Due to the ever-increasing proportion of older people in the total population and the growing awareness of the importance of protecting workers against physical overload during long-time hard work, the idea of supporting exoskeletons progressed from high-tech fiction to almost commercialized products within the last six decades. Sensors, as part of the perception layer, play a crucial role in enhancing the functionality of exoskeletons by providing as accurate real-time data as possible to generate reliable input data for the control layer. The result of the processed sensor data is the information about current limb position, movement intension, and needed support. With the help of this review article, we want to clarify which criteria for sensors used in exoskeletons are important and how standard sensor types, such as kinematic and kinetic sensors, are used in lower limb exoskeletons. We also want to outline the possibilities and limitations of special medical signal sensors detecting, e.g., brain or muscle signals to improve data perception at the human-machine interface. A topic-based literature and product research was done to gain the best possible overview of the newest developments, research results, and products in the field. The paper provides an extensive overview of sensor criteria that need to be considered for the use of sensors in exoskeletons, as well as a collection of sensors and their placement used in current exoskeleton products. Additionally, the article points out several types of sensors detecting physiological or environmental signals that might be beneficial for future exoskeleton developments.

Faculty of Biomedical Engineering Czech Technical University Prague 272 01 Kladno Czech Republic

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Viteckova S., Kutilek P., Jirina M. Wearable lower limb robotics: A review. Biocybern. Biomed. Eng. 2013;33:96–105. doi: 10.1016/j.bbe.2013.03.005. DOI

Viteckova S., Kutilek P., de Boisboissel G., Krupicka R., Galajdova A., Kauler J., Lhotska L., Szabo Z. Empowering lower limbs exoskeletons: State-of-the-art. Robotica. 2018;36:1743–1756. doi: 10.1017/S0263574718000693. DOI

Lee H.-D., Han C.-S. Technical Trend of the Lower Limb Exoskeleton System for the Performance Enhancement. J. Inst. Control Robot. Syst. 2014;20:364–371. doi: 10.5302/J.ICROS.2014.14.9023. DOI

Shao S., Wu J., Zhou Q. Developments and challenges in human performance enhancement technology. Med. Nov. Technol. Devices. 2021;12:100095. doi: 10.1016/j.medntd.2021.100095. DOI

Yan T., Cempini M., Oddo C.M., Vitiello N. Review of assistive strategies in powered lower-limb orthoses and exoskeletons. Robot. Auton. Syst. 2015;64:120–136. doi: 10.1016/j.robot.2014.09.032. DOI

Veale A.J., Xie S.Q. Towards compliant and wearable robotic orthoses: A review of current and emerging actuator technologies. Med. Eng. Phys. 2016;38:317–325. doi: 10.1016/j.medengphy.2016.01.010. PubMed DOI

Vidal A.P., Morales J.R., Torres G.O., Vázquez F.S., Rojas A.C., Mendoza J.B., Cerda J.R. Soft Exoskeletons: Development, Requirements, and Challenges of the Last Decade. Actuators. 2021;10:166. doi: 10.3390/act10070166. DOI

Del Sanchez-Villamañan M.C., Gonzalez-Vargas J., Torricelli D., Moreno J.C., Pons J.L. Compliant lower limb exoskeletons: A comprehensive review on mechanical design principles. J. Neuroeng. Rehabil. 2019;16:55. doi: 10.1186/s12984-019-0517-9. PubMed DOI PMC

Perez-Sala X., Escalera S., Angulo C., Gonzàlez J. A Survey on Model Based Approaches for 2D and 3D Visual Human Pose Recovery. Sensors. 2014;14:4189–4210. doi: 10.3390/s140304189. PubMed DOI PMC

Roriz P., Carvalho L., Frazão O., Santos J.L., Simões J.A. From conventional sensors to fibre optic sensors for strain and force measurements in biomechanics applications: A review. J. Biomech. 2014;47:1251–1261. doi: 10.1016/j.jbiomech.2014.01.054. PubMed DOI

Wong C., Zhang Z.-Q., Lo B.P.L., Yang G.-Z. Wearable Sensing for Solid Biomechanics. IEEE Sensors J. 2015;15:2747–2760. doi: 10.1109/JSEN.2015.2393883. DOI

Wong W.Y., Wong M., Lo K.H. Clinical applications of sensors for human posture and movement analysis: A review. Prosthetics Orthot. Int. 2007;31:62–75. doi: 10.1080/03093640600983949. PubMed DOI

Mukhopadhyay S.C. Wearable Sensors for Human Activity Monitoring: A Review. IEEE Sensors J. 2015;15:1321–1330. doi: 10.1109/JSEN.2014.2370945. DOI

Bonato P. Advances in wearable technology and applications in physical medicine and rehabilitation. J. Neuroeng. Rehabil. 2005;2:2. doi: 10.1186/1743-0003-2-2. PubMed DOI PMC

Bonato P. Advances in Wearable Technology for Rehabilitation. Volume 145. IOS Press; Amsterdam, The Netherlands: 2009. pp. 145–159. PubMed DOI

Patel S., Park H., Bonato P., Chan L., Rodgers M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 2012;9:21. doi: 10.1186/1743-0003-9-21. PubMed DOI PMC

Ahmad N., Ghazilla R.A.R., Khairi N.M., Kasi V. Reviews on Various Inertial Measurement Unit (IMU) Sensor Applications. Int. J. Signal Process. Syst. 2013;1:256–262. doi: 10.12720/ijsps.1.2.256-262. DOI

Rukina N., Kuznetsov A., Borzikov V., Komkova O., Belova A. Surface Electromyography: Its Role and Potential in the Development of Exoskeleton (Review) Sovrem. Tehnol. Med. 2016;8:109–118. doi: 10.17691/stm2016.8.2.15. DOI

