Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know

Gregori, Valentina; Gijsberts, Arjan; Caputo, Barbara

doi:10.1109/icorr.2017.8009401

Cited by 9 publications

(15 citation statements)

References 24 publications

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“…To overcome distribution mismatches, transfer learning and domain adaptation approaches have been used in several domains, including computer vision (Saenko et al, 2010 ; Tommasi et al, 2010 ), and natural language processing (Ben-David et al, 2010 ; Daumé et al, 2010 ). In myoelectric control, several studies explored the use of previous models from different subjects to reduce the amount of required training data (Farina et al, 2002 ; Tommasi et al, 2013 ; Patricia et al, 2014 ), but performance increase was not confirmed after proper model optimization (Gregori et al, 2017 ). The fact that the motor modules are common to the subjects can provide physiological foundations to include within the prosthesis a subject-independent motor memory.…”

Section: Discussionmentioning

confidence: 99%

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps

et al. 2018

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Background: Kinematic and muscle patterns underlying hand grasps have been widely investigated in the literature. However, the identification of a reduced set of motor modules, generalizing across subjects and grasps, may be valuable for increasing the knowledge of hand motor control, and provide methods to be exploited in prosthesis control and hand rehabilitation.Methods: Motor muscle synergies were extracted from a publicly available database including 28 subjects, executing 20 hand grasps selected for daily-life activities. The spatial synergies and temporal components were analyzed with a clustering algorithm to characterize the patterns underlying hand-grasps.Results: Motor synergies were successfully extracted on all 28 subjects. Clustering orders ranging from 2 to 50 were tested. A subset of ten clusters, each one represented by a spatial motor module, approximates the original dataset with a mean maximum error of 5% on reconstructed modules; however, each spatial synergy might be employed with different timing and recruited at different grasp stages. Two temporal activation patterns are often recognized, corresponding to the grasp/hold phase, and to the pre-shaping and release phase.Conclusions: This paper presents one of the biggest analysis of muscle synergies of hand grasps currently available. The results of 28 subjects performing 20 different grasps suggest that a limited number of time dependent motor modules (shared among subjects), correctly elicited by a control activation signal, may underlie the execution of a large variety of hand grasps. However, spatial synergies are not strongly related to specific motor functions but may be recruited at different stages, depending on subject and grasp. This result can lead to applications in rehabilitation and assistive robotics.

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Section: Discussionmentioning

confidence: 99%

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps

et al. 2018

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“…The experimental setup is based on the "realistic setting" proposed in a previous work [32]. The setting was considered as "realistic" as it exploits real data coming from the Ninapro Dataset, recorded during the execution of daily life gestures.…”

Section: Methodsmentioning

confidence: 99%

“…The standardized data were used according to the protocol already proposed for control by Englehart and Hudgins [7], where features were extracted from a sliding window of 200 ms and an increment of 10 ms. As described in the papers presenting the datasets, sEMG signals were filtered from 50 Hz (and harmonics) power-line interference using a Hampel filter [31,36]. The resulting set of windows was subsequently split in the training set and test set as inputs for the classifier [32]. The sEMG representation used in this setting was the average of the marginal discrete wavelet transform (mDWT), mean absolute value (MAV) and variance (VAR) features [37].…”

Section: Datamentioning

confidence: 99%

“…Although different studies stated the efficacy of the use of domain adaptation on gesture recognition, they were usually applied on able-bodied subjects. Exploiting previously achieved results on intact subjects [30] from the Non-Invasive Adaptive Prosthetics (NinaPro) database [31], Gregori et al [32] extended the study to amputee subjects and presented a novel framework for a realistic experimental setup. They found that, if the hyperparameters were properly tuned, transfer learning approaches showed the same performance of the standard methods that did not employ prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study

Marano

Brambilla

Mira

et al. 2021

Sensors

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One major challenge limiting the use of dexterous robotic hand prostheses controlled via electromyography and pattern recognition relates to the important efforts required to train complex models from scratch. To overcome this problem, several studies in recent years proposed to use transfer learning, combining pre-trained models (obtained from prior subjects) with training sessions performed on a specific user. Although a few promising results were reported in the past, it was recently shown that the use of conventional transfer learning algorithms does not increase performance if proper hyperparameter optimization is performed on the standard approach that does not exploit transfer learning. The objective of this paper is to introduce novel analyses on this topic by using a random forest classifier without hyperparameter optimization and to extend them with experiments performed on data recorded from the same patient, but in different data acquisition sessions. Two domain adaptation techniques were tested on the random forest classifier, allowing us to conduct experiments on healthy subjects and amputees. Differently from several previous papers, our results show that there are no appreciable improvements in terms of accuracy, regardless of the transfer learning techniques tested. The lack of adaptive learning is also demonstrated for the first time in an intra-subject experimental setting when using as a source ten data acquisitions recorded from the same subject but on five different days.

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“…Similar techniques have been used for the classification of electromyography (EMG) signals (Gregori et al, 2017; Tommasi et al, 2013). The benefit of such techniques in the control of prosthetic devices is, however, debated (Gregori et al, 2017) and may need to be further developed. A similar problem is being studied in robotics for training visual recognition systems from small dataset (Pasquale et al, 2016; Schwarz et al, 2015).…”

Section: Neuroengineering Meets Aimentioning

confidence: 99%

Progress in Neuroengineering for brain repair: New challenges and open issues

Panuccio

Semprini

Natale

et al. 2018

Brain and Neuroscience Advances

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In recent years, biomedical devices have proven to be able to target also different neurological disorders. Given the rapid ageing of the population and the increase of invalidating diseases affecting the central nervous system, there is a growing demand for biomedical devices of immediate clinical use. However, to reach useful therapeutic results, these tools need a multidisciplinary approach and a continuous dialogue between neuroscience and engineering, a field that is named neuroengineering. This is because it is fundamental to understand how to read and perturb the neural code in order to produce a significant clinical outcome. In this review, we first highlight the importance of developing novel neurotechnological devices for brain repair and the major challenges expected in the next years. We describe the different types of brain repair strategies being developed in basic and clinical research and provide a brief overview of recent advances in artificial intelligence that have the potential to improve the devices themselves. We conclude by providing our perspective on their implementation to humans and the ethical issues that can arise. Neuroengineering approaches promise to be at the core of future developments for clinical applications in brain repair, where the boundary between biology and artificial intelligence will become increasingly less pronounced.

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Adaptive learning to speed-up control of prosthetic hands: A few things everybody should know

Cited by 9 publications

References 24 publications

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps

Muscle Synergy Analysis of a Hand-Grasp Dataset: A Limited Subset of Motor Modules May Underlie a Large Variety of Grasps

Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study

Progress in Neuroengineering for brain repair: New challenges and open issues

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