Panizzolo FA, Galiana I, Asbeck AT, Siviy C, Schmidt K, Holt KG, Walsh CJ. A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking. J NeuroEng Rehabil. 2016. https://doi.org/10.1186/s12984-016-0150-9.
Article
PubMed
PubMed Central
Google Scholar
Panizzolo FA, Freisinger GM, Karavas N, Eckert-Erdheim AM, Siviy C, Long A, Zifchock RA, LaFiandra ME, Walsh CJ. Metabolic cost adaptations during training with a soft exosuit assisting the hip joint. Sci Rep. 2019;9(1):1–10. https://doi.org/10.1038/s41598-019-45914-5.
Article
CAS
Google Scholar
Kim J, Lee G, Heimgartner R, Revi DA, Karavas N, Nathanson D, Galiana I, Eckert-Erdheim A, Murphy P, Perry D, Menard N, Choe DK, Malcolm P, Walsh CJ. Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science. 2019;365(6454):668–72. https://doi.org/10.1126/science.aav7536.
Article
CAS
PubMed
Google Scholar
Mooney LM, Lai CH, Rouse EJ. Design and characterization of a biologically inspired quasi- passive prosthetic ankle-foot. 36th Annual international conference of the IEEE engineering in medicince and biology society. 2014; 02139, 1611–1617.
Sawicki GS, Ferris DP. Mechanics and energetics of level walking with powered ankle exoskeletons. J Exp Biol. 2008;211(9):1402–13. https://doi.org/10.1242/jeb.009241.
Article
PubMed
Google Scholar
Bryan GM, Franks PW, Klein SC, Peuchen RJ, Collins SH. A hip-knee-ankle exoskeleton emulator for studying gait assistance. Int J Robot Res. 2020. https://doi.org/10.1177/0278364920961452.
Article
Google Scholar
Zhu H, Nesler C, Divekar N, Peddinti V, Gregg RD. Design principles for compact, backdrivable actuation in partial-assist powered knee orthoses. IEEE/ASME Transactions on Mechatronics. 2021 Jan 20;26(6):3104-15.
Sawicki GS, Beck ON, Kang I, Young AJ. The exoskeleton expansion: improving walking and running economy. J NeuroEng Rehabil. 2020;17(1):1–9. https://doi.org/10.1186/s12984-020-00663-9.
Article
Google Scholar
Mooney LM, Rouse EJ, Herr HM. Autonomous exoskeleton reduces metabolic cost of walking. 2014 36th Annual international conference of the IEEE engineering in medicine and biology society, EMBC 2014 11(1), 3065–3068 (2014). https://doi.org/10.1109/EMBC.2014.6944270. arXiv:759764.
Mooney LM, Rouse EJ, Herr HM. Autonomous exoskeleton reduces metabolic cost of human walking during load carriage. J NeuroEng Rehabil. 2014;11(1):1–5. https://doi.org/10.1186/1743-0003-11-151.
Article
Google Scholar
Malcolm P, Derave W, Galle S, De Clercq D. A simple exoskeleton that assists plantarflexion can reduce the metabolic cost of human walking. PLoS ONE. 2013;8(2):1–7. https://doi.org/10.1371/journal.pone.0056137.
Article
CAS
Google Scholar
Jackson RW, Collins SH. An experimental comparison of the relative benefits of work and torque assistance in ankle exoskeletons. J Appl Physiol. 2015;119(5):541–57. https://doi.org/10.1152/japplphysiol.01133.2014.
Article
CAS
PubMed
Google Scholar
Zhang J, Fiers P, Witte KA, Jackson RW, Poggensee KL, Atkeson CG, Collins SH. Human-in-the-loop optimization of exoskeleton assistance during walking. Science. 2017;1284(June):1280–4. https://doi.org/10.1016/j.gaitpost.2011.08.025.The.
Article
Google Scholar
Nuckols R, Lee S, Swaminathan K, Orzel D, Howe R, Walsh C. Individualization of exosuit assistance based on measured muscle dynamics during versatile walking. Sci Robot. 2021;6(60):1362.