Yang C.-J., Zhang J.-F., Chen Y., Dong Y.-M., Zhang Y. A Review of exoskeleton-type systems and their key technologies. Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci. 2008;222:1599–1612. doi: 10.1243/09544062JMES936. DOI

Lee H., Kim W., Han J., Han C. The technical trend of the exoskeleton robot system for human power assistance. Int. J. Precis. Eng. Manuf. 2012;13:1491–1497. doi: 10.1007/s12541-012-0197-x. DOI

Hong Y.W., King Y.-J., Yeo W.-H., Ting C.-H., Chuah Y.-D., Lee J.-V. Lower Extremity Exoskeleton: Review and Challenges Surrounding the Technology and its Role in Rehabilitation of Lower Limbs. Aust. J. Basic Appl. Sci. 2013;7:520–524. doi: 10.22587/ajbas. DOI

Zhou J., Yang S., Xue Q. Lower limb rehabilitation exoskeleton robot: A review. Adv. Mech. Eng. 2021;13:168781402110118. doi: 10.1177/16878140211011862. DOI

de la Tejera J., Bustamante-Bello R., Ramirez-Mendoza R., Izquierdo-Reyes J. Systematic Review of Exoskeletons towards a General Categorization Model Proposal. Appl. Sci. 2021;11:76. doi: 10.3390/app11010076. DOI

Novak D., Riener R. A survey of sensor fusion methods in wearable robotics. Robot. Auton. Syst. 2015;73:155–170. doi: 10.1016/j.robot.2014.08.012. DOI

Li W.-Z., Cao G.-Z., Zhu A.-B. Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons. IEEE Access. 2021;9:123040–123060. doi: 10.1109/ACCESS.2021.3110595. DOI

Tucker M.R., Olivier L., Pagel A., Bleuler H., Bouri M., Lambercy O., Millán J.D.R., Riener R., Vallery H., Gassert R. Control strategies for active lower extremity prosthetics and orthotics: A review. J. Neuroeng. Rehabil. 2015;12:1. doi: 10.1186/1743-0003-12-1. PubMed DOI PMC

Young A.J., Ferris D.P. State of the Art and Future Directions for Lower Limb Robotic Exoskeletons. IEEE Trans. Neural Syst. Rehabil. Eng. 2017;25:171–182. doi: 10.1109/TNSRE.2016.2521160. PubMed DOI

Wang T., Zhang B., Liu C., Liu T., Han Y., Wang S., Ferreira J.P., Dong W., Zhang X. A Review on the Rehabilitation Exoskeletons for the Lower Limbs of the Elderly and the Disabled. Electronics. 2022;11:388. doi: 10.3390/electronics11030388. DOI

Shi D., Zhang W., Zhang W., Ding X. A Review on Lower Limb Rehabilitation Exoskeleton Robots. Chin. J. Mech. Eng. 2019;32:1–11. doi: 10.1186/s10033-019-0389-8. DOI

Baud R., Manzoori A.R., Ijspeert A., Bouri M. Review of control strategies for lower-limb exoskeletons to assist gait. J. Neuroeng. Rehabil. 2021;18:119. doi: 10.1186/s12984-021-00906-3. PubMed DOI PMC

Hussain F., Goecke R., Mohammadian M. Exoskeleton robots for lower limb assistance: A review of materials, actuation, and manufacturing methods. Proc. Inst. Mech. Eng. Part H J. Eng. Med. 2021;235:1375–1385. doi: 10.1177/09544119211032010. PubMed DOI

Redkar S., Chinmilli S.R.P.T., Zhang T.S.W. A Review on Wearable Inertial Tracking based Human Gait Analysis and Control Strategies of Lower-Limb Exoskeletons. Int. Robot. Autom. J. 2017;3:1–19. doi: 10.15406/iratj.2017.03.00080. DOI

Batavia A.I., Hammer G.S. Toward the development of consumer-based criteria for the evaluation of assistive devices. J. Rehabil. Res. Dev. 1990;27:425–436. doi: 10.1682/JRRD.1990.10.0425. PubMed DOI

Scherer M.J., Lane J.P. Assessing consumer profiles of ‘ideal’ assistive technologies in ten categories: An integration of quantitative and qualitative methods. Disabil. Rehabil. 1997;19:528–535. doi: 10.3109/09638289709166046. PubMed DOI

Chen G., Qi P., Guo Z., Yu H. Gait-Event-Based Synchronization Method for Gait Rehabilitation Robots via a Bioinspired Adaptive Oscillator. IEEE Trans. Biomed. Eng. 2017;64:1345–1356. doi: 10.1109/TBME.2016.2604340. PubMed DOI

Lebel K., Boissy P., Hamel M., Duval C. Inertial Measures of Motion for Clinical Biomechanics: Comparative Assessment of Accuracy under Controlled Conditions—Effect of Velocity. PLoS ONE. 2013;8:e79945. doi: 10.1371/journal.pone.0079945. PubMed DOI PMC

Kumar N., Kumar D.P.N., Pankaj D., Mahajan A., Kumar A., Sohi B.S. Evaluation of normal Gait using electro-goniometer. J. Sci. Ind. Res. 2009;68:696–698.

