References
- A. Demeco, L. Zola, A. Frizziero, C. Martini, A. Palumbo, R. Foresti, G. Buccino, and C. Costantino, “Immersive virtual reality in post-stroke rehabilitation: a systematic review,” Sensors, vol. 23, no. 3, p. 1712, 2023.
- N. Marotta, A. Ammendolia, C. Marinaro, A. Demeco, L. Moggio, and C. Costantino, “International classification of functioning, disability and health (icf) and correlation between disability and finance assets in chronic stroke patients,” Acta Bio Medica: Atenei Parmensis, vol. 91, no. 3, p. e2020064, 2020.
- G. Collaborators, V. L. Feigin, B. A. Stark, C. O. Johnson, G. A. Roth, C. Bisignano, G. G. Abady, M. Abbasifard, M. Abbasi-Kangevari, F. Abd-Allah, et al., “Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the global burden of disease study 2019,” The Lancet Neurology, vol. 20, no. 10, pp. 795–820, 2021.
- A. Demeco, R. Foresti, A. Frizziero, N. Daracchi, F. Renzi, M. Rovellini, A. Salerno, C. Martini, L. Pelizzari, and C. Costantino, “The upper limb orthosis in the rehabilitation of stroke patients: The role of 3d printing,” Bioengineering, vol. 10, no. 11, p. 1256, 2023.
- C. M. Stinear, C. E. Lang, S. Zeiler, and W. D. Byblow, “Advances and challenges in stroke rehabilitation,” The Lancet Neurology, vol. 19, no. 4, pp. 348–360, 2020.
- K. S. Hayward, S. F. Kramer, E. J. Dalton, G. R. Hughes, A. Brodtmann, L. Churilov, G. Cloud, D. Corbett, L. Jolliffe, T. Kaffenberger, et al., “Timing and dose of upper limb motor intervention after stroke: a systematic review,” Stroke, vol. 52, no. 11, pp. 3706–3717, 2021.
- R. Bertani, C. Melegari, M. C. De Cola, A. Bramanti, P. Bramanti, and R. S. Calabro, “Effects of robot-assisted upper limb rehabilitation in stroke patients: a systematic review with meta-analysis,” Neurological Sciences, vol. 38, pp. 1561–1569, 2017.
- L. A. Simpson, K. S. Hayward, M. McPeake, T. S. Field, and J. J. Eng, “Challenges of estimating accurate prevalence of arm weakness early after stroke,” Neurorehabilitation and Neural Repair, vol. 35, no. 10, pp. 871–879, 2021.
- A. G. Doğan, “The effect of occupational therapy on upper extremity function and activities of daily living in hemiplegic patients,” Journal of Medicine and Palliative Care, vol. 4, no. 4, pp. 350–354, 2023.
- B. Zhang, K.-P. Wong, and J. Qin, “Effects of virtual reality on the limb motor function, balance, gait, and daily function of patients with stroke: Systematic review,” Medicina, vol. 59, no. 4, p. 813, 2023.
- J. Huang, J.-R. Ji, C. Liang, Y.-Z. Zhang, H.-C. Sun, Y.-H. Yan, and X.-B. Xing, “Effects of physical therapy-based rehabilitation on recovery of upper limb motor function after stroke in adults: a systematic review and meta-analysis of randomized controlled trials,” Annals of Palliative Medicine, vol. 11, no. 2, pp. 52131–52531, 2022.
- M. Franceschini, S. Mazzoleni, M. Goffredo, S. Pournajaf, D. Galafate, S. Criscuolo, M. Agosti, and F. Posteraro, “Upper limb robot-assisted rehabilitation versus physical therapy on subacute stroke patients: A follow-up study,” Journal of Bodywork and Movement Therapies, vol. 24, no. 1, pp. 194–198, 2020.
- M. Coscia, M. J. Wessel, U. Chaudary, J. d. R. Millan, S. Micera, A. Guggisberg, P. Vuadens, J. Donoghue, N. Birbaumer, and F. C. Hummel, “Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke,” Brain, vol. 142, no. 8, pp. 2182–2197, 2019.
- J. Bernhardt, K. S. Hayward, N. Dancause, N. A. Lannin, N. S. Ward, R. J. Nudo, A. Farrin, L. Churilov, L. A. Boyd, T. A. Jones, et al., “A stroke recovery trial development framework: consensus-based core recommendations from the second stroke recovery and rehabilitation roundtable,” International Journal of Stroke, vol. 14, no. 8, pp. 792–802, 2019.