Article
Google Scholar
Donelan JM, Kram R, Kuo AD. Mechanical and metabolic determinants of the preferred step width in human walking. Proc R Soc B Biol Sci. 2001;268(1480):1985–92. https://doi.org/10.1098/rspb.2001.1761.
Article
CAS
Google Scholar
Selinger JC, O’Connor SM, Wong JD, Donelan JM. Humans can continuously optimize energetic cost during walking. Curr Biol. 2015;25(18):2452–6. https://doi.org/10.1016/j.cub.2015.08.016.
Article
CAS
PubMed
Google Scholar
Wong JD, Selinger JC, Donelan JM. Is natural variability in gait sufficient to initiate spontaneous energy optimization in human walking? J Neurophysiol. 2019;121(5):1848–55. https://doi.org/10.1152/jn.00417.2018.
Article
PubMed
PubMed Central
Google Scholar
Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q Manag Inf Syst. 1989;13(3):319–39. https://doi.org/10.2307/249008.
Article
Google Scholar
King WR, He J. A meta-analysis of the technology acceptance model. Inf Manag. 2006;43(6):740–55. https://doi.org/10.1016/j.im.2006.05.003.
Article
Google Scholar
Oh S, Ahn J, Kim B. Adoption of broadband internet in Korea: the role of experience in building attitudes. J Inf Technol. 2003;18(4):267–80.
Article
Google Scholar
Hu PH, Chau P, Sheng OR, Tam K. Examining the technology acceptance model using physician acceptance of telemedicine technology. J Manag Inf Syst. 1999;16:91–112.
Article
Google Scholar
Chuah SHW, Rauschnabel PA, Krey N, Nguyen B, Ramayah T, Lade S. Wearable technologies: the role of usefulness and visibility in smartwatch adoption. Comput Hum Behav. 2016;65:276–84. https://doi.org/10.1016/j.chb.2016.07.047.
Article
Google Scholar
Kingdom F, Prins N. Psychophysics: a practical introduction. 2013;53: 1689–1699. https://doi.org/10.1017/CBO9781107415324.004. arXiv:10111669v3.
Gescheider GA. Psychophysics: the fundamentals. Hove: Psychology Press; 2013.
Book
Google Scholar
Wichmann FA, Hill NJ. The psychometric function: II. Bootstrap-based confidence intervals and sampling. Percept Psychophys. 2001;63(8):1314–29. https://doi.org/10.3758/BF03194545.
Article
CAS
PubMed
Google Scholar
Torgerson WS. Theory and methods of scaling.
Stevens SS, Galanter EH. Ratio scales and category scales for a dozen perceptual continua. J Exp Psychol. 1957;54(6):377.
Article
CAS
PubMed
Google Scholar
Stevens SS. On the psychophysical law. Psychol Rev. 1957;64(3):153–81. https://doi.org/10.1037/h0046162.
Article
CAS
PubMed
Google Scholar
Beebe-Center JG, Waddell D. A general psychological scale of taste. J Psychol. 1948;26:517–24. https://doi.org/10.1080/00223980.1948.9917423.
Article
CAS
PubMed
Google Scholar
Brodie EE, Ross HE. Sensorimotor mechanisms in weight discrimination. Percept Psychophys. 1984;36(5):477–81. https://doi.org/10.3758/BF03207502.
Article
CAS
PubMed
Google Scholar
Shepherd MK, Azocar AF, Major MJ, Rouse EJ. Amputee perception of prosthetic ankle stiffness during locomotion. J NeuroEng Rehabil. 2018;15(1):1–10. https://doi.org/10.1186/s12984-018-0432-5.
Article
Google Scholar
Shepherd MK, Rouse EJ. Comparing preference of ankle-foot stiffness in below-knee amputees and prosthetists. Sci Rep. 2020;10(1):16067. https://doi.org/10.1038/s41598-020-72131-2.
Article
CAS
PubMed
PubMed Central
Google Scholar
Azocar AF, Rouse EJ. Stiffness perception during active ankle and knee movement. IEEE Trans Biomed Eng. 2017;64(12):2949–56.
