Publication: A Socially Assistive Robot for Long-Term Cardiac Rehabilitation in the Real World
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Aamot, I. L., Forbord, S. H., Karlsen, T., and Støylen, A. (2014). Does rating of perceived exertion result in target exercise intensity during interval training in cardiac rehabilitation? A study of the Borg scale versus a heart rate monitor. J. Sci. Med. Sport 17, 541–545. doi: 10.1016/j.jsams.2013.07.019
Aguirre, A., Sierra, S. D., Múnera, M., and Cifuentes, C. A. (2020). Online system for gait parameters estimation using a lrf sensor for assistive devices. IEEE Sens. J. doi: 10.1109/JSEN.2020.3028279
Altenhoener, T., Leppin, A., Grande, G., and Romppel, M. (2005). Social inequality in patients? physical and psychological state and participation in rehabilitation after myocardial infarction in Germany. Int. J. Rehabil. Res. 28, 251–257. doi: 10.1097/00004356-200509000-00008
Anderson, L., Thompson, D. R., Oldridge, N., Zwisler, A., Rees, K., Martin, N., et al. (2016). Exercise-based cardiac rehabilitation for coronary heart disease. Cochrane Syst. Rev. 67, 1–12. doi: 10.1002/14651858.CD001800. pub3
Apos Therapy (2021). Spatio-Temporal Parameters - What Do They Mean. AposHealth R UK. Available online at: https://www.aposhealth.co.uk/blog/ apostherapy/spatio-temporal-parameters-what-do-they-mean/ (accessed February 26, 2021)
Beswick, A. D., Rees, K., West, R. R., Taylor, F. C., Burke, M., Griebsch, I., et al. (2005). Improving uptake and adherence in cardiac rehabilitation: literature review. J. Adv. Nurs. 49, 538–555. doi: 10.1111/j.1365-2648.2004.03327.x
Bethell, H., Lewin, R., and Dalal, H. (2009). Cardiac rehabilitation in the United Kingdom. Heart 95, 271–275. doi: 10.1136/hrt.2007.134338
Bickmore, T. W., and Picard, R. W. (2005). Establishing and maintaining longterm human-computer relationships. ACM Trans. Comput. Hum. Interact. 12, 293–327. doi: 10.1145/1067860.1067867
Borg, G. (1998). Borg’s Perceived Exertion and Pain Scales. Champaign, IL: Human Kinetics.
Carlson, J., Johnson, J., Franklin, B., and VanderLaan, R. (2000). Program participation, exercise adherence, cardiovascular outcomes, and program cost of traditional versus modified cardiac rehabilitation. Am. J. Cardiol. 86, 17–23. doi: 10.1016/S0002-9149(00)00822-5
Carnethon, M., Sternfeld, B., Liu, K., Jcob, D., Schreiner, P., Williams, D., et al. (2015). Correlates of heart rate recovery over 20 years in a population sample. J. Investig. Dermatol. 135, 612–615. doi: 10.1249/MSS.0b013e31822cb190
Carrillo, F. M., Butchart, J., Kruse, N., Scheinberg, A., Wise, L., and McCarthy, C. (2018). “Physiotherapists’ acceptance of a socially assistive robot in ongoing clinical deployment,” in 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (Nanjing), 850–855. doi: 10.1109/ROMAN.2018.8525508
Casas, J., Céspedes, N., Cifuentes, C., Gutierrez, L. F., Rincón-Roncancio, M., and Múnera, M. (2019). Expectation vs. reality: Attitudes towards a socially assistive robot in cardiac rehabilitation. Appl. Sci. 9:4651. doi: 10.3390/app9214651
Casas, J., Gomez, N. C., Senft, E., Irfan, B., Gutiérrez, L. F., Rincón, M., et al. (2018c). “Architecture for a social assistive robot in cardiac rehabilitation,” in 2018 IEEE 2nd Colombian Conference on Robotics and Automation (CCRA) (Barranquilla), 1–6. doi: 10.1109/CCRA.2018.8588133
Casas, J., Irfan, B., Senft, E., Gutiérrez, L., Rincon-Roncancio, M., Munera, M., et al. (2018a). “Social assistive robot for cardiac rehabilitation: a pilot study with patients with angioplasty,” in Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL: ACM), 79–80. doi: 10.1145/3173386.3177052
Casas, J., Irfan, B., Senft, E., Gutierrez, L., Rincon-Roncancio, M., Munera, M., et al. (2018b). “Towards a sar system for personalized cardiac rehabilitation: a patient with PCI,” in 2018 ACM/IEEE International Conference on Human-Robot Interaction Personal Robots for Exercising and Coaching Workshop (Chicago, IL: ACM).
