{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T19:02:09Z","timestamp":1772823729889,"version":"3.50.1"},"reference-count":123,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100007950","name":"Tanta University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100007950","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique.<\/jats:p>\n                <jats:p><jats:bold>Graphical abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s11517-024-03019-w","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T07:02:19Z","timestamp":1706857339000},"page":"1313-1332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A survey on the state of the art of force myography technique (FMG): analysis and assessment"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5840-4659","authenticated-orcid":false,"given":"Omar","family":"Sherif","sequence":"first","affiliation":[]},{"given":"Mohamed Mahgoub","family":"Bassuoni","sequence":"additional","affiliation":[]},{"given":"Omar","family":"Mehrez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"3019_CR1","doi-asserted-by":"publisher","unstructured":"Accogli A, Grazi L, Crea S, Panarese A, Carpaneto J, Vitiello N, Micera S (2016) EMG-based detection of user\u2019s intentions for human-machine shared control of an assistive upper-limb exoskeleton. Biosyst Biorobotics 181\u2013185. https:\/\/doi.org\/10.1007\/978-3-319-46532-6_30","DOI":"10.1007\/978-3-319-46532-6_30"},{"issue":"4","key":"3019_CR2","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1109\/mra.2017.2747899","volume":"24","author":"C Ahmadizadeh","year":"2017","unstructured":"Ahmadizadeh C, Merhi L-K, Pousett B, Sangha S, Menon C (2017) Toward intuitive prosthetic control: Solving common issues using force myography, surface electromyography, and pattern recognition in a pilot case study. IEEE Robot Autom Mag 24(4):102\u2013111. https:\/\/doi.org\/10.1109\/mra.2017.2747899","journal-title":"IEEE Robot Autom Mag"},{"key":"3019_CR3","doi-asserted-by":"publisher","unstructured":"Anvaripour M, Saif M (2018) Hand gesture recognition using force myography of the forearm activities and optimized features. 2018 IEEE International Conference on Industrial Technology (ICIT). https:\/\/doi.org\/10.1109\/icit.2018.8352174","DOI":"10.1109\/icit.2018.8352174"},{"key":"3019_CR4","doi-asserted-by":"publisher","unstructured":"Anvaripour M, Khoshnam M, Menon C, Saif M (2020) FMG- and RNN-based estimation of motor intention of upper-limb motion in human-robot collaboration. Front Robot AI 7.\u00a0https:\/\/doi.org\/10.3389\/frobt.2020.573096","DOI":"10.3389\/frobt.2020.573096"},{"issue":"4","key":"3019_CR5","doi-asserted-by":"publisher","first-page":"1504","DOI":"10.3390\/s21041504","volume":"21","author":"M Asfour","year":"2021","unstructured":"Asfour M, Menon C, Jiang X (2021) A machine learning processing pipeline for reliable hand gesture classification of FMG signals with stochastic variance. Sensors 21(4):1504. https:\/\/doi.org\/10.3390\/s21041504","journal-title":"Sensors"},{"key":"3019_CR6","doi-asserted-by":"publisher","unstructured":"Barioul R, Ghribi SF, Ben Jmaa Derbel H, Kanoun O (2020) Four sensors bracelet for American Sign Language recognition based on wrist force myography. 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). https:\/\/doi.org\/10.1109\/civemsa48639.2020.9132742","DOI":"10.1109\/civemsa48639.2020.9132742"},{"key":"3019_CR7","doi-asserted-by":"publisher","unstructured":"Barioul R, Gharbi SF, Abbasi MB, Fasih A, Ben-Jmeaa-Derbel H, Kanoun O (2019) WRIST FORCE MYOGRAPHY (FMG) exploitation for finger signs distinguishing. 2019 5th International Conference on Nanotechnology for Instrumentation and Measurement (NanofIM). https:\/\/doi.org\/10.1109\/nanofim49467.2019.9233484","DOI":"10.1109\/nanofim49467.2019.9233484"},{"issue":"10","key":"3019_CR8","first-page":"469","volume":"62","author":"JV Basmajian","year":"1981","unstructured":"Basmajian JV (1981) Biofeedback in rehabilitation: a review of principles and practices. Arch Phys Med Rehabil 62(10):469\u2013475","journal-title":"Arch Phys Med Rehabil"},{"issue":"3","key":"3019_CR9","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/s10484-023-09586-2","volume":"48","author":"CK Behera","year":"2023","unstructured":"Behera CK, Reddy TK, Behera L, Birbaumer N, Ika K (2023) A meditation based cognitive therapy (HMBCT) for primary insomnia: a treatment feasibility pilot study. Appl Psychophysiol Biofeedback 48(3):369\u2013378. https:\/\/doi.org\/10.1007\/s10484-023-09586-2","journal-title":"Appl Psychophysiol Biofeedback"},{"key":"3019_CR10","doi-asserted-by":"publisher","unstructured":"Behera CK, Reddy TK, Behera L, Bhattacarya B (2014) \"Artificial neural network based arousal detection from sleep electroencephalogram data,\" 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, Malaysia, pp. 458\u2013462. https:\/\/doi.org\/10.1109\/I4CT.2014.6914226","DOI":"10.1109\/I4CT.2014.6914226"},{"issue":"11","key":"3019_CR11","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1109\/tbme.2019.2900415","volume":"66","author":"A Belyea","year":"2019","unstructured":"Belyea A, Englehart K, Scheme E (2019) FMG versus EMG: A comparison of usability for real-time pattern recognition based control. IEEE Trans Biomed Eng 66(11):3098\u20133104. https:\/\/doi.org\/10.1109\/tbme.2019.2900415","journal-title":"IEEE Trans Biomed Eng"},{"key":"3019_CR12","unstructured":"Benne (2022). Force sensing resistor (FSR) arduino tutorial (3 examples). Makerguides.com. Retrieved January 26, 2023, from http:\/\/www.makerguides.com\/fsr-arduino-tutorial"},{"issue":"2","key":"3019_CR13","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s40846-017-0303-8","volume":"38","author":"R Booth","year":"2017","unstructured":"Booth R, Goldsmith P (2017) A wrist-worn piezoelectric sensor array for gesture input. J Med Biol Eng 38(2):284\u2013295. https:\/\/doi.org\/10.1007\/s40846-017-0303-8","journal-title":"J Med Biol Eng"},{"key":"3019_CR14","doi-asserted-by":"publisher","unstructured":"Carbonaro N, Anania G, Bacchereti M, Donati G, Ferretti L, Pellicci G, Parrini G, Vitetta N, De Rossi D, Tognetti A (2014) An innovative multisensor controlled prosthetic hand. IFMBE Proc 93\u201396. https:\/\/doi.org\/10.1007\/978-3-319-00846-2_23","DOI":"10.1007\/978-3-319-00846-2_23"},{"key":"3019_CR15","doi-asserted-by":"publisher","unstructured":"Centracchio J (2022) \"A new piezoelectric sensor for force myography application,\" 2022 E-Health and Bioengineering Conference (EHB), Iasi, Romania, pp. 1\u20134.