Article
PubMed
Google Scholar
Azocar AF, Mooney LM, Hargrove LJ, Rouse EJ. Design and characterization of an open-source robotic leg prosthesis. In2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) 2018 Aug 26 (pp. 111-118). IEEE.
Ackerley R, Wasling HB, Ortiz-Catalan M, Brånemark R, Wessberg J. Case studies in neuroscience: sensations elicited and discrimination ability from nerve cuff stimulation in an amputee over time. J Neurophysiol. 2018;120(1):291–5. https://doi.org/10.1152/jn.00909.2017.
Article
PubMed
Google Scholar
Clemente F, Hakansson B, Cipriani C, Wessberg J, Kulbacka-Ortiz K, Brånemark R, Fredén Jansson KJ, Ortiz-Catalan M. Touch and hearing mediate osseoperception. Sci Rep. 2017;7:1–11. https://doi.org/10.1038/srep45363.
Article
CAS
Google Scholar
Lee S, Kim J, Baker L, Long A, Karavas N, Menard N, Galiana I, Walsh CJ. Autonomous multi-joint soft exosuit with augmentation-power-based control parameter tuning reduces energy cost of loaded walking. J NeuroEng Rehabil. 2018;15(1):1–9.
Article
Google Scholar
Ding Y, Kim M, Kuindersma S, Walsh CJ. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Sci Robot. 2018;3(15):1–9. https://doi.org/10.1126/scirobotics.aar5438.
Article
Google Scholar
Lim B, Lee J, Jang J, Kim K, Park YJ, Seo K, Shim Y. Delayed output feedback control for gait assistance with a robotic hip exoskeleton. IEEE Trans Robot. 2019;35(4):1055–62.
Article
Google Scholar
Collins SH, Zhang J, Poggensee KL, Witte KA, Jackson RW, Fiers P, Atkeson CG. Supplementary materials for human-in-the-loop optimization of exoskeleton assistance during walking. Science. 2017;356(6344):1280–4. https://doi.org/10.1126/science.aal5054.
Article
CAS
PubMed
Google Scholar
Borg E, Kaijser L. A comparison between three rating scales for perceived exertion and two different work tests. Scand J Med Sci Sports. 2006;16(1):57–69. https://doi.org/10.1111/j.1600-0838.2005.00448.x.
Article
CAS
PubMed
Google Scholar
Borg E. So what’s that on a scale of 1 to 10. Proceedings of the 24th International Congress of Vexillology (August 2011), 2011; 988–995.
Borg E. On Perceived exertion and its measurement. 2007. http://su.diva-portal.org/smash/get/diva2:197216/FULLTEXT01.
Koller JR, Gates DH, Ferris DP, Remy CD. Confidence in the curve: establishing instantaneous cost mapping techniques using bilateral ankle exoskeletons. J Appl Physiol. 2016;122(2):242–52. https://doi.org/10.1152/japplphysiol.00710.2016.
Article
PubMed
Google Scholar
Galle S, Malcolm P, De Clercq D. 2D Parameter sweep of bilateral exoskeleton actuation: push off timing and work. Dynamic Walking 2014 2014.
Kang I, Molinaro D, Duggal S, Chen Y, Kunapuli P, Young A. Real-time gait phase estimation for robotic hip exoskeleton control during multimodal locomotion. IEEE Robot Autom Lett. 2021;6(2):3491–7. https://doi.org/10.1109/LRA.2021.3062562.
Article
PubMed
PubMed Central
Google Scholar
Camargo J, Flanagan W, Csomay-Shanklin N, Kanwar B, Young A. A machine learning strategy for locomotion classification and parameter estimation using fusion of wearable sensors. IEEE Trans Biomed Eng. 2021;68(5):1569–78. https://doi.org/10.1109/TBME.2021.3065809.