Nogueira S.L., Siqueira A.A.G., Inoue R.S., Terra M.H. Markov Jump Linear Systems-Based Position Estimation for Lower Limb Exoskeletons. Sensors. 2014;14:1835–1849. doi: 10.3390/s140101835. PubMed DOI PMC

Dollar A.M., Herr H. Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art. IEEE Trans. Robot. 2008;24:144–158. doi: 10.1109/TRO.2008.915453. DOI

Maggioni S., Melendez-Calderon A., van Asseldonk E., Klamroth-Marganska V., Lünenburger L., Riener R., van der Kooij H. Robot-aided assessment of lower extremity functions: A review. J. Neuroeng. Rehabil. 2016;13:72. doi: 10.1186/s12984-016-0180-3. PubMed DOI PMC

Luinge H., Veltink P., Baten C. Estimating orientation with gyroscopes and accelerometers. Technol. Heal. Care. 1999;7:455–459. doi: 10.3233/THC-1999-7612. PubMed DOI

Willemsen A., van Alsté J., Boom H. Real-time gait assessment utilizing a new way of accelerometry. J. Biomech. 1990;23:859–863. doi: 10.1016/0021-9290(90)90033-Y. PubMed DOI

Shaw G.A., Siegel A.M., Zogbi G., Opar T.P. Warfighter Physiological and Environmental Monitoring: A Study for the U.S. Army Research Institute in Environmental Medicine and the Soldier Systems Center. Massachusetts Inst Of Tech Lexington Lincoln Lab; Lexington, MA, USA: 2004. DOI

Ma W., Zhang X., Yin G. Design on intelligent perception system for lower limb rehabilitation exoskeleton robot; Proceedings of the 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI); Xian, China. 19–22 August 2016; pp. 587–592. DOI

Del-Ama A.J., Moreno J.C., Gil-Agudo A., de-los-Reyes A., Pons J.L. Online Assessment of Human-Robot Interaction for Hybrid Control of Walking. Sensors. 2012;12:215–225. doi: 10.3390/s120100215. PubMed DOI PMC

Ahmed A.I., Cheng H., Lin X., Omer M., Juma M.A. Variable Admittance Control for Climbing Stairs in Human-Powered Exoskeleton Systems. Adv. Robot. Autom. 2016;5:1–7. doi: 10.4172/2168-9695.1000157. DOI

Hassan M., Kadone H., Suzuki K., Sankai Y. Wearable Gait Measurement System with an Instrumented Cane for Exoskeleton Control. Sensors. 2014;14:1705–1722. doi: 10.3390/s140101705. PubMed DOI PMC

De Rossi S.M.M., Vitiello N., Lenzi T., Ronsse R., Koopman B., Persichetti A., Vecchi F., Ijspeert A.J., Van Der Kooij H., Carrozza M.C. Sensing Pressure Distribution on a Lower-Limb Exoskeleton Physical Human-Machine Interface. Sensors. 2011;11:207–227. doi: 10.3390/s110100207. PubMed DOI PMC

Aminian K., Najafi B., Büla C., Leyvraz P.-F., Robert P. Spatio-temporal parameters of gait measured by an ambulatory system using miniature gyroscopes. J. Biomech. 2002;35:689–699. doi: 10.1016/S0021-9290(02)00008-8. PubMed DOI

Mansfield A., Lyons G.M. The use of accelerometry to detect heel contact events for use as a sensor in FES assisted walking. Med Eng. Phys. 2003;25:879–885. doi: 10.1016/S1350-4533(03)00116-4. PubMed DOI

Fleischer C., Hommel G. A Human--Exoskeleton Interface Utilizing Electromyography. IEEE Trans. Robot. 2008;24:872–882. doi: 10.1109/TRO.2008.926860. DOI

Gordon K.E., Ferris D. Learning to walk with a robotic ankle exoskeleton. J. Biomech. 2007;40:2636–2644. doi: 10.1016/j.jbiomech.2006.12.006. PubMed DOI

Aguilar-Sierra H., Yu W., Salazar S., Lopez-Gutierrez J.R. Design and control of hybrid actuation lower limb exoskeleton. Adv. Mech. Eng. 2015;7:168781401559098. doi: 10.1177/1687814015590988. DOI

Gordon K.E., Kinnaird C.R., Ferris D.P. Locomotor adaptation to a soleus EMG-controlled antagonistic exoskeleton. J. Neurophysiol. 2013;109:1804–1814. doi: 10.1152/jn.01128.2011. PubMed DOI PMC

Galle S., Malcolm P., Collins S.H., De Clercq D. Reducing the metabolic cost of walking with an ankle exoskeleton: Interaction between actuation timing and power. J. Neuroeng. Rehabil. 2017;14:35. doi: 10.1186/s12984-017-0235-0. PubMed DOI PMC

Kim K., Yu C.-H., Jeong G.-Y., Heo M., Kwon T.-K. Analysis of the assistance characteristics for the knee extension motion of knee orthosis using muscular stiffness force feedback. J. Mech. Sci. Technol. 2013;27:3161–3169. doi: 10.1007/s12206-013-0837-9. DOI

Beck T.W., Housh T.J., Cramer J.T., Weir J.P., Johnson G.O., Coburn J.W., Malek M.H., Mielke M. Mechanomyographic amplitude and frequency responses during dynamic muscle actions: A comprehensive review. Biomed. Eng. Online. 2005;4:67. doi: 10.1186/1475-925X-4-67. PubMed DOI PMC

Islam A., Sundaraj K., Ahmad R.B., Ahamed N.U. Mechanomyogram for Muscle Function Assessment: A Review. PLoS ONE. 2013;8:e58902. doi: 10.1371/journal.pone.0058902. PubMed DOI PMC

Meagher C., Franco E., Turk R., Wilson S., Steadman N., McNicholas L., Vaidyanathan R., Burridge J., Stokes M. New advances in mechanomyography sensor technology and signal processing: Validity and intrarater reliability of recordings from muscle. J. Rehabil. Assist. Technol. Eng. 2020;7:2055668320916116. doi: 10.1177/2055668320916116. PubMed DOI PMC

Cram J.R., Kasman G.S., Holtz J. Introduction to Surface Electromyography. Aspen Publishers; Gaithersburg, MD, USA: 1998.

