Quantitative Investigation of Hand Grasp Functionality: Thumb Grasping Behavior Adapting to Different Object Shapes, Sizes, and Relative Positions

Quantification of finger grasps during activities of daily life using convolutional neural networks: A pilot study

Quantifying finger kinematics can improve the authors' understanding of finger function and facilitate the design of efficient prosthetic devices while also identifying movement disorders and assessing the impact of rehabilitation interventions. Here, the authors present a study that quantifies grasps depicted in taxonomies during selected Activities of Daily Living (ADL). A single participant held a series of standard objects using specific grasps which were used to train Convolutional Neural Networks (CNN) for each of the four fingers individually. The experiment also recorded hand manipulation of objects during ADL. Each set of ADL finger kinematic data was tested using the trained CNN, which identified and quantified the grasps required to accomplish each task. Certain grasps appeared more often depending on the finger studied, meaning that even though there are physiological interdependencies, fingers have a certain degree of autonomy in performing dexterity tasks. The identified and most frequent grasps agreed with the previously reported findings, but also highlighted that an individual might have specific dexterity needs which may vary with profession and age. The proposed method can be used to identify and quantify key grasps for finger/hand prostheses, to provide a more efficient solution that is practical in their day-to-day tasks.

Quantitative Investigation of Hand Grasp Functionality: Hand Joint Motion Correlation, Independence, and Grasping Behavior

Modeling and understanding human grasp functionality are fundamental in prosthetics, robotics, medicine, and rehabilitation, since they contribute to exploring motor control mechanism, evaluating grasp function, and designing and controlling prosthetic hands or exoskeletons. However, there are still limitations in providing a comprehensive and quantitative understanding of hand grasp functionality. After simultaneously considering three significant and essential influence factors in daily grasping contained relative position, object shape, and size, this paper presents the tolerance grasping to provide a more comprehensive understanding of human grasp functionality. The results of joint angle distribution and variance explained by PCs supported that tolerance grasping can represent hand grasp functionality more comprehensively. Four synergies are found and account for 93% ± 1.5% of the overall variance. The ANOVA confirmed that there was no significant individual difference in the first four postural synergies. The common patterns of grasping behavior were found and characterized by the mean value of postural synergy across 10 subjects. The independence analysis demonstrates that the tolerance grasping results highly correlate with unstructured natural grasping and more accurately correspond to cortical representation size of finger movement. The potential for exploring the neuromuscular control mechanism of human grasping is discussed. The analysis of hand grasp characteristics that contained joint angle distribution, correlation, independence, and postural synergies, presented here, should be more representative to provide a more comprehensive understanding of hand grasp functionality.