Article
PubMed
Google Scholar
Selinger JC, Donelan JM. Estimating instantaneous energetic cost during non-steady-state gait. J Appl Physiol. 2014;117(11):1406–15. https://doi.org/10.1152/japplphysiol.00445.2014.
Article
PubMed
Google Scholar
Guidetti L, Bolletta F, Gallotta MC, Baldari C, Meucci M, Emerenziani GP. Validity, reliability and minimum detectable change of COSMED K5 portable gas exchange system in breath-by-breath mode. PLoS ONE. 2018;13(12):1–12. https://doi.org/10.1371/journal.pone.0209925.
Article
Google Scholar
Medrano RL, Thomas GC, Rouse E. Methods for measuring the just noticeable difference for variable stimuli: implications for perception of metabolic rate with exoskeleton assistance. Proceedings of the IEEE RAS and EMBS international conference on biomedical robotics and biomechatronics 2020-November, 483–490, 2020. https://doi.org/10.1109/BioRob49111.2020.9224374.
Norwich KH. On the theory of Weber fractions. Percept Psychophys. 1987;42(3):286–98. https://doi.org/10.3758/BF03203081.
Article
CAS
PubMed
Google Scholar
Gescheider GA. Psychophysical measurement of thresholds: differential sensitivity. Psychophysics: the fundamentals. 1997:1-5.
García-Pérez MA, Alcalá-Quintana R. Sampling plans for fitting the psychometric function. Span J Psychol. 2005;8(2):256–89. https://doi.org/10.1017/S113874160000514X.
Article
PubMed
Google Scholar
Kuroda T, Hasuo E. The very first step to start psychophysical experiments. Acoust Sci Technol. 2013;35(1):1–9. https://doi.org/10.1250/ast.35.1.
Article
Google Scholar
Kuss M, Jäkel F, Wichmann FA. Bayesian inference for psychometric functions. J Vis. 2005;5(5):478–92. https://doi.org/10.1167/5.5.8.
Article
PubMed
Google Scholar
Wichmann FA, Jäkel F. Methods in psychophysics. New Jersey: Wiley; 2018. p. 1–42. https://doi.org/10.1002/9781119170174.epcn507.
Book
Google Scholar
Prins N, Kingdom FAA. Applying the model-comparison approach to test specific research hypotheses in psychophysical research using the Palamedes toolbox. Front Psychol. 2018;9:1250. https://doi.org/10.3389/fpsyg.2018.01250.
Article
PubMed
PubMed Central
Google Scholar
Salvatier J, Wiecki TV, Fonnesbeck C. Probabilistic programming in python using pymc3. PeerJ Comput Sci. 2016;2:55.
Article
Google Scholar
Hoffman MD, Gelman A. The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J Mach Learn Res. 2014;15(2008):1593–623 arXiv:1111.4246.
Google Scholar
Vehtari A, Gelman A, Gabry J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat Comput. 2017;27(5): 1413–1432. https://doi.org/10.1007/s11222-016-9696-4. arXiv:1507.04544.
Das Gupta S, Bobbert MF, Kistemaker DA. The metabolic cost of walking in healthy young and older adults—a systematic review and meta analysis. Sci Rep. 2019;9(1):1–10. https://doi.org/10.1038/s41598-019-45602-4.
Article
CAS
Google Scholar
Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Stat Comput. 2014;24(6):997–1016. https://doi.org/10.1007/s11222-013-9416-2arXiv:1307.5928.
Article
Google Scholar
Zandstra EH, Miyapuram KP, Tobler PN. Understanding consumer decisions using behavioral economics. 1st ed. Amsterdam: Elsevier B.V; 2013. p. 197–211. https://doi.org/10.1016/B978-0-444-62604-2.00012-5.
Book
Google Scholar
Green L, Fristoe N, Myerson J. Temporal discounting and preference reversals in choice between delayed outcomes. Psychon Bull Rev. 1994;1(3):383–9. https://doi.org/10.3758/BF03213979.
Article
CAS
PubMed
Google Scholar
Green L, Myerson J, McFadden E. Rate of temporal discounting decreases with amount of reward. Mem Cogn. 1997;25(5):715–23. https://doi.org/10.3758/BF03211314.