Orizio C. Muscle sound: Bases for the introduction of a mechanomyographic signal in muscle studies. Crit. Rev. Biomed. Eng. 1993;21:201–243. PubMed

Smith T., Stokes M. Technical aspects of acoustic myography (AMG) of human skeletal muscle: Contact pressure and force/AMG relationships. J. Neurosci. Methods. 1993;47:85–92. doi: 10.1016/0165-0270(93)90024-L. PubMed DOI

Dobrunz L., Pelletier D., McMahon T. Muscle stiffness measured under conditions simulating natural sound production. Biophys. J. 1990;58:557–565. doi: 10.1016/S0006-3495(90)82399-7. PubMed DOI PMC

Alves N., Sejdić E., Sahota B., Chau T. The effect of accelerometer location on the classification of single-site forearm mechanomyograms. Biomed. Eng. Online. 2010;9:23. doi: 10.1186/1475-925X-9-23. PubMed DOI PMC

Zuniga J.M., Housh T.J., Camic C.L., Hendrix C.R., Mielke M., Schmidt R.J., Johnson G.O. The effects of accelerometer placement on mechanomyographic amplitude and mean power frequency during cycle ergometry. J. Electromyogr. Kinesiol. 2010;20:719–725. doi: 10.1016/j.jelekin.2010.01.001. PubMed DOI

Deffieux T., Gennisson J.-L., Tanter M., Fink M., Nordez A. Ultrafast imaging of in vivo muscle contraction using ultrasound. Appl. Phys. Lett. 2006;89:184107. doi: 10.1063/1.2378616. DOI

Vanello N., Hartwig V., Tesconi M., Ricciardi E., Tognetti A., Zupone G., Gassert R., Chapuis D., Sgambelluri N., Scilingo E.P., et al. Sensing Glove for Brain Studies: Design and Assessment of Its Compatibility for fMRI With a Robust Test. IEEE/ASME Trans. Mechatronics. 2008;13:345–354. doi: 10.1109/TMECH.2008.924115. DOI

Lukowicz P., Ward J.A., Junker H., Stäger M., Tröster G., Atrash A., Starner T. Recognizing Workshop Activity Using Body Worn Microphones and Accelerometers. In: Ferscha A., Mattern F., editors. Proceedings of the Pervasive Computing: Second International Conference, PERVASIVE 2004, Linz/Vienna, Austria, 18–23 April 2004. Springer; Berlin, Germay: 2004. pp. 18–32.

Moromugi S., Koujina Y., Ariki S., Okamoto A., Tanaka T., Feng M.Q., Ishimatsu T. Muscle stiffness sensor to control an assistance device for the disabled. Artif. Life Robot. 2004;8:42–45. doi: 10.1007/s10015-004-0286-8. DOI

Murayama M., Nosaka K., Yoneda T., Minamitani K. Changes in hardness of the human elbow flexor muscles after eccentric exercise. Eur. J. Appl. Physiol. 2000;82:361–367. doi: 10.1007/s004210000242. PubMed DOI

Han H., Kim J. Active muscle stiffness sensor based on piezoelectric resonance for muscle contraction estimation. Sensors Actuators A Phys. 2013;194:212–219. doi: 10.1016/j.sna.2013.01.054. DOI

Szumilas M., Władziński M., Wildner K. A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces. Sensors. 2021;21:8380. doi: 10.3390/s21248380. PubMed DOI PMC

Silva J., Chau T. Coupled microphone-accelerometer sensor pair for dynamic noise reduction in MMG signal recording. Electron. Lett. 2003;39:1496–1498. doi: 10.1049/el:20031003. DOI

Wołczowski A., Błędowski M., Witkowski J. The System for EMG and MMG Singals Recording for the Bioprosthetic Hand Control. J. Autom. Mob. Robot. Intell. Syst. 2017;11:22–29. doi: 10.14313/JAMRIS_3-2017/25. DOI

Gregori B., Galié E., Accornero N. Surface electromyography and mechanomyography recording: A new differential composite probe. Med Biol. Eng. Comput. 2003;41:665–669. doi: 10.1007/BF02349974. PubMed DOI

Fukuhara S., Oka H. A Simplified Analysis of Real-time Monitoring of Muscle Contraction during Dynamic Exercise Using an MMG/EMG Hybrid Transducer System. Adv. Biomed. Eng. 2019;8:185–192. doi: 10.14326/abe.8.185. DOI

Zhang X., Li X., Samuel O.W., Huang Z., Fang P., Li G. Improving the Robustness of Electromyogram-Pattern Recognition for Prosthetic Control by a Postprocessing Strategy. Front. Neurorobotics. 2017;11:51. doi: 10.3389/fnbot.2017.00051. PubMed DOI PMC

Kaas A., Goebel R., Valente G., Sorger B. Topographic Somatosensory Imagery for Real-Time fMRI Brain-Computer Interfacing. Front. Hum. Neurosci. 2019;13:427. doi: 10.3389/fnhum.2019.00427. PubMed DOI PMC

Matthews F., Pearlmutter B.A., Wards T.E., Soraghan C., Markham C. Hemodynamics for Brain-Computer Interfaces. IEEE Signal Process. Mag. 2008;25:87–94. doi: 10.1109/MSP.2008.4408445. DOI

Fukuma R., Yanagisawa T., Saitoh Y., Hosomi K., Kishima H., Shimizu T., Sugata H., Yokoi H., Hirata M., Kamitani Y., et al. Real-Time Control of a Neuroprosthetic Hand by Magnetoencephalographic Signals from Paralysed Patients. Sci. Rep. 2016;6:21781. doi: 10.1038/srep21781. PubMed DOI PMC

Faress A., Chau T. Towards a multimodal brain–computer interface: Combining fNIRS and fTCD measurements to enable higher classification accuracy. NeuroImage. 2013;77:186–194. doi: 10.1016/j.neuroimage.2013.03.028. PubMed DOI

