Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach.

Autor:	Bruni G; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy., Marinelli A; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.; Department of Informatics, Bioengineering, Robotics System Engineering (DIBRIS), University of Genova, Genoa, Italy., Bucchieri A; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.; Department of Electronics, Information and Bioengineering (NearLab), Politecnico of Milan, Milan, Italy., Boccardo N; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.; The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Genoa, Italy., Caserta G; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy., Di Domenico D; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.; Department of Electronics and Telecommunications, Politecnico of Torino, Turin, Italy., Barresi G; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy., Florio A; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy., Canepa M; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.; The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), Genoa, Italy., Tessari F; Newman Laboratory, Massachusetts Institute of Technology, Boston, MA, United States., Laffranchi M; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy., De Michieli L; Rehab Technologies, Istituto Italiano di Tecnologia, Genoa, Italy.
Jazyk:	angličtina
Zdroj:	Frontiers in neuroscience [Front Neurosci] 2023 Feb 16; Vol. 17, pp. 1078846. Date of Electronic Publication: 2023 Feb 16 (Print Publication: 2023).
DOI:	10.3389/fnins.2023.1078846
Abstrakt:	Introduction: In recent years, hand prostheses achieved relevant improvements in term of both motor and functional recovery. However, the rate of devices abandonment, also due to their poor embodiment, is still high. The embodiment defines the integration of an external object - in this case a prosthetic device - into the body scheme of an individual. One of the limiting factors causing lack of embodiment is the absence of a direct interaction between user and environment. Many studies focused on the extraction of tactile information via custom electronic skin technologies coupled with dedicated haptic feedback, though increasing the complexity of the prosthetic system. Contrary wise, this paper stems from the authors' preliminary works on multi-body prosthetic hand modeling and the identification of possible intrinsic information to assess object stiffness during interaction. Methods: Based on these initial findings, this work presents the design, implementation and clinical validation of a novel real-time stiffness detection strategy, without ad-hoc sensing, based on a Non-linear Logistic Regression (NLR) classifier. This exploits the minimum grasp information available from an under-sensorized and under-actuated myoelectric prosthetic hand, Hannes. The NLR algorithm takes as input motor-side current, encoder position, and reference position of the hand and provides as output a classification of the grasped object (no-object, rigid object, and soft object). This information is then transmitted to the user via vibratory feedback to close the loop between user control and prosthesis interaction. This implementation was validated through a user study conducted both on able bodied subjects and amputees. Results: The classifier achieved excellent performance in terms of F1Score (94.93%). Further, the able-bodied subjects and amputees were able to successfully detect the objects' stiffness with a F1Score of 94.08% and 86.41%, respectively, by using our proposed feedback strategy. This strategy allowed amputees to quickly recognize the objects' stiffness (response time of 2.82 s), indicating high intuitiveness, and it was overall appreciated as demonstrated by the questionnaire. Furthermore, an embodiment improvement was also obtained as highlighted by the proprioceptive drift toward the prosthesis (0.7 cm). Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2023 Bruni, Marinelli, Bucchieri, Boccardo, Caserta, Di Domenico, Barresi, Florio, Canepa, Tessari, Laffranchi and De Michieli.)
Databáze:	MEDLINE
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