Article
CAS
Google Scholar
Simpson CA, Vuchinich RE. Reliability of a measure of temporal discounting. Psychol Rec. 2000;50(1):3–16. https://doi.org/10.1007/BF03395339.
Article
Google Scholar
Critchfield TS, Kollins SH. Temporal discounting: basic research and the analysis of socially important behavior. J Appl Behav Anal. 2001;34(1):101–22. https://doi.org/10.1901/jaba.2001.34-101.
Article
CAS
PubMed
PubMed Central
Google Scholar
Schultz W. Subjective neuronal coding of reward: temporal value discounting and risk. Eur J Neurosci. 2010;31(12):2124–35. https://doi.org/10.1111/j.1460-9568.2010.07282.x.
Article
PubMed
Google Scholar
Bos WVD, McClure SM. Towards a general model of temporal discounting. J Exp Anal Behav. 2013;99(1):58–73. https://doi.org/10.1002/jeab.6.
Article
PubMed
Google Scholar
McDonald K. Multi-objective prioritization in human walking.
Ackermann M, Van Den Bogert AJ. Optimality principles for model-based prediction of human gait. Phys Therapy. 2008;2011(January 31):1–8. https://doi.org/10.1016/j.jbiomech.2009.12.012.Optimality.
Article
Google Scholar
Crowell HP, Kanagaki GB, O’donovan MP, Haynes CA, Park J-H, Neugebauer JM, Hennessy ER, Boynton AC, Mitchell B, Tweedell AJ, Girolamo HJ. Methodologies for evaluating the effects of physical augmentation technologies on soldier performance. US Army Research Laboratory Aberdeen Proving Ground United States (May). (2018). https://doi.org/10.13140/RG.2.2.13662.48961.
Hunter LC, Hendrix EC, Dean JC. The cost of walking downhill: is the preferred gait energetically optimal? J Biomech. 2010;43(10):1910–5. https://doi.org/10.1016/j.jbiomech.2010.03.030.
Article
CAS
PubMed
Google Scholar
Medrano RL, Rouse EJ, Thomas GC. Biological joint loading and exoskeleton design. IEEE Trans Med Robot Bio. 2021;3(3):847–51.
Article
Google Scholar
Thatte N, Duan H, Geyer H. A sample-efficient black-box optimizer to train policies for human-in-the-loop systems with user preferences. IEEE Robot Autom Lett. 2017;2(2):993–1000.
Article
Google Scholar
Ingraham KA, Remy CD, Rouse EJ. User preference of applied torque characteristics for bilateral powered ankle exoskeletons. Proceedings of the IEEE RAS and embs international conference on biomedical robotics and biomechatronics 2020-November, 839–845, 2020. https://doi.org/10.1109/BioRob49111.2020.9224358.
Tucker M, Novoseller E, Kann C, Sui Y, Yue Y, Burdick J, Ames AD. Preference-based learning for exoskeleton gait optimization. 2020 IEEE international conference on robotics and automation (ICRA). 2019. arXiv:1909.12316.
Haile L, Robertson RJ, Nagle EF, Krause MP, Gallagher M, Ledezma CM, Wisniewski KS, Shafer AB, Goss FL. Just noticeable difference in perception of physical exertion during cycle exercise in young adult men and women. Eur J Appl Physiol. 2013;113(4):877–85. https://doi.org/10.1007/s00421-012-2497-3.
Article
PubMed
Google Scholar
Davidson A, Gardinier ES, Gates DH. Within and between-day reliability of energetic cost measures during treadmill walking. Cogent Eng. 2016;3(1):1–7. https://doi.org/10.1080/23311916.2016.1251028.
Article
Google Scholar
Wier CC, Jesteadt W, Green DM. A comparison of method-of-adjustment and forced-choice procedures in frequency discrimination. Percept Psychophys. 1976;19(1):75–9. https://doi.org/10.3758/BF03199389.
Article
Google Scholar
Stevens SS. Problems and methods of psychophysics. Psychological Bulletin. 1958;55(4):177.