Casas, J., Senft, E., Gutierrez, L. F., Rincon-Rocancio, M., Munera, M., Belpaeme, T., et al. (2020). Social assistive robots: assessing the impact of a training assistant robot in cardiac rehabilitation. Int. J. Soc. Robot. 13, 1–15. doi: 10.1007/s12369-020-00708-y
Clark, A. M., Hartling, L., Vandermeer, B., and McAlister, F. A. (2005). Meta-analysis: secondary prevention programs for patients with coronary artery disease. Ann. Intern. Med. 143, 659–672. doi: 10.7326/0003-4819-143-9-200511010-00010
Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. New York, NY: Routledge Academic.
Cooper, A. F., Weinman, J., Hankins, M., Jackson, G., and Horne, R. (2007). Assessing patients’ beliefs about cardiac rehabilitation as a basis for predicting attendance after acute myocardial infarction. Heart 93, 53–58. doi: 10.1136/hrt.2005.081299
Eriksson, J., Mataric, M. J., and Winstein, C. J. (2005). “Hands-off assistive ´ robotics for post-stroke arm rehabilitation,” in 9th International Conference on Rehabilitation Robotics, 2005, ICORR 2005 (Chicago, IL), 21–24. doi: 10.1109/ICORR.2005.1501042
Fasola, J., and Mataric, M. J. (2010). “Robot exercise instructor: a socially assisti ´ ve robot system to monitor and encourage physical exercise for the elderly,” in Proceedings - IEEE International Workshop on Robot and Human Interactive Communication (Viareggio), 416–421. doi: 10.1109/ROMAN.2010.5598658
Fasola, J., and Mataric, M. J. (2012). Using socially assistive human-robot interaction to motivate physical exercise for older adults. Proc. IEEE 100, 2512–2526. doi: 10.1109/JPROC.2012.2200539
Fasola, J., and Mataric, M. J. (2013). A socially assistive robot exercise coach for th ´ e elderly. J. Hum. Robot Interact. 2, 3–32. doi: 10.5898/JHRI.2.2.Fasola
Feil-Seifer, D., and Mataric, M. J. (2005). “Defining socially assistive robotics,” in ´ 9th International Conference on Rehabilitation Robotics (ICORR) (Chicago, IL), 465–468. doi: 10.1109/ICORR.2005.1501143
Gadde, P., Kharrazi, H., Patel, H., and MacDorman, K. F. (2011). Toward monitoring and increasing exercise adherence in older adults by robotic intervention: a proof of concept study. J. Robot. 2011, 1–11. doi: 10.1155/2011/438514
Galve, E., Alegria, E., Cordero, A., Facila, L., de Bobadilla, J. F., LluisGanella, C., et al. (2014). Temas de actualidad en cardiologia: riesgo vascular y rehabilitacion cardiaca. Revista Espanola de Cardiologia 67, 203–210. doi: 10.1016/j.recesp.2013.09.021
Giuliano, C., Parmenter, B. J., Baker, M., Mitchell, B. L., Williams, A. D., Lyndon, K., et al. (2017). Cardiac rehabilitation for patients with coronary artery disease: a practical guide to enhance patient outcomes through continuity of care. Clin. Med. Insights Cardiol. 11, 1–7. doi: 10.1177/1179546817710028
Glass, G. V., McGaw, B., and Smith, M. L. (1981). Meta-Analysis Insocial Research. Newbury Park, CA: Sage
Gockley, R., Bruce, A., Forlizzi, J., Michalowski, M., Mundell, A., Rosenthal, S., et al. (2005). “Designing robots for long-term social interaction,” in 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (Edmonton, AB), 1338–1343. doi: 10.1109/IROS.2005.1545303
Gockley, R., and Mataric, M. J. (2006). “Encouraging physical therapy compliance with a hands-off mobile robot,” in Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, HRI?06 (New York, NY: Association for Computing Machinery), 150–155. doi: 10.1145/1121241.1121268
Hammill, B. G., Curtis, L. H., Schulman, K. A., and Whellan, D. J. (2010). Relationship between cardiac rehabilitation and long-term risks of death and myocardial infarction among elderly medicare beneficiaries. Circulation 121, 63–70. doi: 10.1161/CIRCULATIONAHA.109.876383
