Performance Evaluation of Pattern Recognition Algorithms for Upper Limb Prosthetic Applications

Marinelli, Andrea; Semprini, Marianna; Canepa, Michele; Lombardi, L.; Stedman, S.; Bellingegni, Alberto Dellacasa; Chiappalone, M.; Laffranchi, Matteo; Gruppioni, Emanuele; Michieli, Lorenzo De; Boccardo, Nicolò

doi:10.1109/biorob49111.2020.9224466

Cited by 12 publications

(7 citation statements)

References 17 publications

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“…The algorithm chosen for the object stiffness discrimination task is the NLR classifier. This machine learning algorithm was selected given the good performance shown for multiclass classification problems, and for simplicity reasons, since the NLR is already employed for the Hannes pattern recognition control strategy (Marinelli et al, 2020 ; Di Domenico et al, 2021 ). It is based on the calculation of the class membership probability through the following formulation:…”

Section: Methodsmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

“…Where m is the number of samples used to train the algorithm and y ( i ) is the known class membership of the i th sample (Dellacasa Bellingegni et al, 2017 ; Marinelli et al, 2020 ).…”

Section: Methodsmentioning

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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“…This configuration allows the control of the prosthetic system in a robust and simple way (Hudgins et al 1993). However, the detection of complex and simultaneous movements of the phantom limb can be improved by using an array of EMG electrodes placed on the superficial skin of the residual forearm (Coapt 2017, Dellacasa Bellingegni et al 2017, Ottobock 2019, Marinelli et al 2020, The use of sEMG in prosthetic applications has become the most widespread source of information about voluntary movement (Schmidl 1965) because of the direct correlation between EMG activity and subjects' intentions.…”

Section: Semgmentioning

confidence: 99%

“…Considering an underactuated system, the relation between inputs and outputs for trans-radial case is typically based on the real residual muscle of the forearm. In this case, the research is focused on finding a connection between EMG and movements (Marinelli et al 2020, Nguyen et al 2021.…”

Section: Biomimetic Performancementioning

confidence: 99%

Active upper limb prostheses: a review on current state and upcoming breakthroughs

et al. 2023

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The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish activities of daily living. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of ULPs. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and towards the engineering of human-centered artificial limbs.

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“…Note that the performance of different features and classifiers may vary across applications. We choose the SVM and RMS combination because they were consistently among the better performing classifiers in comparative studies showing higher offline classification accuracy [58]- [61]. The preprocessed data are segmented with 100 ms windows and we computed the RMS for each segment to create a labeled dataset for each participant.…”

Section: Experiments 1 Data Analysismentioning

confidence: 99%

Age-Dependent Upper Limb Myoelectric Control Capability in Typically Developing Children

González¹,

2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Research in EMG-based control of prostheses has mainly utilized adult subjects who have fully developed neuromuscular control. Little is known about children's ability to generate consistent EMG signals necessary to control artificial limbs with multiple degrees of freedom. As a first step to address this gap, experiments were designed to validate and benchmark two experimental protocols that quantify the ability to coordinate forearm muscle contractions in typically developing children. Non-disabled, healthy adults and children participated in our experiments that aimed to measure an individual's ability to use myoelectric control interfaces. In the first experiment, participants performed 8 repetitions of 16 different hand/wrist movements. Using offline classification analysis based on Support VectorMachine, we quantified their ability to consistently produce distinguishable muscle contraction patterns. We demonstrated that children had a smaller number of highly independent movements (can be classified with > 90% accuracy) than adults did. The second experiment measured participants' ability to control the position of a cursor on a 1-DoF virtual slide using proportional EMG control with three different visuomotor gain levels. We found that children had higher failure rates and slower average target acquisitions than adults did, primarily due to longer correction times that did not improve over repetitive practice. We also found that the performance in both experiments was age-dependent in children. The results of this study provide novel insights into the technical and empirical basis to better understand neuromuscular development in children with upper-limb loss.

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Performance Evaluation of Pattern Recognition Algorithms for Upper Limb Prosthetic Applications

Cited by 12 publications

References 17 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

Active upper limb prostheses: a review on current state and upcoming breakthroughs

Age-Dependent Upper Limb Myoelectric Control Capability in Typically Developing Children

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