Article
CAS
PubMed
Google Scholar
Cardozo BL. Adjusting the method of adjustment: SD vs DL. J Acoust Soc Am. 1965;37(5):786–92. https://doi.org/10.1121/1.1909439.
Article
Google Scholar
Pelli DG, Farell B. Psychophysical methods. Handbook opt 1995;1:29–31.
Google Scholar
Abram SJ, Selinger JC, Donelan JM. Energy optimization is a major objective in the real-time control of step width in human walking. J Biomech. 2019;91:85–91. https://doi.org/10.1016/j.jbiomech.2019.05.010.
Article
PubMed
Google Scholar
Wong JD, O’Connor SM, Selinger JC, Donelan JM. Contribution of blood oxygen and carbon dioxide sensing to the energetic optimization of human walking. J Neurophysiol. 2017. https://doi.org/10.1152/jn.00195.2017.
Article
PubMed
PubMed Central
Google Scholar
Selinger JC, Wong JD, Simha SN, Donelan JM. How people initiate energy optimization and converge on their optimal gaits. J Exp Biol. 2019. https://doi.org/10.1242/jeb.198234.
Article
PubMed
PubMed Central
Google Scholar
Hampson DB, Clair Gibson AS, Lambert MI, Noakes TD. The influence of sensory cues on the perception of exertion during exercise and central regulation of exercise performance. Sports Med. 2001;31(13):935–52. https://doi.org/10.2165/00007256-200131130-00004.
Article
CAS
PubMed
Google Scholar
Gibson ASC, Baden DA, Lambert MI, Lambert EV, Harley XR, Hampson D, Russell VA, Noakes TD. The conscious perception of the sensation of fatigue. Sports Med. 2003;33(3):1–10.
Google Scholar
Scherr J, Wolfarth B, Christle JW, Pressler A, Wagenpfeil S, Halle M. Associations between Borg’s rating of perceived exertion and physiological measures of exercise intensity. Eur J Appl Physiol. 2013;113(1):147–55. https://doi.org/10.1007/s00421-012-2421-x.
Article
PubMed
Google Scholar
Borg GA. Psychophysical bases of perceived exertion. Medicine & science in sports & exercise. 1982.
Prins N. Kingdom fa., Applying the model-comparison approach to test specific research hypotheses in psychophysical research using the Palamedes toolbox. Front Psychol. 2018;9(1250):10–3389.
Google Scholar
Treutwein B. Adaptive psychophysical procedures. Vis Res. 1995;35(17):2503–22. https://doi.org/10.1016/0042-6989(95)00016-X.
Article
CAS
PubMed
Google Scholar
Levitt H. Transformed up-down methods in psychoacoustics. J Acoust Soc Am. 1971;49(2B):467–77. https://doi.org/10.1121/1.1912375.
Article
Google Scholar
Galle S, Malcolm P, Collins SH, De Clercq D. Reducing the metabolic cost of walking with an ankle exoskeleton: interaction between actuation timing and power. J NeuroEng Rehabil. 2017;14(1):1–16. https://doi.org/10.1186/s12984-017-0235-0.
Article
Google Scholar
Hangen H, Melanson E, Tran Z, Kearney JT, Hill JO. Variability of resting metabolic rate. Am J Clin Nutr. 2003;78:1141–4.
Article
Google Scholar
Zhang J, Cheah CC, Collins SH. Torque control in legged locomotion. 1st ed. Amsterdam: Elsevier Inc.; 2017. p. 347–400.
Google Scholar
Teunissen LPJ, Grabowski A, Kram R. Effects of independently altering body weight and body mass on the metabolic cost of running. J Exp Biol. 2007;210(24):4418–27. https://doi.org/10.1242/jeb.004481.
Article
PubMed
Google Scholar
Medrano RL, Thomas GC, Rouse EJ. Can humans perceive the metabolic benefit provided by augmentative exoskeletons? [Source Code]. https://doi.org/10.24433/CO.8128032.v1.