Relevant bibliographies by topics / Force myography

Academic literature on the topic 'Force myography'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Force myography"

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Curcio, Brittney C., Nicholas V. Cirillo, and Michael Wininger. "Force Myography across Socket Material." Journal of Prosthetics and Orthotics 32, no. 1 (January 2020): 52–58. http://dx.doi.org/10.1097/jpo.0000000000000295.

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Jiang, Xianta, Lukas-Karim Merhi, and Carlo Menon. "Force Exertion Affects Grasp Classification Using Force Myography." IEEE Transactions on Human-Machine Systems 48, no. 2 (April 2018): 219–26. http://dx.doi.org/10.1109/thms.2017.2693245.

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Godiyal, Anoop Kant, Upinderpal Singh, Sneh Anand, and Deepak Joshi. "Analysis of force myography based locomotion patterns." Measurement 140 (July 2019): 497–503. http://dx.doi.org/10.1016/j.measurement.2019.04.009.

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Stokes, M. J., and P. A. Dalton. "Acoustic myography for investigating human skeletal muscle fatigue." Journal of Applied Physiology 71, no. 4 (October 1, 1991): 1422–26. http://dx.doi.org/10.1152/jappl.1991.71.4.1422.

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Sounds produced during voluntary isometric contractions of the quadriceps muscle were studied by acoustic myography (AMG) in five healthy adults. With the subject seated, isometric force, surface electromyography (EMG), and AMG were recorded over rectus femoris, and the EMG and AMG signals were integrated (IEMG and IAMG). Contractions lasting 5 s each were performed at 10, 25, 50, 60, 75, and 100% of maximum voluntary contraction (MVC) force. Fatigue was then induced by repeated voluntary contractions (10 s on, 10 s off) at 75% MVC until only 40% MVC could be sustained. After 15 min of rest, the different force levels were again tested in relation to the fresh MVC. Both before and after fatiguing activity the relationships between force and IEMG [r = 0.99 +/- 0.01 (SD), n = 10] and force and IAMG (r = 0.98 +/- 0.02) were linear. After activity, however, the slopes of the regression lines for force and IEMG increased (P less than 0.01) but those for force and IAMG remained the same (P greater than 0.05). The present results clarify the relationship between AMG and isometric force in fatigued muscle without the problem of fatigue-induced tremor, which hampered previous studies of prolonged activity. This study contributes to the validation of AMG and shows that it is a potentially useful method for noninvasive assessment of force production and fatigue. Further studies to establish the origin of AMG activity are required before AMG can be accepted for use in neuromuscular physiology or rehabilitation.
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Xiao, Zhen Gang, and Carlo Menon. "A Review of Force Myography Research and Development." Sensors 19, no. 20 (October 20, 2019): 4557. http://dx.doi.org/10.3390/s19204557.

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Information about limb movements can be used for monitoring physical activities or for human-machine-interface applications. In recent years, a technique called Force Myography (FMG) has gained ever-increasing traction among researchers to extract such information. FMG uses force sensors to register the variation of muscle stiffness patterns around a limb during different movements. Using machine learning algorithms, researchers are able to predict many different limb activities. This review paper presents state-of-art research and development on FMG technology in the past 20 years. It summarizes the research progress in both the hardware design and the signal processing techniques. It also discusses the challenges that need to be solved before FMG can be used in an everyday scenario. This paper aims to provide new insight into FMG technology and contribute to its advancement.
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Godiyal, Anoop Kant, Milton Mondal, Shiv Dutt Joshi, and Deepak Joshi. "Force Myography Based Novel Strategy for Locomotion Classification." IEEE Transactions on Human-Machine Systems 48, no. 6 (December 2018): 648–57. http://dx.doi.org/10.1109/thms.2018.2860598.

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Kadkhodayan, Anita, Xianta Jiang, and Carlo Menon. "Continuous Prediction of Finger Movements Using Force Myography." Journal of Medical and Biological Engineering 36, no. 4 (July 29, 2016): 594–604. http://dx.doi.org/10.1007/s40846-016-0151-y.

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Chu, Kelvin HT, Xianta Jiang, and Carlo Menon. "Wearable step counting using a force myography-based ankle strap." Journal of Rehabilitation and Assistive Technologies Engineering 4 (January 2017): 205566831774630. http://dx.doi.org/10.1177/2055668317746307.

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Introduction Step counting can be used to estimate the activity level of people in daily life; however, commercially available accelerometer-based step counters have shown inaccuracies in detection of low-speed walking steps (<2.2 km/h), and thus are not suitable for older adults who usually walk at low speeds. This proof-of-concept study explores the feasibility of using force myography recorded at the ankle to detect low-speed steps. Methods Eight young healthy participants walked on a treadmill at three speeds (1, 1.5, and 2.0 km/h) while their force myography signals were recorded at the ankle using a customized strap embedded with an array of eight force-sensing resistors. A K-nearest neighbour model was trained and tested with the recorded data. Additional three mainstream machine learning algorithms were also employed to evaluate the performance of force myography band as a pedometer. Results Results showed a low error rate of the step detection (<1.5%) at all three walking speeds. Conclusions This study demonstrates not only the feasibility of the proposed approach but also the potential of the investigated technology to reliably monitor low-speed step counting.
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Belyea, Alexander T., Kevin B. Englehart, and Erik J. Scheme. "A proportional control scheme for high density force myography." Journal of Neural Engineering 15, no. 4 (June 19, 2018): 046029. http://dx.doi.org/10.1088/1741-2552/aac89b.