Irfan, B., Céspedes Gomez, N., Casas, J., Senft, E., Gutiérrez, L. F., RinconRoncancio, M., et al. (2020). “Using a personalised socially assistive robot for cardiac rehabilitation: a long-term case study,” in 29th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (IEEE), 124–130. doi: 10.1109/RO-MAN47096.2020.9223491
James, G. S. (1951). The comparison of several groups of observations when the ratios of the population variances are unknown. Biometrika 38, 324–329. doi: 10.1093/biomet/38.3-4.324
Johansen, S. (1980). The welch-james approximation to the distribution of the residual sum of squares in a weighted linear regression. Biometrika 67, 85–92. doi: 10.1093/biomet/67.1.85
Jolly, K., Taylor, R., Lip, G., Greenfield, S., Raftery, J., Mant, J., et al. (2007). The Birmingham rehabilitation uptake maximisation study (brum). Homebased compared with hospital-based cardiac rehabilitation in a multi-ethnic population: cost-effectiveness and patient adherence. Health Technol. Assess. 11, 1–118. doi: 10.3310/hta11350
Kang, K. I., Freedman, S., Mataric, M. J., Cunningham, M. J., and Lopez, B. (2005). “A hands-off physical therapy assistance robot for cardiac patients,” in 9th International Conference on Rehabilitation Robotics, 2005 (ICORR 2005) (Chicago, IL), 337–340. doi: 10.1109/ICORR.2005.1501114
Keselman, H. J., Wilcox, R. R., and Lix, L. M. (2003). A generally robust approach to hypothesis testing in independent and correlated groups designs. Psychophysiology 40, 586–596. doi: 10.1111/1469-8986.00060
Kidd, C. D., and Breazeal, C. (2007). “A robotic weight loss coach,” in Proceedings of the 22nd National Conference on Artificial Intelligence (Vancouver, BC: AAAI Press), 1985–1986
Kraus, W., and Keteyian, S. (2007). Cardiac Rehabilitation. Totowa, NJ: Humana Press. doi: 10.1007/978-1-59745-452-0
Lane, G. W., Noronha, D., Rivera, A., Craig, K., Yee, C., Mills, B., et al. (2016). Effectiveness of a social robot, “Paro,” in a VA long-term care setting. Psychol. Serv. 13, 292–299. doi: 10.1037/ser0000080
Langer, A., Feingold-Polak, R., Mueller, O., Kellmeyer, P., and Levy-Tzedek, S. (2019). Trust in socially assistive robots: considerations for use in rehabilitation. Neurosci. Biobehav. Rev. 104, 231–239. doi: 10.1016/j.neubiorev.2019.07.014
Lara, J. S., Casas, J., Aguirre, A., Munera, M., Rincon-Roncancio, M., Irfan, B., et al. (2017). “Human-robot sensor interface for cardiac rehabilitation,” in 2017 International Conference on Rehabilitation Robotics (ICORR) (London), 1013–1018. doi: 10.1109/ICORR.2017.8009382
Lawler, P. R., Filion, K. B., and Eisenberg, M. J. (2011). Efficacy of exercise-based cardiac rehabilitation post-myocardial infarction: a systematic review and meta-analysis of randomized controlled trials. Am. Heart J. 162, 571.e2–584.e2. doi: 10.1016/j.ahj.2011.07.017
Leite, I., Martinho, C., and Paiva, A. (2013). Social robots for long-term interaction: a survey. Int. J. Soc. Robot. 5, 291–308. doi: 10.1007/s12369-013-0178-y
Lemaignan, S., Garcia, F., Jacq, A., and Dillenbourg, P. (2016). “From real-time attention assessment to “with-me-ness” in human-robot interaction,” in 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (Christchurch: IEEE), 157–164. doi: 10.1109/HRI.2016.7451747
Li, J. (2015). The benefit of being physically present. Int. J. Hum. Comput. Stud. 77, 23–37. doi: 10.1016/j.ijhcs.2015.01.001
Maclean, N., and Pound, P. (2000). A critical review of the concept of patient motivation in the literature on physical rehabilitation. Soc. Sci. Med. 50, 495–506. doi: 10.1016/S0277-9536(99)00334-2
