1
|
Shenoy P, Gupta A, S K M V. Comparison of synergy patterns between the right and left hand while performing postures and object grasps. Sci Rep 2023; 13:20290. [PMID: 37985707 PMCID: PMC10662439 DOI: 10.1038/s41598-023-47620-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 11/16/2023] [Indexed: 11/22/2023] Open
Abstract
The human hand, with many degrees of freedom, serves as an excellent tool for dexterous manipulation. Previous research has demonstrated that there exists a lower-dimensional subspace that synergistically controls the full hand kinematics. The elements of this subspace, also called synergies, have been viewed as the strategy developed by the CNS in the control of finger movements. Considering that the control of fingers is lateralized to the contralateral hemisphere, how the synergies differ for the control of the dominant and the non-dominant hand has not been widely addressed. In this paper, hand kinematics was recorded using electromagnetic tracking system sensors as participants made various postures and object grasps with their dominant hand and non-dominant hand separately. Synergies that explain 90% of variance in data of both hands were analyzed for similarity at the individual level as well as at the population level. The results showed no differences in synergies between the hands at both these levels. PC scores and cross-reconstruction errors were analyzed to further support the prevalence of similarity between the synergies of the hands. Future work is proposed, and implications of the results to the treatment and diagnosis of neuromotor disorders are discussed.
Collapse
Affiliation(s)
- Prajwal Shenoy
- Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Anurag Gupta
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Varadhan S K M
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India.
| |
Collapse
|
2
|
Chu D, Sun B, Cai J, Zhang J, Ma J, Xiong C. Decomposition and Reconstruction of Human Palm Movements. IEEE Trans Biomed Eng 2023; 70:3093-3104. [PMID: 37192037 DOI: 10.1109/tbme.2023.3276079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
OBJECTIVE The human hand is known to have excellent manipulation ability compared to other primate hands. Without the palm movements, the human hand would lose more than 40% of its functions. However, uncovering the constitution of palm movements is still a challenging problem involving kinesiology, physiology, and engineering science. METHODS By recording the palm joint angles during common grasping, gesturing, and manipulation tasks, we built a palm kinematic dataset. Then, a method for extracting the eigen-movements to characterize the common motion correlation relationships of palm joints was proposed to explore the palm movement constitution. RESULTS This study revealed a palm kinematic characteristic that we named the joint motion grouping coupling characteristic. During natural palm movements, there are several joint groups with a high degree of motor independence, while the movements of joints within each joint group are interdependent. Based on these characteristics, the palm movements can be decomposed into seven eigen-movements. The linear combinations of these eigen-movements can reconstruct more than 90% of palm movement ability. Moreover, combined with the palm musculoskeletal structures, we found that the revealed eigen-movements are associated with joint groups that are defined by muscular functions, which provided a meaningful context for palm movement decomposition. CONCLUSION This paper suggests that some invariable characteristics underlie the variable palm motor behaviors and can be used to simplify palm movement generation. SIGNIFICANCE This paper provides important insights into palm kinematics, and helps facilitate motor function assessment and the development of better artificial hands.
Collapse
|
3
|
Zielinska T, Coba G. The Measure of Motion Similarity for Robotics Application. SENSORS (BASEL, SWITZERLAND) 2023; 23:1643. [PMID: 36772683 PMCID: PMC9919200 DOI: 10.3390/s23031643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/22/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
A new measure of motion similarity has been proposed. The formulation of this measure is presented and its logical basis is described. Unlike in most of other methods, the measure enables easy determination of the instantaneous synergies of the motion of body parts. To demonstrate how to use the measure, the data describing human movement is used. The movement is recorded using a professional motion capture system. Two different cases of non-periodic movements are discussed: stepping forward and backward, and returning to a stable posture after an unexpected thrust to the side (hands free or tied). This choice enables the identification of synergies in slow dynamics (stepping) and in fast dynamics (push recovery). The trajectories of motion similarity measures are obtained for point masses of the human body. The interpretation of these trajectories in relation to motion events is discussed. In addition, ordinary motion trajectories and footprints are shown in order to better illustrate the specificity of the discussed examples. The article ends with a discussion and conclusions.