Lu J., Mamun K.A., Chau T. Pattern classification to optimize the performance of Transcranial Doppler Ultrasonography-based brain machine interface. Pattern Recognit. Lett. 2015;66:135–143. doi: 10.1016/j.patrec.2015.07.020. DOI

Khalaf A., Sejdic E., Akcakaya M. A novel motor imagery hybrid brain computer interface using EEG and functional transcranial Doppler ultrasound. J. Neurosci. Methods. 2019;313:44–53. doi: 10.1016/j.jneumeth.2018.11.017. PubMed DOI

Lystad R.P., Pollard H. Functional neuroimaging: A brief overview and feasibility for use in chiropractic research. J. Can. Chiropr. Assoc. 2009;53:59–72. PubMed PMC

Min B.-K., Marzelli M.J., Yoo S.-S. Neuroimaging-based approaches in the brain–computer interface. Trends Biotechnol. 2010;28:552–560. doi: 10.1016/j.tibtech.2010.08.002. PubMed DOI

He B., Yang L., Wilke C., Yuan H. Electrophysiological Imaging of Brain Activity and Connectivity—Challenges and Opportunities. IEEE Trans. Biomed. Eng. 2011;58:1918–1931. doi: 10.1109/tbme.2011.2139210. PubMed DOI PMC

Nicolas-Alonso L.F., Gomez-Gil J. Brain Computer Interfaces, a Review. Sensors. 2012;12:1211–1279. doi: 10.3390/s120201211. PubMed DOI PMC

Sitaram R., Weiskopf N., Caria A., Veit R., Erb M., Birbaumer N. fMRI Brain-Computer Interfaces. IEEE Signal Process. Mag. 2008;25:95–106. doi: 10.1109/MSP.2008.4408446. DOI

Ge S., Yang Q., Wang R., Lin P., Gao J., Leng Y., Yang Y., Wang H. A Brain-Computer Interface Based on a Few-Channel EEG-fNIRS Bimodal System. IEEE Access. 2017;5:208–218. doi: 10.1109/ACCESS.2016.2637409. DOI

Hämäläinen M.S. Magnetoencephalography: A tool for functional brain imaging. Brain Topogr. 1992;5:95–102. doi: 10.1007/BF01129036. PubMed DOI

Kauhanen L., Nykopp T., Lehtonen J., Jylanki P., Heikkonen J., Rantanen P., Alaranta H., Sams M. EEG and MEG brain-computer interface for tetraplegic patients. IEEE Trans. Neural Syst. Rehabil. Eng. 2006;14:190–193. doi: 10.1109/TNSRE.2006.875546. PubMed DOI

Rashid M., Sulaiman N., Majeed A.P.P.A., Musa R.M., Nasir A.F.A., Bari B.S., Khatun S. Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review. Front. Neurorobotics. 2020;14:25. doi: 10.3389/fnbot.2020.00025. PubMed DOI PMC

Saha S., Mamun K.A., Ahmed K., Mostafa R., Naik G.R., Darvishi S., Khandoker A.H., Baumert M. Progress in Brain Computer Interface: Challenges and Opportunities. Front. Syst. Neurosci. 2021;15:578875. doi: 10.3389/fnsys.2021.578875. PubMed DOI PMC

Lotte F., Bougrain L., Clerc M. Wiley Encyclopedia of Electrical and Electronics Engineering. Wiley Online Library; Hoboken, NJ, USA: 2015. Electroencephalography (EEG)-Based Brain-Computer Interfaces; pp. 1–20. DOI

Wyckoff S.N., Sherlin L.H., Ford N.L., Dalke D. Validation of a wireless dry electrode system for electroencephalography. J. Neuroeng. Rehabil. 2015;12:95. doi: 10.1186/s12984-015-0089-2. PubMed DOI PMC

Zander T.O., Lehne M., Ihme K., Jatzev S., Correia J., Kothe C., Picht B., Nijboer F. A Dry EEG-System for Scientific Research and Brain–Computer Interfaces. Front. Behav. Neurosci. 2011;5:53. doi: 10.3389/fnins.2011.00053. PubMed DOI PMC

Kutilek P., Volf P., Viteckova S., Smrcka P., Krivanek V., Lhotska L., Hana K., Doskocil R., Navratil L., Hon Z., et al. Proceedings of the Wearable systems for monitoring the health condition of soldiers: Review and application; Brno, Czech Republic. 31 May–2 June 2017; pp. 748–752. DOI

Asselin P., Knezevic S., Kornfeld S., Cirnigliaro C., Agranova-Breyter I., Bauman W.A., Spungen A.M. Heart rate and oxygen demand of powered exoskeleton-assisted walking in persons with paraplegia. J. Rehabil. Res. Dev. 2015;52:147–158. doi: 10.1682/JRRD.2014.02.0060. PubMed DOI

Onose G., Cârdei V., Crãciunoiu Ş.T., Avramescu V. Concept, Sumptions and Functional-Construction Aspects. Editura ICTCM; Bucuresti, Romania: 2012. Mechatronic Orthotic Device. onal-Construction Aspects.

