Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove
This paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the int...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Wearable Technologies |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2631717624000252/type/journal_article |
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| author | Subhash Pratap Kazuaki Ito Shyamanta M. Hazarika |
| author_facet | Subhash Pratap Kazuaki Ito Shyamanta M. Hazarika |
| author_sort | Subhash Pratap |
| collection | DOAJ |
| description | This paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the intricate dynamics of hand movement. The study engaged five subjects to capture a comprehensive dataset that includes contact forces at the fingertips and joint angles, furnishing a detailed portrayal of grasp mechanics. Focusing on grasp synergies, our analysis delved into the quantitative relationships between the correlated forces among the fingers. By manipulating one variable at a time—either the object or the subject—our cross-sectional approach yields rich insights into the nature of grasp forces and angles. The correlation coefficients for finger pairs presented median values ranging from 0.5 to nearly 0.9, indicating varying degrees of inter-finger coordination, with the thumb-index and index-middle pairs exhibiting particularly high synergy. The findings, depicted through spider charts and correlation coefficients, reveal significant patterns of cooperative finger behavior. These insights are crucial for the advancement of hand mechanics understanding and have profound implications for the development of assistive technologies and rehabilitation devices. |
| format | Article |
| id | doaj-art-1134a274ad234475b0475c2089782ddc |
| institution | Kabale University |
| issn | 2631-7176 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Wearable Technologies |
| spelling | doaj-art-1134a274ad234475b0475c2089782ddc2025-01-23T08:02:27ZengCambridge University PressWearable Technologies2631-71762025-01-01610.1017/wtc.2024.25Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data gloveSubhash Pratap0https://orcid.org/0000-0002-9904-4497Kazuaki Ito1Shyamanta M. Hazarika2Biomimetic Robotics and AI Lab, Mechanical Engineering, IIT Guwahati, Guwahati, Assam, India Department of Intelligent Mechanical Engineering, Gifu University, Gifu, JapanDepartment of Intelligent Mechanical Engineering, Gifu University, Gifu, JapanBiomimetic Robotics and AI Lab, Mechanical Engineering, IIT Guwahati, Guwahati, Assam, IndiaThis paper investigates hand grasping, a fundamental activity in daily living, by examining the forces and postures involved in the lift-and-hold phases of grasping. We introduce a novel multi-sensory data glove, integrated with resistive flex sensors and capacitive force sensors, to measure the intricate dynamics of hand movement. The study engaged five subjects to capture a comprehensive dataset that includes contact forces at the fingertips and joint angles, furnishing a detailed portrayal of grasp mechanics. Focusing on grasp synergies, our analysis delved into the quantitative relationships between the correlated forces among the fingers. By manipulating one variable at a time—either the object or the subject—our cross-sectional approach yields rich insights into the nature of grasp forces and angles. The correlation coefficients for finger pairs presented median values ranging from 0.5 to nearly 0.9, indicating varying degrees of inter-finger coordination, with the thumb-index and index-middle pairs exhibiting particularly high synergy. The findings, depicted through spider charts and correlation coefficients, reveal significant patterns of cooperative finger behavior. These insights are crucial for the advancement of hand mechanics understanding and have profound implications for the development of assistive technologies and rehabilitation devices.https://www.cambridge.org/core/product/identifier/S2631717624000252/type/journal_articlegraspsynergiesmulti-sensorydata glovehuman-centered computing |
| spellingShingle | Subhash Pratap Kazuaki Ito Shyamanta M. Hazarika Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove Wearable Technologies grasp synergies multi-sensory data glove human-centered computing |
| title | Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove |
| title_full | Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove |
| title_fullStr | Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove |
| title_full_unstemmed | Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove |
| title_short | Synergistic grasp analysis: A cross-sectional exploration using a multi-sensory data glove |
| title_sort | synergistic grasp analysis a cross sectional exploration using a multi sensory data glove |
| topic | grasp synergies multi-sensory data glove human-centered computing |
| url | https://www.cambridge.org/core/product/identifier/S2631717624000252/type/journal_article |
| work_keys_str_mv | AT subhashpratap synergisticgraspanalysisacrosssectionalexplorationusingamultisensorydataglove AT kazuakiito synergisticgraspanalysisacrosssectionalexplorationusingamultisensorydataglove AT shyamantamhazarika synergisticgraspanalysisacrosssectionalexplorationusingamultisensorydataglove |