Collapse
Affiliation(s)
- Teresa Zielinska
- Faculty of Power And Aeronautical Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland
| | | |
Collapse
|
4
|
Huang B, Chen W, Liang J, Cheng L, Xiong C. Characterization and Categorization of Various Human Lower Limb Movements Based on Kinematic Synergies. Front Bioeng Biotechnol 2022; 9:793746. [PMID: 35127668 PMCID: PMC8812690 DOI: 10.3389/fbioe.2021.793746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/20/2021] [Indexed: 11/23/2022] Open
Abstract
A proper movement categorization reduces the complexity of understanding or reproducing human movements in fields such as physiology, rehabilitation, and robotics, through partitioning a wide variety of human movements into representative sub-motion groups. However, how to establish a categorization (especially a quantitative categorization) for various human lower limb movements is rarely investigated in literature and remains challenging due to the diversity and complexity of the lower limb movements (diverse gait modes and interaction styles with the environment). Here we present a quantitative categorization for the various lower limb movements. To this end, a similarity measure between movements was first built based on limb kinematic synergies that provide a unified and physiologically meaningful framework for evaluating the similarities among different types of movements. Then, a categorization was established via hierarchical cluster analysis for thirty-four lower limb movements, including walking, running, hopping, sitting-down-standing-up, and turning in different environmental conditions. According to the movement similarities, the various movements could be divided into three distinct clusters (cluster 1: walking, running, and sitting-down-standing-up; cluster 2: hopping; cluster 3: turning). In each cluster, cluster-specific movement synergies were required. Besides the uniqueness of each cluster, similarities were also found among part of the synergies employed by these different clusters, perhaps related to common behavioral goals in these clusters. The mix of synergies shared across the clusters and synergies for specific clusters thus suggests the coexistence of the conservation and augmentation of the kinematic synergies underlying the construction of the diverse and complex motor behaviors. Overall, the categorization presented here yields a quantitative and hierarchical representation of the various lower limb movements, which can serve as a basis for the understanding of the formation mechanisms of human locomotion and motor function assessment and reproduction in related fields.
Collapse
Affiliation(s)
| | | | | | | | - Caihua Xiong
- *Correspondence: Jiejunyi Liang, ; Caihua Xiong,
| |
Collapse
|
5
|
Liu B, Jiang L, Fan S. Reducing Anthropomorphic Hand Degrees of Actuation with Grasp-Function-Dependent and Joint-Element-Sparse Hand Synergies. INT J HUM ROBOT 2022. [DOI: 10.1142/s0219843621500171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, a set of grasp-function-dependent and joint-element-sparse hand synergies was proposed. First, hand synergies were extracted from five basic categories of movements by principal component analysis (PCA). Then, varimax rotation was applied on these synergies, so each sparse synergy only represented a limited number of joints. Next, according to the contribution to these sparse synergies, finger joints were clustered into different joint modules. Finally, integrating the joint modules in different categories of hand movements, the minimum number of actuators and joint synergic modules for anthropomorphic hands were determined. The results showed that using 5 groups of joint modules and 7–9 actuators we can achieve the best performance of grasp function and motion flexibility. Furthermore, through the reasonable design of adaptive and hyperextension functional joint modules, anthropomorphic hands can better meet the requirements of different tasks like power grasping and precision pinching. Comparing with traditional finger-based actuation strategy, the joint coupling scheme achieved better anthropomorphic performance and larger workspace. These above findings will benefit the development of mechanical structure design and control method of anthropomorphic hands.
