دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: Object stiffness recognition and vibratory feedback without ad-hoc sensing on the Hannes prosthesis: A machine learning approach
المؤلفون: Giulia Bruni, Andrea Marinelli, Anna Bucchieri, Nicolò Boccardo, Giulia Caserta, Dario Di Domenico, Giacinto Barresi, Astrid Florio, Michele Canepa, Federico Tessari, Matteo Laffranchi, Lorenzo De Michieli
المصدر: Frontiers in Neuroscience, Vol 17 (2023)
بيانات النشر: Frontiers Media S.A., 2023.
سنة النشر: 2023
المجموعة: LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
مصطلحات موضوعية: closed-loop control, stiffness recognition, vibrotactile feedback, vibromotor, Hannes prosthetic hand, non-linear logistic regression, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: IntroductionIn 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.MethodsBased 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.ResultsThe 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).
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1662-453X
Relation: https://www.frontiersin.org/articles/10.3389/fnins.2023.1078846/full; https://doaj.org/toc/1662-453X
DOI: 10.3389/fnins.2023.1078846
URL الوصول: https://doaj.org/article/3d690e005c2145abac7b752b71d9b8e2
رقم الأكسشن: edsdoj.3d690e005c2145abac7b752b71d9b8e2
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:1662453X
DOI:10.3389/fnins.2023.1078846