Onose G., Cârdei V., Crăciunoiu T., Avramescu V., Opriş I., Lebedev M.A., Constantinescu M.V. Mechatronic Wearable Exoskeletons for Bionic Bipedal Standing and Walking: A New Synthetic Approach. Front. Neurosci. 2016;10:343. doi: 10.3389/fnins.2016.00343. PubMed DOI PMC

Díaz I., Gil J.J., Sánchez E. Lower-Limb Robotic Rehabilitation: Literature Review and Challenges. J. Robot. 2011;2011:1–11. doi: 10.1155/2011/759764. DOI

Rupal B.S., Rafique S., Singla A., Singla E., Isaksson M., Virk G.S. Lower-limb exoskeletons. Int. J. Adv. Robot. Syst. 2017;14:172988141774355. doi: 10.1177/1729881417743554. DOI

Zoss A., Kazerooni H., Chu A. On the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX); Proceedings of the 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems; Edmonton, AB, Canada. 2–6 August 2005; pp. 3465–3472. DOI

Kwa H.K., Noorden J.H., Missel M., Craig T., Pratt J.E., Neuhaus P.D. Development of the IHMC Mobility Assist Exoskeleton; Proceedings of the Robotics and Automation, 2009. ICRA ’09. IEEE International Conference on Robotics and Automation; Kobe, Japan. 12–17 May 2009; pp. 2556–2562. DOI

Fontana M., Vertechy R., Marcheschi S., Salsedo F., Bergamasco M. The Body Extender: A Full-Body Exoskeleton for the Transport and Handling of Heavy Loads. IEEE Robot. Autom. Mag. 2014;21:34–44. doi: 10.1109/MRA.2014.2360287. DOI

Lim D., Kim W., Lee H., Kim H., Shin K., Park T., Lee J., Han C. Development of a lower extremity Exoskeleton Robot with a quasi-anthropomorphic design approach for load carriage; Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); Hamburg, Germany. 28 September–3 October 2015; pp. 5345–5350. DOI

Walsh C., Paluska D., Pasch K., Grand W., Valiente A., Herr H. Development of a lightweight, underactuated exoskeleton for load-carrying augmentation; Proceedings of the Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006; Orlando, FL, USA. 15–19 May 2006; pp. 3485–3491. DOI

Walsh C.J., Endo K., Herr H.M. A quasi-passive leg exoskeleton for load-carrying augmentation. Int. J. Humanoid Robot. 2007;4:487–506. doi: 10.1142/S0219843607001126. DOI

Zoss A.B., Kazerooni H., Chu A. Biomechanical design of the Berkeley lower extremity exoskeleton (BLEEX) IEEE/ASME Trans. Mechatron. 2006;11:128–138. doi: 10.1109/TMECH.2006.871087. DOI

Kazerooni H., Steger R., Huang L. Hybrid Control of the Berkeley Lower Extremity Exoskeleton (BLEEX) Int. J. Robot. Res. 2006;25:561–573. doi: 10.1177/0278364906065505. DOI

Gui L., Yang Z., Yang X., Gu W., Zhang Y. Design and Control Technique Research of Exoskeleton Suit; Jinan, China. 18–21 August 2007; pp. 541–546. DOI

Yoshimitsu T., Yamamoto K. Development of a power assist suit for nursing work; Proceedings of the SICE 2004 Annual Conference; Sapporo, Japan. 4–6 August 2004; pp. 577–580.

Kim W.-S., Lee S.-H., Lee H.-D., Yu S.-N., Han J.-S., Han C.-S. Development of the heavy load transferring task oriented exoskeleton adapted by lower extremity using qausi—Active joints; Proceedings of the ICCAS-SICE; Fukuoka, Japan. 18–21 August 2009; pp. 1353–1358.

Kazerooni H., Racine J.L., Huang L., Steger R. On the Control of the Berkeley Lower Extremity Exoskeleton (BLEEX); Proceedings of the 2005 IEEE International Conference on Robotics and Automation; Barcelona, Spain. 18–22 April 2005; pp. 4353–4360.

Toyama S., Yamamoto G. Development of Wearable-Agri-Robot—Mechanism for agricultural work; Proceedings of the Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); St. Louis, MO, USA. 10–15 October 2009; pp. 5801–5806. DOI

Yamamoto K., Hyodo K., Ishii M., Matsuo T. Development of Power Assisting Suit for Assisting Nurse Labor. JSME Int. J. Ser. C. 2002;45:703–711. doi: 10.1299/jsmec.45.703. DOI

Chen F., Yu Y., Ge Y., Sun J., Deng X. WPAL for Enhancing Human Strength and Endurance during Walking; Proceedings of the Information Acquisition, 2007. ICIA ’07. International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing Corporate Author; Taichung, Taiwan. 5–7 June 2007; pp. 487–491. DOI

Raj A.K., Neuhaus P.D., Moucheboeuf A.M., Noorden J.H., Lecoutre D.V. Mina: A Sensorimotor Robotic Orthosis for Mobility Assistance. J. Robot. 2011;2011:8. doi: 10.1155/2011/284352. DOI

Neuhaus P.D., Noorden J.H., Craig T.J., Torres T., Kirschbaum J., Pratt J.E. Design and evaluation of Mina: A robotic orthosis for paraplegics. IEEE Int. Conf. Rehabil. Robot. 2011;2011:5975468. doi: 10.1109/icorr.2011.5975468. PubMed DOI

Esquenazi A., Talaty M., Packel A., Saulino M. The ReWalk Powered Exoskeleton to Restore Ambulatory Function to Individuals with Thoracic-Level Motor-Complete Spinal Cord Injury. Am. J. Phys. Med. Rehabil. 2012;91:911–921. doi: 10.1097/PHM.0b013e318269d9a3. PubMed DOI

Tung W.Y.-W., McKinley M., Pillai M.V., Reid J., Kazerooni H. Design of a Minimally Actuated Medical Exoskeleton With Mechanical Swing-Phase Gait Generation and Sit-Stand Assistance. Dyn. Syst. Control. Conf. 2013;56130:V002T28A004. doi: 10.1115/dscc2013-4038. DOI

