A quantitative taxonomy of human hand grasps

Stival, Francesca; Michieletto, Stefano; Cognolato, Matteo; Pagello, Enrico; Müller, Henning; Atzori, Manfredo

doi:10.1186/s12984-019-0488-x

Cited by 55 publications

(38 citation statements)

References 52 publications

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“…These results also suggest that, across sessions, the subjects' synergies may have a comparable variability with respect to other people [24,25,50]. This result is not new and was found in this domain of application in recent studies at both the kinematic and EMG levels [51][52][53]. This effect may have implications in prosthesis and robot control based on the muscle synergy paradigms [54].…”

Section: Discussionsupporting

confidence: 67%

Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data

Pale

Atzori

Müller

et al. 2020

Sensors

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Background. Muscle synergy analysis is an approach to understand the neurophysiological mechanisms behind the hypothesized ability of the Central Nervous System (CNS) to reduce the dimensionality of muscle control. The muscle synergy approach is also used to evaluate motor recovery and the evolution of the patients’ motor performance both in single-session and longitudinal studies. Synergy-based assessments are subject to various sources of variability: natural trial-by-trial variability of performed movements, intrinsic characteristics of subjects that change over time (e.g., recovery, adaptation, exercise, etc.), as well as experimental factors such as different electrode positioning. These sources of variability need to be quantified in order to resolve challenges for the application of muscle synergies in clinical environments. The objective of this study is to analyze the stability and similarity of extracted muscle synergies under the effect of factors that may induce variability, including inter- and intra-session variability within subjects and inter-subject variability differentiation. The analysis was performed using the comprehensive, publicly available hand grasp NinaPro Database, featuring surface electromyography (EMG) measures from two EMG electrode bracelets. Methods. Intra-session, inter-session, and inter-subject synergy stability was analyzed using the following measures: variance accounted for (VAF) and number of synergies (NoS) as measures of reconstruction stability quality and cosine similarity for comparison of spatial composition of extracted synergies. Moreover, an approach based on virtual electrode repositioning was applied to shed light on the influence of electrode position on inter-session synergy similarity. Results. Inter-session synergy similarity was significantly lower with respect to intra-session similarity, both considering coefficient of variation of VAF (approximately 0.2–15% for inter vs. approximately 0.1% to 2.5% for intra, depending on NoS) and coefficient of variation of NoS (approximately 6.5–14.5% for inter vs. approximately 3–3.5% for intra, depending on VAF) as well as synergy similarity (approximately 74–77% for inter vs. approximately 88–94% for intra, depending on the selected VAF). Virtual electrode repositioning revealed that a slightly different electrode position can lower similarity of synergies from the same session and can increase similarity between sessions. Finally, the similarity of inter-subject synergies has no significant difference from the similarity of inter-session synergies (both on average approximately 84–90% depending on selected VAF). Conclusion. Synergy similarity was lower in inter-session conditions with respect to intra-session. This finding should be considered when interpreting results from multi-session assessments. Lastly, electrode positioning might play an important role in the lower similarity of synergies over different sessions.

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Section: Discussionsupporting

confidence: 67%

Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data

Pale

Atzori

Müller

et al. 2020

Sensors

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“…All the subjects were asked to concentrate on mimicking the movements rather than on exerting high forces. The set of movements was selected from the hand taxonomy, robotics and rehabilitation literature with the aim of corresponding to the hand movements encountered in activities of daily living (ADL) [22][23][24][25] . Each movement repetition lasted 5 seconds, and it was followed by 3 seconds of rest.…”

Section: Methodsmentioning

confidence: 99%

A large calibrated database of hand movements and grasps kinematics

2020

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Modelling hand kinematics is a challenging problem, crucial for several domains including robotics, 3D modelling, rehabilitation medicine and neuroscience. Currently available datasets are few and limited in the number of subjects and movements. the objective of this work is to advance the modelling of hand kinematics by releasing and validating a large publicly available kinematic dataset of hand movements and grasp kinematics. the dataset is based on the harmonization and calibration of the kinematics data of three multimodal datasets previously released (Ninapro DB1, DB2 and DB5, that include electromyography, inertial and dynamic data). the novelty of the dataset is related to the high number of subjects (77) and movements (40 movements, each repeated several times) for which we release for the first time calibrated kinematic data, resulting in the largest available kinematic dataset. Differently from the previous datasets, the data are also calibrated to avoid sensor nonlinearities. the validation confirms that the data are not affected by experimental procedures and that they are similar to data acquired in real-life conditions.

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“…The signal feature extraction scripts were used in previous works on sEMG data analysis and on kinematics data too. In particular, their application to both sEMG and kinematic data allowed to create recently a quantitative taxonomy of hand movements based on both muscular and kinematic information (Stival et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

Atzori

Müller

2019

Front. Neurorobot.

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Motivation: Hand amputations can dramatically affect the quality of life of a person. Researchers are developing surface electromyography and machine learning solutions to control dexterous and robotic prosthetic hands, however long computational times can slow down this process.Objective: This paper aims at creating a fast signal feature extraction algorithm that can extract widely used features and allow researchers to easily add new ones.Methods: PaWFE (Parallel Window Feature Extractor) extracts the signal features from several time windows in parallel. The MATLAB code is publicly available and supports several time domain and frequency features. The code was tested and benchmarked using 1,2,4,8,16,32, and 48 threads on a server with four Xeon E7- 4820 and 128 GB RAM using the first 5 datasets of the Ninapro database, that are recorded with different acquisition setups.Results: The parallel time window analysis approach allows to reduce the computational time up to 20 times when using 32 cores, showing a very good scalability. Signal features can be extracted in few seconds from an entire data acquisition and in <100 ms from a single time window, easily reducing of up to over 15 times the feature extraction procedure in comparison to traditional approaches. The code allows users to easily add new signal feature extraction scripts, that can be added to the code and on the Ninapro website upon request.Significance: The code allows researchers in machine learning and biosignals data analysis to easily and quickly test modern machine learning approaches on big datasets and it can be used as a resource for real time data analysis too.

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A quantitative taxonomy of human hand grasps

Cited by 55 publications

References 52 publications

Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data

Variability of Muscle Synergies in Hand Grasps: Analysis of Intra- and Inter-Session Data

A large calibrated database of hand movements and grasps kinematics

PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows

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