human brain. Here, we discuss how intermediate coupling strengths can lead to chaotic resonance and increase information transmission of the error signal despite the very low IO firing rate. This increased information transmission can then lead to more efficient learning than with weak or strong coupling. In addition, we argue that a dynamic modulation of IO electrical coupling via the Purkinje cell-deep cerebellar neurons – IO triangle could speed up learning and improve on-line control. Initially strong coupling would allow transmission of large errors to multiple functionally related Purkinje cells, resulting in fast but coarse learning as well as significant effects on deep cerebellar nucleus and on-line motor control. In the late phase of learning decreased coupling would allow desynchronized IO firing, allowing high-fidelity transmission of error, resulting in slower but fine learning, and little on-line motor control effects.

This article is from Frontiers in Neural Circuits , volume 7 . Abstract How is the cerebellum capable of efficient motor learning and control despite very low firing of the inferior olive (IO) inputs, which are postulated to carry errors needed for learning and contribute to on-line motor control? IO neurons form the largest electrically coupled network in the adult human brain. Here, we discuss how intermediate coupling strengths can lead to chaotic resonance and increase information transmission of the error signal despite the very low IO firing rate. This increased information transmission can then lead to more efficient learning than with weak or strong coupling. In addition, we argue that a dynamic modulation of IO electrical coupling via the Purkinje cell-deep cerebellar neurons – IO triangle could speed up learning and improve on-line control. Initially strong coupling would allow transmission of large errors to multiple functionally related Purkinje cells, resulting in fast but coarse learning as well as significant effects on deep cerebellar nucleus and on-line motor control. In the late phase of learning decreased coupling would allow desynchronized IO firing, allowing high-fidelity transmission of error, resulting in slower but fine learning, and little on-line motor control effects.