\u00a0https:\/\/doi.org\/10.1109\/EHB55594.2022.9991364","DOI":"10.1109\/EHB55594.2022.9991364"},{"key":"3019_CR16","doi-asserted-by":"publisher","unstructured":"Castellini C, Koiva R (2013) Using a high spatial resolution tactile sensor for intention detection. 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR). https:\/\/doi.org\/10.1109\/icorr.2013.6650365","DOI":"10.1109\/icorr.2013.6650365"},{"key":"3019_CR17","doi-asserted-by":"publisher","unstructured":"Castellini C, Ravindra V (2014) A wearable low-cost device based upon force-sensing resistors to detect single-finger forces. 5th IEEE RAS\/EMBS International Conference on Biomedical Robotics and Biomechatronics. https:\/\/doi.org\/10.1109\/biorob.2014.6913776","DOI":"10.1109\/biorob.2014.6913776"},{"issue":"2","key":"3019_CR18","doi-asserted-by":"publisher","first-page":"38","DOI":"10.3390\/technologies6020038","volume":"6","author":"C Castellini","year":"2018","unstructured":"Castellini C, K\u00f5iva R, Pasluosta C, Viegas C, Eskofier B (2018) Tactile myography: An off-line assessment of able-bodied subjects and one upper-limb amputee. Technologies 6(2):38. https:\/\/doi.org\/10.3390\/technologies6020038","journal-title":"Technologies"},{"issue":"1","key":"3019_CR19","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1007\/BF02351021","volume":"42","author":"C Cescon","year":"2004","unstructured":"Cescon C, Farina D, Gobbo M, Merletti R, Orizio C (2004) Effect of accelerometer location on mechanomyogram variables during voluntary, constant-force contractions in three human muscles. Med Biol Eng Compu 42(1):121\u2013127. https:\/\/doi.org\/10.1007\/BF02351021","journal-title":"Med Biol Eng Compu"},{"issue":"1","key":"3019_CR20","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1186\/1743-0003-10-75","volume":"10","author":"B Cesqui","year":"2013","unstructured":"Cesqui B, Tropea P, Micera S, Krebs H (2013) EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: A feasibility study. J Neuroeng Rehabil 10(1):75. https:\/\/doi.org\/10.1186\/1743-0003-10-75","journal-title":"J Neuroeng Rehabil"},{"key":"3019_CR21","doi-asserted-by":"publisher","unstructured":"Chengani R, Delva ML, Sakr M, Menon C (2016) Pilot study on strategies in sensor placement for robust hand\/wrist gesture classification based on movement related changes in forearm volume. 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT). https:\/\/doi.org\/10.1109\/hic.2016.7797693","DOI":"10.1109\/hic.2016.7797693"},{"key":"3019_CR22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.3389\/fbioe.2016.00018","volume":"4","author":"E Cho","year":"2016","unstructured":"Cho E, Chen R, Merhi L-K, Xiao Z, Pousett B, Menon C (2016) Force myography to control robotic upper extremity prostheses: A feasibility study. Front Bioeng Biotechnol 4:18. https:\/\/doi.org\/10.3389\/fbioe.2016.00018","journal-title":"Front Bioeng Biotechnol"},{"issue":"4","key":"3019_CR23","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1007\/s41315-019-00115-1","volume":"3","author":"S-G Cho","year":"2019","unstructured":"Cho S-G, Yoshikawa M, Ding M, Takamatsu J, Ogasawara T (2019) Machine-learning-based hand motion recognition system by measuring forearm deformation with a distance sensor array. Int J Intell Robot Appl 3(4):418\u2013429. https:\/\/doi.org\/10.1007\/s41315-019-00115-1","journal-title":"Int J Intell Robot Appl"},{"issue":"1","key":"3019_CR24","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/7333.918278","volume":"9","author":"DJ Curcie","year":"2001","unstructured":"Curcie DJ, Flint JA, Craelius W (2001) Biomimetic finger control by filtering of distributed forelimb pressures. IEEE Trans Neural Syst Rehabil Eng 9(1):69\u201375. https:\/\/doi.org\/10.1109\/7333.918278","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3019_CR25","doi-asserted-by":"publisher","unstructured":"Delva ML, Lajoie K, Khoshnam M, Menon C (2020) Wrist-worn wearables based on force myography: On the significance of user anthropometry. BioMed Eng OnLine 19(1). https:\/\/doi.org\/10.1186\/s12938-020-00789-w","DOI":"10.1186\/s12938-020-00789-w"},{"key":"3019_CR26","doi-asserted-by":"publisher","unstructured":"Delva ML, Sakr M, Chegani RS, Khoshnam M, Menon C (2018) Investigation into the potential to create a force myography-based smart-home controller for aging populations. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). https:\/\/doi.org\/10.1109\/biorob.2018.8488087","DOI":"10.1109\/biorob.2018.8488087"},{"key":"3019_CR27","unstructured":"Electrical Engineering Stack Exchange (2017) What is the best interface circuitry for using FSR sensors? [Online forum post]. Retrieved from https:\/\/electronics.stackexchange.com\/questions\/281759\/what-is-the-best-interface-circuitry-for-using-fsr-sensors"},{"issue":"9","key":"3019_CR28","doi-asserted-by":"publisher","first-page":"7167","DOI":"10.3390\/s90907167","volume":"9","author":"H Efendioglu","year":"2009","unstructured":"Efendioglu H, Yildirim T, Fidanboylu K (2009) Prediction of force measurements of a microbend sensor based on an artificial neural network. Sensors 9(9):7167\u20137176. https:\/\/doi.org\/10.3390\/s90907167","journal-title":"Sensors"},{"issue":"8","key":"3019_CR29","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.3390\/s18082553","volume":"18","author":"D Esposito","year":"2018","unstructured":"Esposito D, Andreozzi E, Fratini A, Gargiulo G, Savino S, Niola V, Bifulco P (2018) A piezoresistive sensor to measure muscle contraction and Mechanomyography. Sensors 18(8):2553. https:\/\/doi.org\/10.3390\/s18082553","journal-title":"Sensors"},{"key":"3019_CR30","doi-asserted-by":"publisher","unstructured":"Fajardo J, Neto AR, Silva W, Gomes M, Fujiwara E, Rohmer E (2019) A wearable robotic glove based on optical FMG Driven Controller. 