Collapse
Affiliation(s)
- Bingchen Liu
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology (HIT), Harbin 150001, P. R. China
| | - Li Jiang
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology (HIT), Harbin 150001, P. R. China
| | - Shaowei Fan
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology (HIT), Harbin 150001, P. R. China
| |
Collapse
|
6
|
Sato Y, Kondo T, Shibata R, Nakamura M, Okano H, Ushiba J. Functional reorganization of locomotor kinematic synergies reflects the neuropathology in a mouse model of spinal cord injury. Neurosci Res 2021; 177:78-84. [PMID: 34921835 DOI: 10.1016/j.neures.2021.12.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 12/14/2021] [Accepted: 12/14/2021] [Indexed: 11/27/2022]
Abstract
Spinal cord injury (SCI) disrupts motor commands to modular structures of the spinal cord, limiting the ability to walk. Evidence suggests that these modules are conserved across species from rodent to human and subserve adaptive walking by controlling coordinated joint movements (kinematic synergies). Since SCI causes uncoordinated joint movements of the lower limbs during walking, there may be a disorder of the modular structures that control them. To gain insights into this complex process, we recorded the kinematics of intact and SCI mice when walking on a treadmill and applied principal component analysis to extract kinematic synergies. Most SCI mice walked stably on the treadmill, but their kinematic synergies were generally different from those of intact mice. We classified the kinematic synergies of SCI mice into three groups based on the similarity of the extracted first three synergy components. We found that these three groups had different degrees of spinal cord damage. This suggests that differences in kinematic synergies reflect underlying SCI neuropathology. These results may help guide the development of different rehabilitation approaches and future physiological experiments to understand the mechanisms of motor control and recovery.
Collapse
Affiliation(s)
- Yuta Sato
- Graduate School of Science and Technology, Keio University, Kanagawa, Japan; Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science, Saitama, Japan
| | - Takahiro Kondo
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Reo Shibata
- Department of Orhopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Masaya Nakamura
- Department of Orhopaedic Surgery, Keio University School of Medicine, Tokyo, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan.
| |
Collapse
|
7
|
Gracia-Ibáñez V, Rodríguez-Cervantes PJ, Bayarri-Porcar V, Granell P, Vergara M, Sancho-Bru JL. Using Sensorized Gloves and Dimensional Reduction for Hand Function Assessment of Patients with Osteoarthritis. SENSORS 2021; 21:s21237897. [PMID: 34883898 PMCID: PMC8659816 DOI: 10.3390/s21237897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/12/2021] [Accepted: 11/24/2021] [Indexed: 11/16/2022]
Abstract
Sensorized gloves allow the measurement of all hand kinematics that are essential for daily functionality. However, they are scarcely used by clinicians, mainly because of the difficulty of analyzing all joint angles simultaneously. This study aims to render this analysis easier in order to enable the applicability of the early detection of hand osteoarthritis (HOA) and the identification of indicators of dysfunction. Dimensional reduction was used to compare kinematics (16 angles) of HOA patients and healthy subjects while performing the tasks of the Sollerman hand function test (SHFT). Five synergies were identified by using principal component (PC) analyses, patients using less fingers arch, higher palm arching, and a more independent thumb abduction. The healthy PCs, explaining 70% of patients’ data variance, were used to transform the set of angles of both samples into five reduced variables (RVs): fingers arch, hand closure, thumb-index pinch, forced thumb opposition, and palmar arching. Significant differences between samples were identified in the ranges of movement of most of the RVs and in the median values of hand closure and thumb opposition. A discriminant function for the detection of HOA, based in RVs, is provided, with a success rate of detection higher than that of the SHFT. The temporal profiles of the RVs in two tasks were also compared, showing their potentiality as dysfunction indicators. Finally, reducing the number of sensors to only one sensor per synergy was explored through a linear regression, resulting in a mean error of 7.0°.
Collapse
Affiliation(s)
- Verónica Gracia-Ibáñez
- Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Spain; (P.-J.R.-C.); (V.B.-P.); (M.V.); (J.-L.S.-B.)
- Correspondence:
| | - Pablo-Jesús Rodríguez-Cervantes
- Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Spain; (P.-J.R.-C.); (V.B.-P.); (M.V.); (J.-L.S.-B.)
| | - Vicente Bayarri-Porcar
- Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Spain; (P.-J.R.-C.); (V.B.-P.); (M.V.); (J.-L.S.-B.)
| | - Pablo Granell
- Consorci Hospitalari Provincial de Castelló, Av. del Dr. Clarà, 19, 12002 Castelló de la Plana, Spain;
| | - Margarita Vergara
- Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Spain; (P.-J.R.-C.); (V.B.-P.); (M.V.); (J.-L.S.-B.)
| | - Joaquín-Luis Sancho-Bru
- Department of Mechanical Engineering and Construction, Universitat Jaume I, 12071 Castelló de la Plana, Spain; (P.-J.R.-C.); (V.B.-P.); (M.V.); (J.-L.S.-B.)
| |
Collapse
|
8
|
Zhao K, Zhang Z, Wen H, Scano A. Intra-Subject and Inter-Subject Movement Variability Quantified with Muscle Synergies in Upper-Limb Reaching Movements. Biomimetics (Basel) 2021; 6:63. [PMID: 34698082 PMCID: PMC8544238 DOI: 10.3390/biomimetics6040063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 11/16/2022] Open
Abstract
Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.
