A Multi-Body Model of an upper-limb prosthesis for grip force estimation and related object interaction application

Bruni, Giulia; Bucchieri, Anna; Tessari, Federico; Boccardo, Nicolò; Marinelli, Andrea; Momi, Elena De; Laffranchi, Matteo; Michieli, Lorenzo De

doi:10.1109/biorob52689.2022.9925478

Cited by 2 publications

(1 citation statement)

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“…In the first preliminary study (Bruni and Bucchieri, 2021 ), a virtual multi-body model of Hannes was developed to offline demonstrate, with a virtual simulation, how the motor-side current absorption and the position measurement could be correlated with the hand grasp force and the grasped object's stiffness. Subsequently, in the following study (Bruni et al, 2022 ), an Ensemble Bagged Trees classifier was implemented and offline tested with simulated data to validate an approach to distinguish two different objects' stiffness.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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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).

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Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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Human-Centered Functional Task Design for Robotic Upper-Limb Rehabilitation

Bucchieri,

Tessari,

Buccelli

et al. 2023

2023 International Conference on Rehabilitation Robotics (ICORR)

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Robotic rehabilitation has demonstrated slight positive effects compared to traditional care, but there is still a lack of targeted high-level control strategies in the current state-of-the-art for minimizing pathological motor behaviors. In this study, we analyzed upper-limb motion capture data from healthy subjects performing a pick-and-place task to identify task-specific variability in postural patterns. The results revealed consistent behaviors among subjects, presenting an opportunity to develop a novel extraction method for variable volume references based solely on observations from healthy individuals. These human-centered references were tested on a simulated 4 degrees-of-freedom upper-limb exoskeleton, showing its compliant adaptation to the path considering the variance in healthy subjects' motor behavior.

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A Multi-Body Model of an upper-limb prosthesis for grip force estimation and related object interaction application

Cited by 2 publications

References 20 publications

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

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

Human-Centered Functional Task Design for Robotic Upper-Limb Rehabilitation

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