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM).\u00a0https:\/\/doi.org\/10.1109\/icarm.2019.8834067","DOI":"10.1109\/icarm.2019.8834067"},{"issue":"1","key":"3019_CR31","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1109\/jsen.2017.2766663","volume":"18","author":"P Fang","year":"2018","unstructured":"Fang P, Ma X, Li X, Qiu X, Gerhard R, Zhang X, Li G (2018) Fabrication, structure characterization, and performance testing of Piezoelectret-film sensors for Recording body motion. IEEE Sens J 18(1):401\u2013412. https:\/\/doi.org\/10.1109\/jsen.2017.2766663","journal-title":"IEEE Sens J"},{"issue":"4","key":"3019_CR32","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1109\/tnsre.2014.2305111","volume":"22","author":"D Farina","year":"2014","unstructured":"Farina D, Jiang N, Rehbaum H, Holobar A, Graimann B, Dietl H, Aszmann OC (2014) The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng 22(4):797\u2013809. https:\/\/doi.org\/10.1109\/tnsre.2014.2305111","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"4","key":"3019_CR33","doi-asserted-by":"publisher","first-page":"692","DOI":"10.1016\/s1672-6529(16)60435-3","volume":"14","author":"D Ferigo","year":"2017","unstructured":"Ferigo D, Merhi L-K, Pousett B, Xiao ZG, Menon C (2017) A case study of a force-myography controlled bionic hand mitigating limb position effect. J Bionic Eng 14(4):692\u2013705. https:\/\/doi.org\/10.1016\/s1672-6529(16)60435-3","journal-title":"J Bionic Eng"},{"key":"3019_CR34","doi-asserted-by":"publisher","unstructured":"Fora M, Ben Atitallah B, Lweesy K, Kanoun O (2021) Hand gesture recognition based on force myography measurements using Knn Classifier. 2021 18th International Multi-Conference on Systems, Signals & Devices (SSD).\u00a0https:\/\/doi.org\/10.1109\/ssd52085.2021.9429514","DOI":"10.1109\/ssd52085.2021.9429514"},{"key":"3019_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2018\/8940373","volume":"2018","author":"E Fujiwara","year":"2018","unstructured":"Fujiwara E, Suzuki CK (2018) Optical fiber force myography sensor for identification of hand postures. J Sensors 2018:1\u201310. https:\/\/doi.org\/10.1155\/2018\/8940373","journal-title":"J Sensors"},{"key":"3019_CR36","doi-asserted-by":"publisher","unstructured":"Fujiwara E, Gomes MK, Wu YT, Suzuki CK (2021) \"Identification of dynamic hand gestures with force myography,\" The 32nd 2021 International Symposium on Micro-Nanomechatronics and Human Science, Nagoya, Japan, pp. 1\u20135.\u00a0https:\/\/doi.org\/10.1109\/MHS53471.2021.9767134","DOI":"10.1109\/MHS53471.2021.9767134"},{"key":"3019_CR37","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-319-56148-6_6","volume":"10208","author":"J Ghataurah","year":"2017","unstructured":"Ghataurah J, Ferigo D, Merhi L-K, Pousett B, Menon C (2017) A multi-sensor approach for biomimetic control of a Robotic Prosthetic Hand. Bioinform Biomed Eng 10208:74\u201384. https:\/\/doi.org\/10.1007\/978-3-319-56148-6_6","journal-title":"Bioinform Biomed Eng"},{"issue":"1","key":"3019_CR38","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/1743-0003-10-60","volume":"10","author":"OM Giggins","year":"2013","unstructured":"Giggins OM, Persson U, Caulfield B (2013) Biofeedback in rehabilitation. J Neuroeng Rehabil 10(1):60. https:\/\/doi.org\/10.1186\/1743-0003-10-60","journal-title":"J Neuroeng Rehabil"},{"key":"3019_CR39","doi-asserted-by":"publisher","unstructured":"Godiyal AK, Pandit S, Vimal AK, Singh U, Anand S, Joshi D (2017) Locomotion mode classification using force myography. 2017 IEEE Life Sciences Conference (LSC). https:\/\/doi.org\/10.1109\/lsc.2017.8268158","DOI":"10.1109\/lsc.2017.8268158"},{"issue":"6","key":"3019_CR40","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1109\/thms.2018.2860598","volume":"48","author":"AK Godiyal","year":"2018","unstructured":"Godiyal AK, Mondal M, Joshi SD, Joshi D (2018) Force myography based novel strategy for locomotion classification. IEEE Trans Human-Mach Syst 48(6):648\u2013657. https:\/\/doi.org\/10.1109\/thms.2018.2860598","journal-title":"IEEE Trans Human-Mach Syst"},{"key":"3019_CR41","doi-asserted-by":"publisher","unstructured":"Ha N, Withanachchi GP, Yihun Y (2018) Force myography signal-based hand gesture classification for the implementation of real-time control system to a prosthetic hand. 2018 Design of Medical Devices Conference.\u00a0https:\/\/doi.org\/10.1115\/dmd2018-6937","DOI":"10.1115\/dmd2018-6937"},{"issue":"1","key":"3019_CR42","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1007\/s42235-019-0009-4","volume":"16","author":"N Ha","year":"2019","unstructured":"Ha N, Withanachchi GP, Yihun Y (2019) Performance of forearm FMG for estimating hand gestures and prosthetic hand control. J Bionic Eng 16(1):88\u201398. https:\/\/doi.org\/10.1007\/s42235-019-0009-4","journal-title":"J Bionic Eng"},{"key":"3019_CR43","doi-asserted-by":"publisher","first-page":"36779","DOI":"10.3389\/fnbot.2022.836779","volume":"16","author":"O Heeb","year":"2022","unstructured":"Heeb O, Barua A, Menon C, Jiang X (2022) Building effective machine learning models for ankle joint power estimation during walking using FMG Sensors. Front Neurorobotics 16:36779. https:\/\/doi.org\/10.3389\/fnbot.2022.836779","journal-title":"Front Neurorobotics"},{"issue":"3","key":"3019_CR44","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.jelekin.2011.12.010","volume":"22","author":"XL Hu","year":"2012","unstructured":"Hu XL, Tong KY, Li