Dataglove measurement of joint angles in sign language handshapes

Eccarius, Petra; Bour, Rebecca; Scheidt, Robert A.

doi:10.1075/sll.15.1.03ecc

Cited by 14 publications

(24 citation statements)

References 32 publications

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“…The difference in the results can be due to several aspects, such as the exact setup, the number of subjects and the degrees of freedom. The similarity between the two approaches may be limited, as the work by Santello et al did not mention any calibration procedure to obtain anatomical angles from raw data (which can be important to reduce problems related to non-linearities [24]).…”

Section: Resultsmentioning

confidence: 99%

“…In this work the physiological angles of the hand were computed using a calibration method. Calibration is fundamental to have reliable data because nonlinearities and imperfect kinematic representations are normally expected by measurement systems such as data gloves [24, 25].…”

Section: Discussionmentioning

confidence: 99%

“…Third, most studies analyze postural synergies and do not consider the whole movement, despite reach and release phases being a part of the coordination of daily life activity that is intimately related to hand grasp. Fourth, most studies do not specify if calibration procedures were used before analyzing the kinematic data, which is fundamental considering that the nonlinearities and imperfect kinematic representations are normally expected by measurement systems such as data gloves [24, 25]. Last, kinematic synergies are commonly extracted without distinguishing between tasks and subjects, although it is known that kinematic synergies are task and subject-dependent [12].…”

Section: Introductionmentioning

confidence: 99%

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Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset

Jarque-Bou

Scano

Atzori

et al. 2019

J NeuroEngineering Rehabil

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Background Hand grasp patterns require complex coordination . The reduction of the kinematic dimensionality is a key process to study the patterns underlying hand usage and grasping. It allows to define metrics for motor assessment and rehabilitation, to develop assistive devices and prosthesis control methods. Several studies were presented in this field but most of them targeted a limited number of subjects, they focused on postures rather than entire grasping movements and they did not perform separate analysis for the tasks and subjects, which can limit the impact on rehabilitation and assistive applications. This paper provides a comprehensive mapping of synergies from hand grasps targeting activities of daily living. It clarifies several current limits of the field and fosters the development of applications in rehabilitation and assistive robotics. Methods In this work, hand kinematic data of 77 subjects, performing up to 20 hand grasps, were acquired with a data glove (a 22-sensor CyberGlove II data glove) and analyzed. Principal Component Analysis (PCA) and hierarchical cluster analysis were used to extract and group kinematic synergies that summarize the coordination patterns available for hand grasps. Results Twelve synergies were found to account for > 80% of the overall variation. The first three synergies accounted for more than 50% of the total amount of variance and consisted of: the flexion and adduction of the Metacarpophalangeal joint (MCP) of fingers 3 to 5 (synergy #1), palmar arching and flexion of the wrist (synergy #2) and opposition of the thumb (synergy #3). Further synergies refine movements and have higher variability among subjects. Conclusion Kinematic synergies are extracted from a large number of subjects (77) and grasps related to activities of daily living (20). The number of motor modules required to perform the motor tasks is higher than what previously described. Twelve synergies are responsible for most of the variation in hand grasping. The first three are used as primary synergies, while the remaining ones target finer movements (e.g. independence of thumb and index finger). The results generalize the description of hand kinematics, better clarifying several limits of the field and fostering the development of applications in rehabilitation and assistive robotics.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset

Jarque-Bou

Scano

Atzori

et al. 2019

J NeuroEngineering Rehabil

View full text Add to dashboard Cite

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“…Another possible source of difference can be related to non-linearities existing between some joint angles in the CyberGlove sensor output (e.g. abduction/adduction at the metacarpophalangeal joints) [60, 61]. Although this aspect can affect the kinematic data, the kinematic taxonomies are based on grasp similarities in the kinematic feature space (that take into account the distribution of the data) and not directly the joint angles, probably contributing to the similarity with the muscular taxonomy.…”

Section: Discussionmentioning

confidence: 99%

A quantitative taxonomy of human hand grasps

Stival

Michieletto²,

Cognolato

et al. 2019

J NeuroEngineering Rehabil

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Background A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified. Methods This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic. Results The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies. Conclusions The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields.

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“…Instrumented gloves seem the most effective method to collect data from all hand joints continuously without occluding problems, and with no special environmental constraints 12,13 . Note that some sensors have non-linear relationships with anatomical angles due to either their position or the influence of other joint movements 14 , and require using calibration procedures to obtain reliable angles, like that described in a previous work 15 , with a mean precision error of 4.45 degrees.…”

Section: Background and Summarymentioning

confidence: 99%

A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living

et al. 2019

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Linking hand kinematics and forearm muscle activity is a challenging and crucial problem for several domains, such as prosthetics, 3D modelling or rehabilitation. To advance in this relationship between hand kinematics and muscle activity, synchronised and well-defined data are needed. However, currently available datasets are scarce, and the presented tasks and data are often limited. this paper presents the KIN-MUS UJI Dataset that contains 572 recordings with anatomical angles and forearm muscle activity of 22 subjects while performing 26 representative activities of daily living. This dataset is, to our knowledge, the biggest currently available hand kinematics and muscle activity dataset to focus on goal-oriented actions. Data were recorded using a CyberGlove instrumented glove and surface EMG electrodes, both properly synchronised. Eighteen hand anatomical angles were obtained from the glove sensors by a validated calibration procedure. Surface EMG activity was recorded from seven representative forearm areas. The statistics verified that data were not affected by the experimental procedures and were similar to the data acquired under real-life conditions.

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Dataglove measurement of joint angles in sign language handshapes

Cited by 14 publications

References 32 publications

Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset

Kinematic synergies of hand grasps: a comprehensive study on a large publicly available dataset

A quantitative taxonomy of human hand grasps

A calibrated database of kinematics and EMG of the forearm and hand during activities of daily living

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