Martin, C. M., and McConahay, D. R. (1972). Maximal treadmill exercise electrocardiography. Correlations with coronary arteriography and cardiac hemodynamics. Circulation 46, 956–962. doi: 10.1161/01.CIR.46.5.956
Mataric, M., Eriksson, J., Feil-Seifer, D., and Winstein, C. J. (2007 ´ a). Socially assistive robotics for post-stroke rehabilitation. J. Neuroeng. Rehabil. 4, 1–9. doi: 10.1186/1743-0003-4-5
Mataric, M., and Scassellati, B. (2016). “Socially assistive robotics, ´ ” in Springer Handbook of Robotics, eds B. Siciliano and O. Khatib (Cham: Springer), 1973– 1993. doi: 10.1007/978-3-319-32552-1_73
Mataric, M., Tapus, A., and Feil-Seifer, D. (2007b). “Personalized so ´ cially assistive robotics,” in Workshop on Intelligent Systems for Assisted Cognition Rochester, NY
McKee, G., Biddle, M., Donnell, S. O., Mooney, M., Brien, F. O., and Moser, D. K. (2014). Cardiac rehabilitation after myocardial infarction: What influences patients? intentions to attend? Eur. J. Cardiovasc. Nurs. 13, 329–337. doi: 10.1177/1474515113496686
Oldridge, N. B., Guyatt, G. H., Fischer, M. E., and Rimm, A. A. (1988). Cardiac rehabilitation after myocardial infarction: combined experience of randomized clinical trials. JAMA 260, 945–950. doi: 10.1001/jama.260.7.945
Piepoli, M. F., Corre, U., Benzer, W., Bjarnason-Wehrens, B., Dendale, P., Gaita, D., et al. (2010). Secondary prevention through cardiac rehabilitation: from knowledge to implementation. A position paper from the cardiac rehabilitation section of the European Association of Cardiovascular Prevention and Rehabilitation. Eur. J. Cardiovasc. Prev. Rehabil. 17, 1–17. doi: 10.1097/HJR.0b013e3283313592
Pinto, M. J., Aguirre, A., Cifuentes, C. A., and Munera, M. (2020). “Wearable sensors for monitoring exercise and fatigue estimation in rehabilitation,” in Internet of Medical Things: Paradigm of Wearable Devices, eds M. Cardona, V. K. Solanki, and C. E. García Cena (Boca Raton, FL: Taylor & Francis Group).
Riek, L. D. (2017). Healthcare robotics. Commun. ACM 60, 68–78. doi: 10.1145/3127874
Ruano-Ravina, A., Pena-Gil, C., Abu-Assi, E., Raposeiras, S., van ’t Hof, A., Meindersma, E., et al. (2016). Participation and adherence to cardiac rehabilitation programs. A systematic review. Int. J. Cardiol. 223, 436–443. doi: 10.1016/j.ijcard.2016.08.120
Šabanovic, S., Bennett, C. C., Chang, W., and Huber, L. (2013). “Paro robo ´ t affects diverse interaction modalities in group sensory therapy for older adults with dementia,” in 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR) (Seattle, WA: IEEE), 1–6. doi: 10.1109/ICORR.2013.6650427
Scane, K., Alter, D., Oh, P., and Brooks, D. (2012). Adherence to a cardiac rehabilitation home program model of care: a comparison to a well-established traditional on-site supervised program. Appl. Physiol. Nutr. Metab. 37, 206–213. doi: 10.1139/h11-151
Schafer, R., and Anderson, J. (1987). Clinical Biomechanics. Baltimore, MD: Williams & Wilkins
Shahsavari, H., Shahriari, M., and Alimohammadi, N. (2012). Motivational factors of adherence to cardiac rehabilitation. Iran. J. Nurs. Midwif. Res. 17, 318–324
She, J., Nakamura, H., Makino, K., Ohyama, Y., and Hashimoto, H. (2014). Selection of suitable maximum-heart-rate formulas for use with Karvonen formula to calculate exercise intensity. Int. J. Automat. Comput. 12, 62–69. doi: 10.1007/s11633-014-0824-3
Siegert, R. J., and Taylor, W. J. (2004). Theoretical aspects of goalsetting and motivation in rehabilitation. Disabil. Rehabil. 26, 1–8. doi: 10.1080/09638280410001644932