R, Xue JJ, Ho SK, Chen P (2012) The effects of electromechanical wrist robot assistive system with neuromuscular electrical stimulation for stroke rehabilitation. J Electromyogr Kinesiol 22(3):431\u2013439. https:\/\/doi.org\/10.1016\/j.jelekin.2011.12.010","journal-title":"J Electromyogr Kinesiol"},{"issue":"3","key":"3019_CR45","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/tnsre.2013.2287383","volume":"22","author":"N Jiang","year":"2014","unstructured":"Jiang N, Vujaklija I, Rehbaum H, Graimann B, Farina D (2014) Is accurate mapping of EMG signals on kinematics needed for precise online myoelectric control? IEEE Trans Neural Syst Rehabil Eng 22(3):549\u2013558. https:\/\/doi.org\/10.1109\/tnsre.2013.2287383","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3019_CR46","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.medengphy.2017.01.015","volume":"41","author":"X Jiang","year":"2017","unstructured":"Jiang X, Merhi L-K, Xiao ZG, Menon C (2017) Exploration of force myography and surface electromyography in hand gesture classification. Med Eng Phys 41:63\u201373. https:\/\/doi.org\/10.1016\/j.medengphy.2017.01.015","journal-title":"Med Eng Phys"},{"key":"3019_CR47","doi-asserted-by":"publisher","unstructured":"Jiang X, Chu HT, Xiao ZG, Merhi L-K, Menon C (2016) Ankle positions classification using force myography: An exploratory investigation. 2016 IEEE Healthcare Innovation Point-Of-Care Technologies Conference (HI-POCT).\u00a0https:\/\/doi.org\/10.1109\/hic.2016.7797689","DOI":"10.1109\/hic.2016.7797689"},{"key":"3019_CR48","doi-asserted-by":"publisher","unstructured":"Jiang X, Tory L, Khoshnam M, Chu KHT, Menon C (2018) Exploration of gait parameters affecting the accuracy of force myography-based gait phase detection. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).\u00a0https:\/\/doi.org\/10.1109\/biorob.2018.8487790","DOI":"10.1109\/biorob.2018.8487790"},{"issue":"4","key":"3019_CR49","doi-asserted-by":"publisher","first-page":"1279","DOI":"10.3390\/s18041279","volume":"18","author":"X Jiang","year":"2018","unstructured":"Jiang X, Chu K, Khoshnam M, Menon C (2018) A wearable gait phase detection system based on force myography techniques. Sensors 18(4):1279. https:\/\/doi.org\/10.3390\/s18041279","journal-title":"Sensors"},{"issue":"4","key":"3019_CR50","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/s10055-018-0339-2","volume":"22","author":"X Jiang","year":"2018","unstructured":"Jiang X, Xiao ZG, Menon C (2018) Virtual grasps recognition using fusion of leap motion and force myography. Virtual Reality 22(4):297\u2013308. https:\/\/doi.org\/10.1007\/s10055-018-0339-2","journal-title":"Virtual Reality"},{"key":"3019_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.sna.2019.111738","volume":"301","author":"S Jiang","year":"2020","unstructured":"Jiang S, Gao Q, Liu H, Shull PB (2020) A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition. Sens Actuators, A 301:111738. https:\/\/doi.org\/10.1016\/j.sna.2019.111738","journal-title":"Sens Actuators, A"},{"key":"3019_CR52","doi-asserted-by":"publisher","unstructured":"Just F, Baur K, Riener R, Klamroth-Marganska V, Rauter G (2016) Online adaptive compensation of the Armin Rehabilitation Robot. 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).\u00a0https:\/\/doi.org\/10.1109\/biorob.2016.7523716","DOI":"10.1109\/biorob.2016.7523716"},{"issue":"4","key":"3019_CR53","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1007\/s40846-016-0151-y","volume":"36","author":"A Kadkhodayan","year":"2016","unstructured":"Kadkhodayan A, Jiang X, Menon C (2016) Continuous prediction of finger movements using force myography. J Med Biol Eng 36(4):594\u2013604. https:\/\/doi.org\/10.1007\/s40846-016-0151-y","journal-title":"J Med Biol Eng"},{"key":"3019_CR54","doi-asserted-by":"publisher","first-page":"853773","DOI":"10.3389\/fnbot.2022.853773","volume":"16","author":"A Ke","year":"2022","unstructured":"Ke A, Huang J, Wang J, He J (2022) Improving the robustness of human-machine interactive control for Myoelectric Prosthetic Hand during arm position changing. Front Neurorobotics 16:853773. https:\/\/doi.org\/10.3389\/fnbot.2022.853773","journal-title":"Front Neurorobotics"},{"key":"3019_CR55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91349-0","author":"A Kos","year":"2018","unstructured":"Kos A, Umek A (2018) Biomechanical biofeedback systems and applications. Human-Comput Interact Ser. https:\/\/doi.org\/10.1007\/978-3-319-91349-0","journal-title":"Human-Comput Interact Ser"},{"issue":"1","key":"3019_CR56","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1023\/a:1024494031121","volume":"15","author":"HI Krebs","year":"2003","unstructured":"Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N (2003) Auton Robot 15(1):7\u201320. https:\/\/doi.org\/10.1023\/a:1024494031121","journal-title":"Auton Robot"},{"key":"3019_CR57","unstructured":"Kohavi R et al (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc 14th Int Joint Conf Artif Intell 14:1137\u201345"},{"key":"3019_CR58","doi-asserted-by":"publisher","unstructured":"Koiva R, Riedenklau E, Viegas C, Castellini C (2015) Shape conformable high spatial resolution tactile bracelet for detecting hand and wrist activity. 