Stiffness Estimation in Single Degree of Freedom Mechanisms using Regression

Balasubramanian, Aravind B.; Magee, David P.; Taylor, David G.

doi:10.1109/iecon48115.2021.9590002

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to these issues, the lack of a suitable feedback restitution method in the prosthetic field is still far from being solved. Two other solutions for object stiffness recognition, without dedicated sensors, were implemented by Balasubramanian et al ( 2021 ) and Wang et al ( 2021 ). Their studies demonstrated the feasibility of these approaches in a robotic scenario using an actuated mechanical gripper.…”

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

View full text Add to dashboard Cite

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).

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Stiffness can be estimated in a fairly straightforward manner from measurements of the force and displacement when the object is squeezed [4]. However, the force-displacement curve of a compliance object can be far from linear, being quite complex instead [5].…”

Section: Introductionmentioning

confidence: 99%

Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors

Lora-Rivera

Oballe-Peinado

Vidal‐Verdú

2023

Sensors

View full text Add to dashboard Cite

This paper presents a procedure for classifying objects based on their compliance with information gathered using tactile sensors. Specifically, smart tactile sensors provide the raw moments of the tactile image when the object is squeezed and desqueezed. A set of simple parameters from moment-versus-time graphs are proposed as features, to build the input vector of a classifier. The extraction of these features was implemented in the field programmable gate array (FPGA) of a system on chip (SoC), while the classifier was implemented in its ARM core. Many different options were realized and analyzed, depending on their complexity and performance in terms of resource usage and accuracy of classification. A classification accuracy of over 94% was achieved for a set of 42 different classes. The proposed approach is intended for developing architectures with preprocessing on the embedded FPGA of smart tactile sensors, to obtain high performance in real-time complex robotic systems.

show abstract

Stiffness Estimation in Single Degree of Freedom Mechanisms using Regression

Cited by 2 publications

References 11 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

Proposal and Implementation of a Procedure for Compliance Recognition of Objects with Smart Tactile Sensors

Contact Info

Product

Resources

About