- R. M. Al-Whaibi, M. S. Al-Jadid, H. R. ElSerougy, and W. M. Badawy, “Effectiveness of virtual reality-based rehabilitation versus conventional therapy on upper limb motor function of chronic stroke patients: a systematic review and meta-analysis of randomized controlled trials,” Physiotherapy Theory and Practice, vol. 38, no. 13, pp. 2402–2416, 2022.
- A. N. Malik, H. Tariq, A. Afridi, and F. Azam Rathore, “Technological advancements in stroke rehabilitation,” Journal of Pakistan Medical Association, vol. 72, no. 8, pp. 1672–1674, 2022.
- M. Kyrarini, F. Lygerakis, A. Rajavenkatanarayanan, C. Sevastopoulos, H. R. Nambiappan, K. K. Chaitanya, A. R. Babu, J. Mathew, and F. Makedon, “A survey of robots in healthcare,” Technologies, vol. 9, no. 1, p. 8, 2021.
- M. Germanotta, L. Cortellini, S. Insalaco, and I. Aprile, “Effects of upper limb robot-assisted rehabilitation compared with conventional therapy in patients with stroke: Preliminary results on a daily task assessed using motion analysis,” Sensors, vol. 23, no. 6, p. 3089, 2023.
- S. Giovannini, C. Iacovelli, F. Brau, C. Loreti, A. Fusco, P. Caliandro, L. Biscotti, L. Padua, R. Bernabei, and L. Castelli, “Robotic-assisted rehabilitation for balance and gait in stroke patients (roar-s): study protocol for a preliminary randomized controlled trial,” Trials, vol. 23, no. 1, p. 872, 2022.
- S. B. Reis, W. M. Bernardo, C.A. Oshiro, H. I. Krebs, and A. B. Conforto, “Effects of robotic therapy associated with noninvasive brain stimulation on upper-limb rehabilitation after stroke: systematic review and meta-analysis of randomized clinical trials,” Neurorehabilitation and Neural Repair, vol. 35, no. 3, pp. 256–266, 2021.
- I. Aprile, G. Guardati, V. Cipollini, D. Papadopoulou, A. Mastrorosa, L. Castelli, S. Monteleone, A. Redolfi, S. Galeri, and M. Germanotta, “Robotic rehabilitation: an opportunity to improve cognitive functions in subjects with stroke. an explorative study,” Frontiers in Neurology, vol. 11, p. 588285, 2020.
- R. Iandolo, F. Marini, M. Semprini, M. Laffranchi, M. Mugnosso, A. Cherif, L. De Michieli, M. Chiappalone, and J. Zenzeri, “Perspectives and challenges in robotic neurorehabilitation,” Applied Sciences, vol. 9, no. 15, p. 3183, 2019.
- F. Yakub, A. Z. M. Khudzari, and Y. Mori, “Recent trends for practical rehabilitation robotics, current challenges and the future,” International Journal of Rehabilitation Research, vol. 37, no. 1, pp. 9–21, 2014.
- T. Takebayashi, K. Takahashi, Y. Okita, H. Kubo, K. Hachisuka, and K. Domen, “Impact of the robotic-assistance level on upper extremity function in stroke patients receiving adjunct robotic rehabilitation: sub-analysis of a randomized clinical trial,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, p. 25, 2022.
- G. A. Albanese, A. Bucchieri, J. Podda, A. Tacchino, S. Buccelli, E. De Momi, M. Laffranchi, K. Mannella, M. W. Holmes, J. Zenzeri, et al., “Robotic systems for upper-limb rehabilitation in multiple sclerosis: a swot analysis and the synergies with virtual and augmented environments,” Frontiers in Robotics and AI, vol. 11, p. 1335147, 2024.
- F. Romero-Sanchez, L. Luporini Menegaldo, J. M. Font-Llagunes, and M. Sartori, “Rehabilitation robotics: Challenges in design, control, and real applications,” Frontiers in neurorobotics, vol. 16, p. 957905, 2022.
- P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn, A. Jansen-Troy, and S. Leonhardt, “A survey on robotic devices for upper limb rehabilitation,” Journal of Neuroengineering and Rehabilitation, vol. 11, pp. 1–29, 2014.
- M. Maaref, A. Rezazadeh, K. Shamaei, and M. Tavakoli, “A gaussian mixture framework for co-operative rehabilitation therapy in assistive impedance-based tasks,” IEEE journal of selected topics in signal processing, vol. 10, no. 5, pp. 904–913, 2016.