Wang S., Wang L., Meijneke C., van Asseldonk E., Hoellinger T., Cheron G., Ivanenko Y., La Scaleia V., Sylos-Labini F., Molinari M., et al. Design and Control of the MINDWALKER Exoskeleton. IEEE Trans. Neural Syst. Rehabil. Eng. 2015;23:277–286. doi: 10.1109/TNSRE.2014.2365697. PubMed DOI

Belforte G., Gastaldi L., Sorli M. Pneumatic active gait orthosis. Mechatronics. 2001;11:301–323. doi: 10.1016/S0957-4158(00)00017-9. DOI

Farris R.J., Quintero H.A., Goldfarb M. Performance evaluation of a lower limb exoskeleton for stair ascent and descent with paraplegia. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012;2012:1908–1911. doi: 10.1109/embc.2012.6346326. PubMed DOI PMC

Bacek T., Moltedo M., Langlois K., Prieto G.A., Sanchez-Villamanan M.C., Gonzalez-Vargas J., Vanderborght B., Lefeber D., Moreno J.C. BioMot exoskeleton—Towards a smart wearable robot for symbiotic human-robot interaction. IEEE Int. Conf. Rehabil. Robot. 2017;2017:1666–1671. doi: 10.1109/icorr.2017.8009487. PubMed DOI

Pröbsting E., Kannenberg A., Zacharias B. Safety and walking ability of KAFO users with the C-Brace® Orthotronic Mobility System, a new microprocessor stance and swing control orthosis. Prosthetics Orthot. Int. 2017;41:65–77. doi: 10.1177/0309364616637954. PubMed DOI PMC

Beil J., Perner G., Asfour T. Design and Control of the Lower Limb Exoskeleton KIT-EXO-1; Proceedings of the IEEE In-ternational Conference on Rehabilitation Robotics (ICORR); Singapore. 11–14 August 2015; pp. 119–124. DOI

Asín-Prieto G., Martínez-Expósito A., Barroso F.O., Urendes E.J., Gonzalez-Vargas J., Alnajjar F.S., González-Alted C., Shimoda S., Pons J.L., Moreno J.C. Haptic Adaptive Feedback to Promote Motor Learning with a Robotic Ankle Exoskeleton Integrated with a Video Game. Front. Bioeng. Biotechnol. 2020;8:113. doi: 10.3389/fbioe.2020.00113. PubMed DOI PMC

McGibbon C.A., Sexton A., Jayaraman A., Deems-Dluhy S., Gryfe P., Novak A., Dutta T., Fabara E., Adans-Dester C., Bonato P. Evaluation of the Keeogo exoskeleton for assisting ambulatory activities in people with multiple sclerosis: An open-label, randomized, cross-over trial. J. Neuroeng. Rehabil. 2018;15:117. doi: 10.1186/s12984-018-0468-6. PubMed DOI PMC

Ekelem A., Goldfarb M. Supplemental Stimulation Improves Swing Phase Kinematics During Exoskeleton Assisted Gait of SCI Subjects with Severe Muscle Spasticity. Front. Neurosci. 2018;12:374. doi: 10.3389/fnins.2018.00374. PubMed DOI PMC

Awad L.N., Esquenazi A., Francisco G.E., Nolan K.J., Jayaraman A. The ReWalk ReStore™ soft robotic exosuit: A multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation. J. Neuroeng. Rehabil. 2020;17:80. doi: 10.1186/s12984-020-00702-5. PubMed DOI PMC

Rueterbories J., Spaich E.G., Larsen B., Andersen O.K. Methods for gait event detection and analysis in ambulatory systems. Med Eng. Phys. 2010;32:545–552. doi: 10.1016/j.medengphy.2010.03.007. PubMed DOI

Kemp B., Janssen A.J., van der Kamp B. Body position can be monitored in 3D using miniature accelerometers and earth-magnetic field sensors. Electroencephalogr. Clin. Neurophysiol. Mot. Control. 1998;109:484–488. doi: 10.1016/S0924-980X(98)00053-8. PubMed DOI

Williamson R., Andrews B.J. Detecting absolute human knee angle and angular velocity using accelerometers and rate gyroscopes. Med Biol. Eng. Comput. 2001;39:294–302. doi: 10.1007/BF02345283. PubMed DOI

Zijlstra W., Bisseling R. Estimation of hip abduction moment based on body fixed sensors. Clin. Biomech. 2004;19:819–827. doi: 10.1016/j.clinbiomech.2004.05.005. PubMed DOI

Giansanti D., Macellari V., Maccioni G., Cappozzo A. Is it feasible to reconstruct body segment 3-D position and orientation using accelerometric data? IEEE Trans. Biomed. Eng. 2003;50:476–483. doi: 10.1109/TBME.2003.809490. PubMed DOI

Zhang B., Jiang S., Wei D., Marschollek M., Zhang W. State of the Art in Gait Analysis Using Wearable Sensors for Healthcare Applications; Proceedings of the 2012 IEEE/ACIS 11th International Conference on Computer and Information Science; Shanghai, China. 30 May–1 June 2012; pp. 213–218. DOI

Kavanagh J.J., Menz H. Accelerometry: A technique for quantifying movement patterns during walking. Gait Posture. 2008;28:1–15. doi: 10.1016/j.gaitpost.2007.10.010. PubMed DOI

Shi J., Zheng Y., Yan Z. Prediction of wrist angle from sonomyography signals with artificial neural networks technique. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2006;2006:3549–3552. doi: 10.1109/iembs.2006.259708. PubMed DOI

Zheng Y., Chan M., Shi J., Chen X., Huang Q. Sonomyography: Monitoring morphological changes of forearm muscles in actions with the feasibility for the control of powered prosthesis. Med Eng. Phys. 2006;28:405–415. doi: 10.1016/j.medengphy.2005.07.012. PubMed DOI

