BioRobotics research topics for healthy living and active ageing
Nowadays ICT and robotic technologies are experimenting an increasing number of opportunities to be exploited in several scenarios of daily living, from the home to the city and at work. This trend comes with a number of multidisciplinary scientific challenges, covering both technological fields and clinical areas related to neurodegenerative diseases, neuropsychology and various chronic diseases [1]. Particularly, the integration of Robotics, Internet of Things and Artificial Intelligence is an interesting approach that enables the possibility to design and develop new frontiers in personalized and precision medicine, cognitive frailty and cooperative robotics [2].
In this context, this talk aims to present the main results in the development of BioRobotics solutions, developed during my research activities as assistant professor, aiming to face bioengineering challenges in 1) objective assessment in Parkinson disease, 2) objective neuropsychological assessment and rehabilitation in Mild Cognitive Impairment, 3) physical and cognitive stimulation and rehabilitation in cognitive frailty and 4) augmented cognitive capability in ageing workforce.
The development of wearable sensors for fine biomechanical analysis of movement, combined with artificial intelligence techniques is, for example, at the base of creating a framework to objectively quantify the UPDRS scores in Parkinson [3] and identify challenging opportunities in early diagnosis [4] and therapy control. Additionally, recent advancements in artificial intelligence, such as Adaptive Neuro-Fuzzy [5] and Convolutional Neural Network [6], represent an interesting opportunities to develop systems that can learn and then adapt to the evolution of the disease.
Similarly, the use of these technologies is also increasing in MCI neuropsychological assessment, for example in the development of tools able to combine physical exercise and traditional cognitive test, giving then the possibility to perform a cognitive training while performing an aerobic physical activity and to be used at home without the presence of clinical staff [7-8].
Social robotics highlights another fundamental scientific problem in cognitive human robot interaction, aiming to improve interactions between robots and their users by developing cognitive models for robots and understanding human mental models of robots. In this context, perception capabilities of robots could be achieved through emotion [9] and gesture recognition using vision [10] and wearable [11] sensor systems, combined with supervised or unsupervised approaches. Interestingly, social robotics plays an important role, not only for pure human robot interaction, but also for bioengineering applications, as demonstrated by the fact that a substantial part of research work in this area deals with treatment of children with ASD [9] or elderly with mild cognitive impairments [12].
Finally, the combination of the above-mentioned technologies with augmented and mixed reality is another crucial research topics that represents an effective solution for enhancing safety and cognitive capabilities in elderly workers [13], and also for treatment of neuropsychological disorders [14-15].
References
- F. Cavallo, P. Dario, “AAL Roadmap 2014”, AALIANCE Project - website: http://www.aaliance2.eu/sites/default/files/AA2_WP2_D2%207_RM2_rev5.0.pdf
- M. Dragone, D. Bacciu, C. Gallicchio, A. Micheli, L. Scudiero, Lucio; O. Vermesan, F. Cavallo, A. Saffiotti, P. Simoens, “A comprehensive investigation of Internet of Robotic Things: novelties, gaps and challenges”, IEEE Internet of Things Journal, Submitted.
- Butt, Abdul Haleem, Erika Rovini, Dario Esposito, Giuseppe Rossi, Carlo Maremmani, and Filippo Cavallo. "Biomechanical parameter assessment for classification of Parkinson’s disease on clinical scale." Intern. Journal of Distributed Sensor Networks 13, no. 5 (2017).
- Maremmani, C., F. Cavallo, C. Purcaro, G. Rossi, S. Salvadori, E. Rovini, D. Esposito et al. "Combining olfactory test and motion analysis sensors in Parkinson's disease preclinical diagnosis: a pilot study." Acta Neurologica Scandinavica 137, no. 2 (2018): 204-211.
- Butt, Abdul Haleem, Erika Rovini, Dario Esposito, Carlo Maremmani, Hamido Fujita, Filippo Cavallo, “A Design of Adaptive Neuro-Fuzzy Inference System (ANFIS) using Fuzzy C-Means Clustering for the Diagnosis of Parkinson Disease based on Biomechanical Parameters”, Computers in biology and medicine, in submission.
- Acharya, U. Rajendra, Hamido Fujita, Shu Lih Oh, U. Raghavendra, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, and Yuki Hagiwara. "Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network." Future Generation Computer Systems 79 (2018): 952-959.
- L. Fiorini, M.Maselli, R. Esposito, E. Castro, G. Mancioppi, F. Cecchi, C. Laschi, S. Ottino, C. Rossi, F. Pinori, S. Tocchini, M. T. Sportiello, F. Cavallo, “Foot Inertial Sensing for Combined Cognitive-Motor Exercise of the Sustained Attention Domain”, Trans. Biomedical engineering, 2° round revision.
- M.Maselli, L. Fiorini, F. Cecchi, E. Castro, R. Esposito, F. Cavallo, G. Mancioppi, S. Ottino, F. Pinori, S. Tocchini, M. T. Sportiello, P. Dario, C. Laschi, “Can Physical and Cognitive Training based on Episodic Memory be Combined in a New Protocol for Daily Training?”, Annals of Biomedical Engineering, in submission.
- Cavallo, F., Semeraro, F., Fiorini, L. et al. J Bionic Eng (2018) 15: 185. https://doi.org/10.1007/s42235-018-0015-y.
- Manzi, Alessandro, L. Fiorini, R. Limosani, P. Dario, and F. Cavallo. "Two-person activity recognition using skeleton data." IET Computer Vision 12, no. 1 (2017): 27-35.
- Moschetti, Alessandra, Laura Fiorini, Dario Esposito, Paolo Dario, and Filippo Cavallo. "Toward an Unsupervised Approach for Daily Gesture Recognition in Assisted Living Applications." IEEE Sensors Journal 17, no. 24 (2017): 8395-8403.
- Magyar, Gergely, P. Sinčák, J. Magyar, K.i Yoshida, A. Manzi, and F. Cavallo. "CoWoOZ—A cloud-based teleoperation platform for social robotics." In Applied Machine Intelligence and Informatics (SAMI), 2017 IEEE 15th Intern. Symposium on, pp. 49-54. IEEE, 2017.
[13] Betti, S., R. Molino Lova, E. Rovini, G. Acerbi, L. Santarelli, M. Cabiati, S. Del Ry, and F.Cavallo. "Evaluation of an integrated system of wearable physiological sensors for stress
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Giovedì 26 aprile 2018, ore 14.30
Aula 43, Edificio U6 (primo piano)
Tutti gli interessati sono invitati a partecipare.
Per informazioni:
Prof.ssa Emanuela Bricolo