2015 IEEE International Conference on Rehabilitation Robotics (ICORR). https:\/\/doi.org\/10.1109\/icorr.2015.7281192","DOI":"10.1109\/icorr.2015.7281192"},{"issue":"4","key":"3019_CR59","doi-asserted-by":"publisher","first-page":"3051","DOI":"10.1007\/s10586-017-0985-2","volume":"20","author":"Y Kuang","year":"2017","unstructured":"Kuang Y, Wu Q, Shao J, Wu J, Wu X (2017) Extreme learning machine classification method for lower limb Movement recognition. Clust Comput 20(4):3051\u20133059. https:\/\/doi.org\/10.1007\/s10586-017-0985-2","journal-title":"Clust Comput"},{"issue":"6","key":"3019_CR60","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1001\/jama.2009.116","volume":"301","author":"TA Kuiken","year":"2009","unstructured":"Kuiken TA (2009) Targeted muscle reinnervation for real-time myoelectric control of multifunction artificial arms. JAMA 301(6):619. https:\/\/doi.org\/10.1001\/jama.2009.116","journal-title":"JAMA"},{"key":"3019_CR61","doi-asserted-by":"publisher","unstructured":"Labb\u00e9 EE (2001) Biofeedback. Assessment and Therapy, 37\u201345. https:\/\/doi.org\/10.1016\/b978-012267806-6\/50038-x","DOI":"10.1016\/b978-012267806-6\/50038-x"},{"key":"3019_CR62","unstructured":"Ladha L, Deepa T (2011) Feature selection methods and algorithms. Int J Comput Sci Eng (IJCSE) 3(5). ISSN: 0975\u20133397"},{"issue":"1","key":"3019_CR63","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/s1672-6529(11)60095-4","volume":"9","author":"N Li","year":"2012","unstructured":"Li N, Yang D, Jiang L, Liu H, Cai H (2012) Combined use of FSR sensor array and SVM classifier for finger motion recognition based on pressure distribution map. J Bionic Eng 9(1):39\u201347. https:\/\/doi.org\/10.1016\/s1672-6529(11)60095-4","journal-title":"J Bionic Eng"},{"issue":"15","key":"3019_CR64","doi-asserted-by":"publisher","first-page":"6497","DOI":"10.1109\/jsen.2019.2910318","volume":"19","author":"M Li","year":"2019","unstructured":"Li M, Liang Z, He B, Zhao C-G, Yao W, Xu G, Xie J, Cui L (2019) Attention-controlled assistive wrist rehabilitation using a low-cost EEG sensor. IEEE Sens J 19(15):6497\u20136507. https:\/\/doi.org\/10.1109\/jsen.2019.2910318","journal-title":"IEEE Sens J"},{"key":"3019_CR65","doi-asserted-by":"publisher","unstructured":"Li X, Zhuo Q, Zhang X, Samuel OW, Xia Z, Zhang X, Fang P, Li G (2016) FMG-based body motion registration using piezoelectret sensors. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).\u00a0https:\/\/doi.org\/10.1109\/embc.2016.7591758","DOI":"10.1109\/embc.2016.7591758"},{"key":"3019_CR66","unstructured":"Lock B, Englehart K, Hudgins B (2005) Real-time myoelectric control in a virtual environment to relate usability vs. accuracy. In Myoelectric Controls\/Powered Prosthetics Symposium (pp. 17\u201320). Fredericton"},{"key":"3019_CR67","unstructured":"Megcircuitsprojects, & Instructables (2019). EMG Sensing Circuit. Instructables. Retrieved February 26, 2023, from https:\/\/www.instructables.com\/EMG-Sensing-Circuit\/"},{"key":"3019_CR68","doi-asserted-by":"publisher","unstructured":"Menon C, Delva ML (2018) FSR based force myography (FMG) stability throughout non-stationary upper extremity tasks. In Special Issue (Ed.), Proceedings of the International Conference on Intelligent Computing and Control Systems (ICICCS), September 20\u201321, 2018 (pp. 248\u2013253). IEEE. https:\/\/doi.org\/10.1109\/ICCONS.2018.8661939","DOI":"10.1109\/ICCONS.2018.8661939"},{"key":"3019_CR69","doi-asserted-by":"publisher","unstructured":"Merletti R, Farina D, editors (2016) \u201cSurface Electromyography.\u201d Physiology, Engineering, and Applications, Wiley-IEEE Press. Bowker, https:\/\/doi.org\/10.1002\/9781119082934","DOI":"10.1002\/9781119082934"},{"key":"3019_CR70","doi-asserted-by":"publisher","unstructured":"Montoya M, Henao O, Mu\u00f1oz J (2017) Muscle Fatigue Detection through wearable sensors. Proc XVIII IntConf Human Comput Interact.\u00a0https:\/\/doi.org\/10.1145\/3123818.3123855","DOI":"10.1145\/3123818.3123855"},{"key":"3019_CR71","doi-asserted-by":"publisher","unstructured":"Morais GD, Neves LC, Masiero AA, Castro MC (2016) Application of myo armband system to control a robot interface. Proc 9th Int Joint Conf Biomed Eng Syst Technol. https:\/\/doi.org\/10.5220\/0005706302270231","DOI":"10.5220\/0005706302270231"},{"key":"3019_CR72","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1016\/j.proeng.2012.07.297","volume":"41","author":"E Morganti","year":"2012","unstructured":"Morganti E, Angelini L, Adami A, Lalanne D, Lorenzelli L, Mugellini E (2012) A smart watch with embedded sensors to recognize objects, grasps and forearm gestures. Procedia Eng 41:1169\u20131175. https:\/\/doi.org\/10.1016\/j.proeng.2012.07.297","journal-title":"Procedia Eng"},{"issue":"8","key":"3019_CR73","doi-asserted-by":"publisher","first-page":"1304","DOI":"10.3390\/s16081304","volume":"16","author":"N Nazmi","year":"2016","unstructured":"Nazmi N, Abdul Rahman M, Yamamoto S-I, Ahmad S, Zamzuri H, Mazlan S (2016) A review of classification techniques of EMG signals during isotonic and isometric contractions. Sensors 16(8):1304. https:\/\/doi.org\/10.3390\/s16081304","journal-title":"Sensors"},{"key":"3019_CR74","doi-asserted-by":"publisher","unstructured":"Nowak M, Eiband T, Castellini C (2017) Multi-modal myocontrol: Testing combined force- and electromyography. 2017 International Conference on Rehabilitation Robotics (ICORR).\u00a0https:\/\/doi.org\/10.1109\/icorr.2017.8009438","DOI":"10.1109\/icorr.2017.8009438"},{"key":"3019_CR75","doi-asserted-by":"publisher","unstructured":"Ogris G, Kreil M, Lukowicz P (2007) Using FSR based muscule activity monitoring to recognize manipulative arm gestures. 2007 11th IEEE International Symposium on Wearable Computers. https:\/\/doi.org\/10.1109\/iswc.2007.4373776","DOI":"10.1109\/iswc.2007.4373776"},{"key":"3019_CR76","doi-asserted-by":"publisher","unstructured":"Ortiz-Catalan M, Rouhani F, Branemark R, Hakansson B (2015) Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).