Hodges P., Pengel L., Herbert R., Gandevia S. Measurement of muscle contraction with ultrasound imaging. Muscle Nerve. 2003;27:682–692. doi: 10.1002/mus.10375. PubMed DOI

Chen X., Zheng Y.-P., Guo J.-Y., Shi J. Sonomyography (SMG) Control for Powered Prosthetic Hand: A Study with Normal Subjects. Ultrasound Med. Biol. 2010;36:1076–1088. doi: 10.1016/j.ultrasmedbio.2010.04.015. PubMed DOI

Ruiz-Muñoz M., Cuesta-Vargas A.I. Electromyography and sonomyography analysis of the tibialis anterior: A cross sectional study. J. Foot Ankle Res. 2014;7:11. doi: 10.1186/1757-1146-7-11. PubMed DOI PMC

Al-Mulla M.R., Sepulveda F., Colley M. A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue. Sensors. 2011;11:3545–3594. doi: 10.3390/s110403545. PubMed DOI PMC

Jarić S., Radovanović S., Milanović S., Ljubisavljević M., Anastasijević R. A comparison of the effects of agonist and antagonist muscle fatigue on performance of rapid movements. Eur. J. Appl. Physiol. Occup. Physiol. 1997;76:41–47. doi: 10.1007/s004210050210. PubMed DOI

Guo J.-Y., Zheng Y.-P., Huang Q.-H., Chen X. Dynamic monitoring of forearm muscles using one-dimensional sonomyography system. J. Rehabil. Res. Dev. 2008;45:187–195. doi: 10.1682/JRRD.2007.02.0026. PubMed DOI

Dong H., Ugalde I., Figueroa N., El Saddik A. Towards Whole Body Fatigue Assessment of Human Movement: A Fatigue-Tracking System Based on Combined sEMG and Accelerometer Signals. Sensors. 2014;14:2052–2070. doi: 10.3390/s140202052. PubMed DOI PMC

Taelman J., Vanderhaegen J., Robijns M., Naulaers G., Spaepen A., Van Huffel S. Estimation of muscle fatigue using surface electromyography and near-infrared spectroscopy. Adv. Exp. Med. Biol. 2011;701:353–359. doi: 10.1007/978-1-4419-7756-4_48. PubMed DOI

Perry-Rana S.R., Housh T.J., Johnson G.O., Bull A.J., Ms J.M.B., Cramer J. MMG and EMG responses during fatiguing isokinetic muscle contractions at different velocities. Muscle Nerve. 2002;26:367–373. doi: 10.1002/mus.10214. PubMed DOI

Gams A., Petrič T., Debevec T., Babič J. Effects of Robotic Knee Exoskeleton on Human Energy Expenditure. IEEE Trans. Biomed. Eng. 2013;60:1636–1644. doi: 10.1109/TBME.2013.2240682. PubMed DOI

Tan D.W., Schiefer M.A., Keith M.W., Anderson J.R., Tyler J., Tyler D.J. A neural interface provides long-term stable natural touch perception. Sci. Transl. Med. 2014;6:257ra138. doi: 10.1126/scitranslmed.3008669. PubMed DOI PMC

Raspopovic S., Capogrosso M., Petrini F.M., Bonizzato M., Rigosa J., Di Pino G., Carpaneto J., Controzzi M., Boretius T., Fernandez E., et al. Restoring Natural Sensory Feedback in Real-Time Bidirectional Hand Prostheses. Sci. Transl. Med. 2014;6:222ra19. doi: 10.1126/scitranslmed.3006820. PubMed DOI

Romero-Laiseca M.A., Delisle-Rodriguez D., Cardoso V., Gurve D., Loterio F., Nascimento J.H.P., Krishnan S., Neto A.F., Bastos-Filho T. A Low-Cost Lower-Limb Brain-Machine Interface Triggered by Pedaling Motor Imagery for Post-Stroke Patients Rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 2020;28:988–996. doi: 10.1109/TNSRE.2020.2974056. PubMed DOI

Yuan W., Li Z. Brain Teleoperation Control of a Nonholonomic Mobile Robot Using Quadrupole Potential Function. IEEE Trans. Cogn. Dev. Syst. 2019;11:527–538. doi: 10.1109/TCDS.2018.2869903. DOI

Ramos-Murguialday A., Broetz D., Rea M., Läer L., Yilmaz O., Msc F.L.B., Liberati G., Curado M.R., Garcia-Cossio E., Vyziotis A., et al. Brain-machine interface in chronic stroke rehabilitation: A controlled study. Ann. Neurol. 2013;74:100–108. doi: 10.1002/ana.23879. PubMed DOI PMC

Riener R., Rabuffetti M., Frigo C. Stair ascent and descent at different inclinations. Gait Posture. 2002;15:32–44. doi: 10.1016/S0966-6362(01)00162-X. PubMed DOI

Scandaroli G.G., Borges G.A., Ishihara J.Y., Terra M.H., da Rocha A.F., Nascimento F.A.D.O. Estimation of foot orientation with respect to ground for an above knee robotic prosthesis; Proceedings of the 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems; St. Louis, MO, USA. 10–15 October 2009; pp. 1112–1117. DOI

Carlson T., Millan J.D.R. Brain-Controlled Wheelchairs: A Robotic Architecture. IEEE Robot. Autom. Mag. 2013;20:65–73. doi: 10.1109/MRA.2012.2229936. DOI

Castano L.M., Flatau A.B. Smart fabric sensors and e-textile technologies: A review. Smart Mater. Struct. 2014;23:53001. doi: 10.1088/0964-1726/23/5/053001. DOI

Gonçalves C., da Silva A.F., Gomes J., Simoes R. Wearable E-Textile Technologies: A Review on Sensors, Actuators and Control Elements. Inventions. 2018;3:14. doi: 10.3390/inventions3010014. DOI