- Y. Meng, C. Munroe, Y.-N. Wu, and M. Begum, “A learning from demonstration framework to promote home-based neuromotor rehabilitation,” in 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 1126–1131, IEEE, 2016.
- M. Maaref, A. Rezazadeh, K. Shamaei, R. Ocampo, and T. Mahdi, “A bicycle cranking model for assist-as-needed robotic rehabilitation therapy using learning from demonstration,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 653–660,2016.
- S. Pareek and T. Kesavadas, “iart: Learning from demonstration for assisted robotic therapy using lstm,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 477–484, 2019.
- S. F. Atashzar, M. Shahbazi, M. Tavakoli, and R. V. Patel, “A computational-model-based study of supervised haptics-enabled therapist-in-the-loop training for upper-limb poststroke robotic rehabilitation,” IEEE/ASME Transactions on Mechatronics, vol. 23, no. 2, pp. 563–574, 2018.
- P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal, “Learning and generalization of motor skills by learning from demonstration,” in 2009 IEEE International Conference on Robotics and Automation, pp. 763–768, 2009.
- C. Tamantini, M. Lapresa, F. Cordella, F. Scotto di Luzio, C. Lauretti, and L. Zollo, “Combined use of dmp and real objects in robot-aided rehabilitation,” in 2020 Italian Conference on Robotics and Intelligent Machines (I-RIM), 01 2021.
- C. Lauretti, F. Cordella, E. Guglielmelli, and L. Zollo, “Learning by demonstration for planning activities of daily living in rehabilitation and assistive robotics,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1375–1382, 2017.
- A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, “Dynamical movement primitives: learning attractor models for motor behaviors,” Neural computation, vol. 25, no. 2, pp. 328–373, 2013.
- W. Kim, C. Lee, and H. J. Kim, “Learning and generalization of dynamic movement primitives by hierarchical deep reinforcement learning from demonstration,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3117–3123, IEEE, 2018.
- M. Saveriano, F. Abu-Dakka, A. Kramberger, and L. Peternel, “Dynamic movement primitives in robotics: A tutorial survey,” The International Journal of Robotics Research, vol. 42, 09 2023.
- I. A. Seleem, H. El-Hussieny, and S. F. M. Assal, “Motion planning for continuum robots: A learning from demonstration approach,” in 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 868–873, IEEE, 2018.
- A. Li, Z. Liu, W. Wang, M. Zhu, Y. Li, Q. Huo, and M. Dai, “Reinforcement learning with dynamic movement primitives for obstacle avoidance,” Applied Sciences, vol. 11, no. 23, 2021.
- G. Li, Z. Jin, M. Volpp, F. Otto, R. Lioutikov, and G. Neumann, “Prodmp: A unified perspective on dynamic and probabilistic movement primitives,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2325–2332, 2023.
- S. Shaw, D. K. Jha, A. Raghunathan, R. Corcodel, D. Romeres, G. Konidaris, and D. Nikovski, “Constrained dynamic movement primitives for safe learning of motor skills,” 2022.
- A. S. Anand, A. Østvik, E. I. Grøtli, M. Vagia, and J. T. Gravdahl, “Real-time temporal adaptation of dynamic movement primitives for moving targets,” in 2021 20th International Conference on Advanced Robotics (ICAR), pp. 261–268, 2021.
- J.-C. Metzger, O. Lambercy, A. Califfi, D. Dinacci, C. Petrillo, P. Rossi, F. M. Conti, and R. Gassert, “Assessment-driven selection and adaptation of exercise difficulty in robot-assisted therapy: a pilot study with a hand rehabilitation robot,” Journal of neuroengineering and rehabilitation, vol. 11, pp. 1–14, 2014.
- B. Siciliano, “Kinematic control of redundant robot manipulators: A tutorial,” Journal of Intelligent and Robotic Systems, vol. 3, pp. 201–212, 09 1990.
- L. Sciavicco and B. Siciliano, “Modelling and control of robot manipulators,” Measurement Science and Technology, vol. 11, p. 1828, dec 2000.
- D. E. Whitney, “Resolved motion rate control of manipulators and human prostheses,” IEEE Transactions on Man-Machine Systems, vol. 10, no. 2, pp. 47–53, 1969.
- P. Chiacchio and B. Siciliano, “A closed-loop jacobian transpose scheme for solving the inverse kinematics of nonredundant and redundant wrists,” Journal of Robotic Systems, vol. 6, no. 5, pp. 601–630, 1989.
- M. L. Latash and J. G. Anson, “What are “normal movements” in atypical populations?,” Behavioral and Brain Sciences, vol. 19, no. 1, p. 55–68, 1996.