Suaya, J., Stason, W., Ades, P., Normand, S., and Shepard, D. (2009). Cardiac rehabilitation and survival in older coronary patients. J. Am. Coll. Cardiol. 54, 25–33. doi: 10.1016/j.jacc.2009.01.078
Süssenbach, L., Riether, N., Schneider, S., Berger, I., Kummert, F., Lutkebohle, I., et al. (2014). “A robot as fitness companion: towards an interactive action-based motivation model,” in The 23rd IEEE International Symposium on Robot and Human Interactive Communication (Edinburgh), 286–293. doi: 10.1109/ROMAN.2014.6926267
Swift-Spong, K., Short, E., Wade, E., and Mataric, M. J. (2015). “Effects of comparative feedback from a socially assistive robot on self-efficacy in poststroke rehabilitation,” in 2015 IEEE International Conference on Rehabilitation Robotics (ICORR) (Nanyang), 764–769. doi: 10.1109/ICORR.2015. 7281294
Taylor, R., Brown, A., and Ebrahim, S. A. (2012). Exercise-based cardiac rehabilitation in patients with coronary heart disease: meta-analysis outcomes revisited. Future Cardiol. 8, 729–751. doi: 10.2217/fca.12.34
Taylor, R. S., Brown, A., Ebrahim, S., Jolliffe, J., Noorani, H., Rees, K., et al. (2004). Exercise-based rehabilitation for patients with coronary heart disease: systematic review and meta-analysis of randomized controlled trials. Am. J. Med. 116, 682–692. doi: 10.1016/j.amjmed.2004.01.009
Thomas, R. J., King, M., Lui, K., Oldridge, N., Pina, I. L., Spertus, J., et al. (2007). AACVPR/ACC/AHA 2007 performance measures on cardiac rehabilitation for referral to and delivery of cardiac rehabilitation/secondary prevention services. J. Cardiopulm. Rehabil. Prev. 27, 260–290. doi: 10.1097/01.HCR.0000291295.24776.7b
Thompson, D. (2002). Stride Analysis. Available online at: https://ouhsc.edu/ bserdac/dthompso/web/gait/knmatics/stride.htm (accessed June 16, 2020)
Turk-Adawi, K., Supervia, M., Lopez-Jimenez, F., Pesah, E., Ding, R., Britto, R. R., et al. (2019). Cardiac rehabilitation availability and density around the globe. eClinicalmedicine 13, 31–45. doi: 10.1016/j.eclinm.2019.06.007
Turk-Adawi, K. I., Oldridge, N. B., Tarima, S. S., Stason, W. B., and Shepard, D. S. (2013). Cardiac rehabilitation patient and organizational factors: what keeps patients in programs? J. Am. Heart Assoc. 2:e000418. doi: 10.1161/JAHA.113.000418
Vasco, V., Willems, C., Chevalier, P., De Tommaso, D., Gower, V., Gramatica, F., et al. (2019). “Train with me: a study comparing a socially assistive robot and a virtual agent for a rehabilitation task,” in International Conference on Social Robotics (ICSR 2019) (Madrid: Springer). doi: 10.1007/978-3-030-358 88-4_42
Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478. doi: 10.2307/30036540
Villacorta, P. J. (2017). The welchADF package for robust hypothesis testing in unbalanced multivariate mixed models with heteroscedastic and non-normal data. R J. 9, 309–328. doi: 10.32614/RJ-2017-049
Welch, B. L. (1938). The significance of the difference between two means when the population variances are unequal. Biometrika 29, 350–362. doi: 10.1093/biomet/29.3-4.350
Welch, B. L. (1951). On the comparison of several mean values: an alternative approach. Biometrika 38, 330–336. doi: 10.1093/biomet/38.3-4.330
Winkle, K., Caleb-Solly, P., Turton, A., and Bremner, P. (2018). “Social robots for engagement in rehabilitative therapies: design implications from a study with therapists,” in Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction, HRI?18 (New York, NY: Association for Computing Machinery), 289–297. doi: 10.1145/3171221.3171273
World Health Organization (2011). World Report on Disability, Vol. 91. Geneva: The World Bank.