\u00a0https:\/\/doi.org\/10.1109\/embc.2015.7318567","DOI":"10.1109\/embc.2015.7318567"},{"issue":"1","key":"3019_CR77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1743-0003-10-22","volume":"10","author":"M Ortiz-Catalan","year":"2013","unstructured":"Ortiz-Catalan M, H\u00e5kansson B, Br\u00e5nemark R (2013) Real-time classification of simultaneous hand and wrist motions using Artificial Neural Networks with variable threshold outputs. J Neuroeng Rehabil 10(1):1\u201311","journal-title":"J Neuroeng Rehabil"},{"issue":"3","key":"3019_CR78","doi-asserted-by":"publisher","first-page":"1338","DOI":"10.1109\/tmech.2014.2340697","volume":"20","author":"AU Pehlivan","year":"2015","unstructured":"Pehlivan AU, Sergi F, OMalley MK (2015) A subject-adaptive controller for wrist robotic rehabilitation. IEEE\/ASME Trans Mechatronics 20(3):1338\u20131350. https:\/\/doi.org\/10.1109\/tmech.2014.2340697","journal-title":"IEEE\/ASME Trans Mechatronics"},{"issue":"8","key":"3019_CR79","doi-asserted-by":"publisher","first-page":"852","DOI":"10.3390\/e22080852","volume":"22","author":"P Qin","year":"2020","unstructured":"Qin P, Shi X (2020) Evaluation of feature extraction and classification for lower limb motion based on SEMG signal. Entropy 22(8):852. https:\/\/doi.org\/10.3390\/e22080852","journal-title":"Entropy"},{"issue":"4","key":"3019_CR80","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1682\/jrrd.2015.03.0041","volume":"53","author":"A Radmand","year":"2016","unstructured":"Radmand A, Scheme E, Englehart K (2016) High-density force myography: A possible alternative for upper-limb prosthetic control. J Rehabil Res Dev 53(4):443\u2013456. https:\/\/doi.org\/10.1682\/jrrd.2015.03.0041","journal-title":"J Rehabil Res Dev"},{"key":"3019_CR81","doi-asserted-by":"publisher","unstructured":"Rahman MH, Saad M, Kenne JP, Archambault PS (2010) Modeling and development of an exoskeleton robot for rehabilitation of wrist movements. 2010 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics.\u00a0https:\/\/doi.org\/10.1109\/aim.2010.5695839","DOI":"10.1109\/aim.2010.5695839"},{"key":"3019_CR82","doi-asserted-by":"publisher","unstructured":"Rasouli M, Ghosh R, Wang Wei Lee, Thakor NV, Kukreja S (2015) Stable force-myographic control of a prosthetic hand using incremental learning. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https:\/\/doi.org\/10.1109\/embc.2015.7319474","DOI":"10.1109\/embc.2015.7319474"},{"key":"3019_CR83","volume-title":"Fundamentals of Biostatistics","author":"B Rosner","year":"2015","unstructured":"Rosner B (2015) Fundamentals of Biostatistics. Brooks\/Cole, Cengage Learning, Boston, MA"},{"key":"3019_CR84","doi-asserted-by":"publisher","unstructured":"Sadarangani G, Menon C (2015) A wearable sensor system for rehabilitation applications. 2015 IEEE International Conference on Rehabilitation Robotics (ICORR). https:\/\/doi.org\/10.1109\/icorr.2015.7281278","DOI":"10.1109\/icorr.2015.7281278"},{"key":"3019_CR85","doi-asserted-by":"publisher","first-page":"42","DOI":"10.3389\/fbioe.2017.00042","volume":"5","author":"GP Sadarangani","year":"2017","unstructured":"Sadarangani GP, Jiang X, Simpson LA, Eng JJ, Menon C (2017) Force myography for monitoring grasping in individuals with stroke with mild to moderate upper-extremity impairments: A preliminary investigation in a controlled environment. Front Bioeng Biotechnol 5:42. https:\/\/doi.org\/10.3389\/fbioe.2017.00042","journal-title":"Front Bioeng Biotechnol"},{"issue":"1","key":"3019_CR86","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1186\/s12938-018-0593-2","volume":"17","author":"R SadeghiChegani","year":"2018","unstructured":"SadeghiChegani R, Menon C (2018) Regressing grasping using force myography: An exploratory study. BioMed Eng OnLine 17(1):159. https:\/\/doi.org\/10.1186\/s12938-018-0593-2","journal-title":"BioMed Eng OnLine"},{"key":"3019_CR87","doi-asserted-by":"publisher","unstructured":"Sakr M, Menon C (2016) On the estimation of isometric wrist\/forearm torque about three axes using force myography. 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). https:\/\/doi.org\/10.1109\/biorob.2016.7523730","DOI":"10.1109\/biorob.2016.7523730"},{"key":"3019_CR88","doi-asserted-by":"publisher","unstructured":"Sakr M, Menon C (2016) Regressing force-myographic signals collected by an armband to estimate torque exerted by the wrist: A preliminary investigation. 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). https:\/\/doi.org\/10.1109\/ccece.2016.7726852","DOI":"10.1109\/ccece.2016.7726852"},{"key":"3019_CR89","doi-asserted-by":"publisher","unstructured":"Sakr M, Menon C (2018) Exploratory evaluation of the force myography (FMG) signals usage for admittance control of a linear actuator. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). https:\/\/doi.org\/10.1109\/biorob.2018.8488028","DOI":"10.1109\/biorob.2018.8488028"},{"key":"3019_CR90","doi-asserted-by":"publisher","unstructured":"Sangha S, Elnady AM, Menon C (2016) A compact robotic orthosis for wrist assistance. 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).\u00a0https:\/\/doi.org\/10.1109\/biorob.2016.7523775","DOI":"10.1109\/biorob.2016.7523775"},{"key":"3019_CR91","doi-asserted-by":"publisher","first-page":"205566831770873","DOI":"10.1177\/2055668317708731","volume":"4","author":"J Sanford","year":"2017","unstructured":"Sanford J, Patterson R, Popa DO (2017) Concurrent surface electromyography and force myography classification during times of prosthetic socket shift and user fatigue. J Rehabil Assist Technol Eng 4:205566831770873. https:\/\/doi.org\/10.1177\/2055668317708731","journal-title":"J Rehabil Assist Technol Eng"},{"key":"3019_CR92","unstructured":"Sensitronics, FSR 101: Force sensing resistor theory and applications [PDF document]. Retrieved from https:\/\/www.sensitronics.com\/pdf\/Sensitronics_FSR_101.pdf"},{"key":"3019_CR93","first-page":"13","volume":"2008","author":"E Scheme","year":"2008","unstructured":"Scheme E, Englehart K (2008) A flexible user interface for rapid prototyping of advanced real-time myoelectric control schemes. MyoElectric Controls\/Powered Prosthetics Symp, Fredericton 2008:13\u201316","journal-title":"MyoElectric Controls\/Powered Prosthetics Symp, Fredericton"},{"issue":"5","key":"3019_CR94","doi-asserted-by":"publisher","first-page":"2716","DOI":"10.3390\/s23052716","volume":"23","author":"F Selimefendigil","year":"2023","unstructured":"Selimefendigil F, Rehman MU, Shah K, Haq IU, Iqbal S, Ismail MA (2023) Assessment of low-density force