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Prakash, Alok, Neeraj Sharma, and Shiru Sharma. "An affordable transradial prosthesis based on force myography sensor." Sensors and Actuators A: Physical 325 (July 2021): 112699. http://dx.doi.org/10.1016/j.sna.2021.112699.

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Dissertations / Theses on the topic "Force myography"

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Вонсевич, Костянтин Петрович. "Міографічна система біонічної руки з оптичною ідентифікацією типу поверхні." Doctoral thesis, Київ, 2020. https://ela.kpi.ua/handle/123456789/35729.

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Дисертаційна робота присвячена створенню міографічної системи протезної руки з розширеними можливостями рухів та жестів із розпізнаванням міоелектричних сигналів нейромережевим інтерфейсом та оптичним ідентифікатором контактної поверхні для дотику пальців. У роботі вдосконалено метод розпізнавання категорій фізіологічних рухів та жестів шляхом аналізу електро- та форс- міографічних сигналів мультирівневими штучними нейронними мережами, що дозволило підвищити точність класифікації жестів кисті руки. Вдосконалено метод розпізнавання контактної поверхні пальцем протезу шляхом оптичної ідентифікації із засобами концентрації оптичної енергії, що дало можливість підвищити достовірність ідентифікації структури об’єктів маніпуляції. Вдосконалено метод координації рухів протезу кисті руки на основі одночасної реєстрації і розпізнавання фізіологічних сигналів та сигналу оптичної ідентифікації.
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Books on the topic "Force myography"

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C, Cutter Nancy, and Kevorkian C. George, eds. Handbook of manual muscle testing. New York: McGraw-Hill, Health Professions Division, 1999.

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Book chapters on the topic "Force myography"

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Godiyal, Anoop Kant, Vinay Verma, Nitin Khanna, and Deepak Joshi. "Force Myography and Its Application to Human Locomotion." In Series in BioEngineering, 49–70. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9097-5_3.

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Ng, Him Wai, Xianta Jiang, Lukas-Karim Merhi, and Carlo Menon. "Investigation of the Feasibility of Strain Gages as Pressure Sensors for Force Myography." In Bioinformatics and Biomedical Engineering, 261–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_22.

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Victorino, M. N., X. Jiang, and C. Menon. "Wearable Technologies and Force Myography for Healthcare." In Wearable Technology in Medicine and Health Care, 135–52. Elsevier, 2018. http://dx.doi.org/10.1016/b978-0-12-811810-8.00007-5.

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Conference papers on the topic "Force myography"

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Godiyal, Anoop Kant, Srinivas Pandit, Amit Kumar Vimal, U. Singh, Sneh Anand, and Deepak Joshi. "Locomotion mode classification using force myography." In 2017 IEEE Life Sciences Conference (LSC). IEEE, 2017. http://dx.doi.org/10.1109/lsc.2017.8268158.

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Khatavkar, Rohan, Ashutosh Tiwari, Rishabh Bajpai, and Deepak Joshi. "Gait Step Length Classification Using Force Myography." In 2022 International Conference for Advancement in Technology (ICONAT). IEEE, 2022. http://dx.doi.org/10.1109/iconat53423.2022.9726014.

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Fujiwara, Eric, Matheus K. Gomes, Yu Tzu Wu, and Carlos K. Suzuki. "Identification of Dynamic Hand Gestures with Force Myography." In 2021 International Symposium on Micro-NanoMehatronics and Human Science (MHS). IEEE, 2021. http://dx.doi.org/10.1109/mhs53471.2021.9767134.

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Sakr, Maram, and Carlo Menon. "Study on the force myography sensors placement for robust hand force estimation." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8122807.

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Anvaripour, Mohammad, and Mehrdad Saif. "Controlling Robot Gripper Force By Transferring Human Forearm Stiffness Using Force Myography." In 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2018. http://dx.doi.org/10.1109/mwscas.2018.8623937.

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Jiang, Xianta, Hon T. Chu, Zhen G. Xiao, Lukas-Karim Merhi, and Carlo Menon. "Ankle positions classification using force myography: An exploratory investigation." In 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT). IEEE, 2016. http://dx.doi.org/10.1109/hic.2016.7797689.

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Barioul, Rim, Sameh Fakhfakh Gharbi, Muhammad Bilal Abbasi, Ahmed Fasih, Houda Ben-Jmeaa-Derbel, and Olfa Kanoun. "Wrist Force Myography (FMG) Exploitation for Finger Signs Distinguishing." In 2019 5th International Conference on Nanotechnology for Instrumentation and Measurement (NanofIM). IEEE, 2019. http://dx.doi.org/10.1109/nanofim49467.2019.9233484.

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Fujiwara, Eric, Yu Tzu Wu, Matheus K. Gomes, Willian H. A. Silva, and Carlos K. Suzuki. "Haptic Interface Based on Optical Fiber Force Myography Sensor." In 2019 IEEE Conference on Virtual Reality and 3D User Interfaces (VR). IEEE, 2019. http://dx.doi.org/10.1109/vr.2019.8797788.

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Fujiwara, Eric, Yu Tzu Wu, and Carlos K. Suzuki. "Assessment of Hand Posture and Grip Force by Optical Fiber Force Myography Sensor." In 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2020. http://dx.doi.org/10.1109/lifetech48969.2020.1570613711.

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Fujwara, Eric, Yu Tzu Wu, Caio S. Villela, Matheus K. Gomes, Marco C. P. Soares, Carlos K. Suzuki, Antonio Ribas Neto, and Eric Rohmer. "Design and Application of Optical Fiber Sensors for Force Myography." In 2018 SBFoton International Optics and Photonics Conference (SBFoton IOPC). IEEE, 2018. http://dx.doi.org/10.1109/sbfoton-iopc.2018.8610923.

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