myography armband for classification of upper limb gestures. Sensors (Basel, Switzerland) 23(5):2716. https:\/\/doi.org\/10.3390\/s23052716","journal-title":"Sensors (Basel, Switzerland)"},{"issue":"7","key":"3019_CR95","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1109\/tnsre.2016.2636122","volume":"25","author":"M Semprini","year":"2017","unstructured":"Semprini M, Cuppone AV, Delis I, Squeri V, Panzeri S, Konczak J (2017) Biofeedback signals for robotic rehabilitation: Assessment of wrist muscle activation patterns in healthy humans. IEEE Trans Neural Syst Rehabil Eng 25(7):883\u2013892. https:\/\/doi.org\/10.1109\/tnsre.2016.2636122","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3019_CR96","doi-asserted-by":"publisher","unstructured":"Stefanou T, Chance G, Assaf T, Lenz A, Dogramadzi S (2018) Wearable tactile sensor brace for motion intent recognition in upper-limb rehabilitation. 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob). https:\/\/doi.org\/10.1109\/biorob.2018.8487721","DOI":"10.1109\/biorob.2018.8487721"},{"key":"3019_CR97","doi-asserted-by":"publisher","unstructured":"Supratak A, Wu C, Dong H, Sun K, Guo Y (2016) Survey on feature extraction and applications of biosignals. Lect Notes Comput Sci 161\u2013182. https:\/\/doi.org\/10.1007\/978-3-319-50478-0_8","DOI":"10.1007\/978-3-319-50478-0_8"},{"key":"3019_CR98","unstructured":"Tekscan. FlexiForce A201 datasheet [PDF document]. Retrieved January 26, 2023, from https:\/\/www.tekscan.com\/resources\/product\/flexiforce-a201-datasheet"},{"issue":"1","key":"3019_CR99","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s12984-019-0512-1","volume":"16","author":"E Trigili","year":"2019","unstructured":"Trigili E, Grazi L, Crea S, Accogli A, Carpaneto J, Micera S, Vitiello N, Panarese A (2019) Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks. J NeuroEng Rehabil 16(1):45. https:\/\/doi.org\/10.1186\/s12984-019-0512-1","journal-title":"J NeuroEng Rehabil"},{"key":"3019_CR100","doi-asserted-by":"publisher","unstructured":"Truong H, Zhang S, Muncuk U, Nguyen P, Bui N, Nguyen A, Lv Q, Chowdhury K, Dinh T, Vu T (2018) Capband. Proc 16th ACM Conf Embed Netw Sensor Syst.https:\/\/doi.org\/10.1145\/3274783.3274854","DOI":"10.1145\/3274783.3274854"},{"issue":"1","key":"3019_CR101","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/thms.2014.2362816","volume":"45","author":"B Ugurlu","year":"2015","unstructured":"Ugurlu B, Nishimura M, Hyodo K, Kawanishi M, Narikiyo T (2015) Proof of concept for robot-aided upper limb rehabilitation using disturbance observers. IEEE Trans Human-Mach Syst 45(1):110\u2013118. https:\/\/doi.org\/10.1109\/thms.2014.2362816","journal-title":"IEEE Trans Human-Mach Syst"},{"key":"3019_CR102","volume-title":"Umphred\u2019s Neurological Rehabilitation","author":"DA Umphred","year":"2013","unstructured":"Umphred DA, Lazaro RT, Roller ML, Burton GU (2013) Umphred\u2019s Neurological Rehabilitation, 6th edn. Elsevier Mosby, St. Louis","edition":"6"},{"issue":"4","key":"3019_CR103","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3109\/03091902.2016.1153739","volume":"40","author":"K Veer","year":"2016","unstructured":"Veer K, Sharma T (2016) Extraction and analysis of above elbow SEMG for pattern classification. J Med Eng Technol 40(4):149\u2013154. https:\/\/doi.org\/10.3109\/03091902.2016.1153739","journal-title":"J Med Eng Technol"},{"issue":"16","key":"3019_CR104","doi-asserted-by":"publisher","first-page":"5411","DOI":"10.3390\/s21165411","volume":"21","author":"MA V\u00e9lez-Guerrero","year":"2021","unstructured":"V\u00e9lez-Guerrero MA, Callejas-Cuervo M, Mazzoleni S (2021) Design, development, and testing of an intelligent wearable robotic exoskeleton prototype for Upper Limb Rehabilitation. Sensors 21(16):5411. https:\/\/doi.org\/10.3390\/s21165411","journal-title":"Sensors"},{"key":"3019_CR105","doi-asserted-by":"publisher","unstructured":"Wang X, Zhao J, Yang D, Li N, Sun C, Liu H (2010) Biomechatronic approach to a multi-fingered hand prosthesis. 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.\u00a0https:\/\/doi.org\/10.1109\/biorob.2010.5627734","DOI":"10.1109\/biorob.2010.5627734"},{"issue":"6","key":"3019_CR106","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1682\/jrrd.2007.11.0187","volume":"45","author":"M Wininger","year":"2008","unstructured":"Wininger M (2008) Pressure signature of forearm as predictor of Grip Force. The J Rehabil Res Dev 45(6):883\u2013892. https:\/\/doi.org\/10.1682\/jrrd.2007.11.0187","journal-title":"The J Rehabil Res Dev"},{"issue":"2","key":"3019_CR107","doi-asserted-by":"publisher","first-page":"1020","DOI":"10.1109\/jsen.2014.2359967","volume":"15","author":"JF Wu","year":"2015","unstructured":"Wu JF, Wang L, Li JQ (2015) Design and crosstalk error analysis of the circuit for the 2-D networked resistive sensor array. IEEE Sens J 15(2):1020\u20131026. https:\/\/doi.org\/10.1109\/jsen.2014.2359967","journal-title":"IEEE Sens J"},{"issue":"1","key":"3019_CR108","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1743-0003-11-2","volume":"11","author":"ZG Xiao","year":"2014","unstructured":"Xiao ZG, Menon C (2014) Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities. J Neuroeng Rehabil 11(1):2. https:\/\/doi.org\/10.1186\/1743-0003-11-2","journal-title":"J Neuroeng Rehabil"},{"issue":"1","key":"3019_CR109","doi-asserted-by":"publisher","first-page":"e5","DOI":"10.2196\/rehab.6901","volume":"4","author":"ZG Xiao","year":"2017","unstructured":"Xiao ZG, Menon C (2017) Counting grasping action using force myography: An exploratory study with healthy individuals. JMIR Rehabil Assist Technol 4(1):e5. https:\/\/doi.org\/10.2196\/rehab.6901","journal-title":"JMIR Rehabil Assist Technol"},{"issue":"2","key":"3019_CR110","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1016\/s1672-6529(16)60398-0","volume":"14","author":"ZG Xiao","year":"2017","unstructured":"Xiao ZG, Menon C (2017) Performance of forearm FMG and SEMG for estimating elbow, forearm and wrist positions. J Bionic Eng 14(2):284\u2013295. https:\/\/doi.org\/10.1016\/s1672-6529(16)60398-0","journal-title":"J Bionic Eng"},{"issue":"20","key":"3019_CR111","doi-asserted-by":"publisher","first-page":"4557","DOI":"10.3390\/s19204557","volume":"19","author":"ZG Xiao","year":"2019","unstructured":"Xiao ZG, Menon C (2019) A review of force myography research and development. Sensors 19(20):4557. https:\/\/doi.org\/10.3390\/s19204557","journal-title":"Sensors"},{"issue":"11","key":"3019_CR112","doi-asserted-by":"publisher","first-page":"2432","DOI":"10.3390\/s19112432","volume":"19","author":"ZG Xiao","year":"2019","unstructured":"Xiao ZG, Menon C (2019) An investigation on the sampling frequency of the upper-limb force myographic signals. Sensors 19(11):2432. https:\/\/doi.org\/10.3390\/s19112432","journal-title":"Sensors"},{"key":"3019_CR113","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.jbiomech.2018.11.035","volume":"83","author":"ZG Xiao","year":"2019","unstructured":"Xiao ZG, Menon C (2019) Does force myography recorded at the wrist correlate to resistance load levels during bicep curls? J Biomech 83:310\u2013314. https:\/\/doi.org\/10.1016\/j.jbiomech.2018.11.035","journal-title":"J Biomech"},{"issue":"1","key":"3019_CR114","doi-asserted-by":"publisher","first-page":"1795051","DOI":"10.1080\/23311916.2020.1795051","volume":"7","author":"ZG Xiao","year":"2020","unstructured":"Xiao ZG, Menon C (2020) Towards the investigation on the effect of the forearm rotation on the wrist FMG signal pattern using a high-density FMG sensing matrix. Cogent Eng 7(1):1795051. https:\/\/doi.org\/10.1080\/23311916.2020.1795051","journal-title":"Cogent Eng"},{"key":"3019_CR115","doi-asserted-by":"publisher","unstructured":"Xu X, Du Z, Zhang H, Zhang R, Hong Z, Huang Q, Han B (2022) Optimization of force myography sensor placement for arm movement recognition. 2022 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS).\u00a0https:\/\/doi.org\/10.1109\/iros47612.2022.9981236","DOI":"10.1109\/iros47612.2022.9981236"},{"key":"3019_CR116","doi-asserted-by":"publisher","unstructured":"Yaniger S I (1991) Force sensing resistors: A review of the technology. Electro Int 1991. https:\/\/doi.org\/10.1109\/electr.1991.718294","DOI":"10.1109\/electr.1991.718294"},{"key":"3019_CR117","doi-asserted-by":"publisher","unstructured":"Yap HK, Mao A, Goh JCH, Yeow C-H (2016) Design of a wearable FMG sensing system for user intent detection during hand rehabilitation with a soft robotic glove. 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob).\u00a0https:\/\/doi.org\/10.1109\/biorob.2016.7523722","DOI":"10.1109\/biorob.2016.7523722"},{"issue":"6","key":"3019_CR118","doi-asserted-by":"publisher","first-page":"464","DOI":"10.3109\/17483107.2011.650782","volume":"7","author":"D Yungher","year":"2012","unstructured":"Yungher D, Craelius W (2012) Improving fine motor function after brain injury using gesture recognition biofeedback. Disabil Rehabil Assist Technol 7(6):464\u2013468. https:\/\/doi.org\/10.3109\/17483107.2011.650782","journal-title":"Disabil Rehabil Assist Technol"},{"issue":"7","key":"3019_CR119","doi-asserted-by":"publisher","first-page":"2104","DOI":"10.3390\/s20072104","volume":"20","author":"U Zakia","year":"2020","unstructured":"Zakia U, Menon C (2020) Estimating exerted hand force via force myography to interact with a biaxial stage in real-time by learning human intentions: A preliminary investigation. Sensors 20(7):2104. https:\/\/doi.org\/10.3390\/s20072104","journal-title":"Sensors"},{"issue":"4","key":"3019_CR120","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1109\/thms.2021.3087902","volume":"51","author":"U Zakia","year":"2021","unstructured":"Zakia U, Menon C (2021) Toward long-term FMG model-based estimation of applied hand force in dynamic motion during human\u2013robot interactions. IEEE Trans Human-Mach Syst 51(4):310\u2013323. https:\/\/doi.org\/10.1109\/thms.2021.3087902","journal-title":"IEEE Trans Human-Mach Syst"},{"key":"3019_CR121","doi-asserted-by":"publisher","unstructured":"Zakia U, Jiang X, Menon C (2020) Deep learning technique in recognizing hand grasps using FMG signals. 2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). https:\/\/doi.org\/10.1109\/iemcon51383.2020.9284893","DOI":"10.1109\/iemcon51383.2020.9284893"},{"issue":"11","key":"3019_CR122","doi-asserted-by":"publisher","first-page":"154","DOI":"10.3390\/data7110154","volume":"7","author":"U Zakia","year":"2022","unstructured":"Zakia U, Menon C (2022) Dataset on force myography for Human-Robot Interactions. Data 7(11):154. https:\/\/doi.org\/10.3390\/data7110154","journal-title":"Data"},{"key":"3019_CR123","doi-asserted-by":"publisher","unstructured":"Zhang N, Li X, Samuel OW, Huang P-G, Fang P, Li G (2018) A pilot study on using forcemyography to record upper-limb movements for Human-Machine Interactive Control. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).\u00a0https:\/\/doi.org\/10.1109\/embc.2018.8513366","DOI":"10.1109\/embc.2018.8513366"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03019-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03019-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03019-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T02:19:14Z","timestamp":1713320354000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03019-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,2]]},"references-count":123,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["3019"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03019-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,2]]},"assertion":[{"value":"20 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and\/or company that could be construed as influencing the position presented in, or the review of, the current manuscript.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}