Collapse
Affiliation(s)
- Kunkun Zhao
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China; (Z.Z.); (H.W.)
| | - Zhisheng Zhang
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China; (Z.Z.); (H.W.)
| | - Haiying Wen
- School of Mechanical Engineering, Southeast University, Nanjing 211189, China; (Z.Z.); (H.W.)
| | - Alessandro Scano
- UOS STIIMA Lecco—Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), Via Previati 1/E, 23900 Lecco, Italy
| |
Collapse
|
9
|
Jarque-Bou NJ, Sancho-Bru JL, Vergara M. A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues. SENSORS 2021; 21:s21093035. [PMID: 33925928 PMCID: PMC8123433 DOI: 10.3390/s21093035] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 11/16/2022]
Abstract
The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models.
Collapse
|
10
|
Alicea R, Xiloyannis M, Chiaradia D, Barsotti M, Frisoli A, Masia L. A soft, synergy-based robotic glove for grasping assistance. WEARABLE TECHNOLOGIES 2021; 2:e4. [PMID: 38486631 PMCID: PMC10936321 DOI: 10.1017/wtc.2021.3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 01/18/2021] [Accepted: 02/18/2021] [Indexed: 03/17/2024]
Abstract
This paper presents a soft, tendon-driven, robotic glove designed to augment grasp capability and provide rehabilitation assistance for postspinal cord injury patients. The basis of the design is an underactuation approach utilizing postural synergies of the hand to support a large variety of grasps with a single actuator. The glove is lightweight, easy to don, and generates sufficient hand closing force to assist with activities of daily living. Device efficiency was examined through a characterization of the power transmission elements, and output force production was observed to be linear in both cylindrical and pinch grasp configurations. We further show that, as a result of the synergy-inspired actuation strategy, the glove only slightly alters the distribution of forces across the fingers, compared to a natural, unassisted grasping pattern. Finally, a preliminary case study was conducted using a participant suffering from an incomplete spinal cord injury (C7). It was found that through the use of the glove, the participant was able to achieve a 50% performance improvement (from four to six blocks) in a standard Box and Block test.
Collapse
Affiliation(s)
- Ryan Alicea
- Assistive Robotics and Interactive ExoSuits (ARIES) Lab, Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
| | - Michele Xiloyannis
- Sensory-Motor Systems (SMS) Lab, Institute of Robotics and Intelligent Systems (IRIS), ETH Zurich, Zurich, Switzerland
- The Spinal Cord Injury Center, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
| | - Domenico Chiaradia
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Michele Barsotti
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Antonio Frisoli
- Perceptual Robotics (PERCRO) Laboratory, TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Lorenzo Masia
- Assistive Robotics and Interactive ExoSuits (ARIES) Lab, Institute for Computer Engineering (ZITI), Heidelberg University, Heidelberg, Germany
| |
Collapse
|
11
|
Huang B, Xiong C, Chen W, Liang J, Sun BY, Gong X. Common kinematic synergies of various human locomotor behaviours. ROYAL SOCIETY OPEN SCIENCE 2021; 8:210161. [PMID: 33996133 PMCID: PMC8059590 DOI: 10.1098/rsos.210161] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/23/2021] [Indexed: 06/12/2023]
Abstract
Humans show a variety of locomotor behaviours in daily living, varying in locomotor modes and interaction styles with the external environment. However, how this excellent motor ability is formed, whether there are some invariants underlying various locomotor behaviours and simplifying their generation, and what factors contribute to the invariants remain unclear. Here, we find three common kinematic synergies that form the six joint motions of one lower limb during walking, running, hopping and sitting-down-standing-up (movement variance accounted for greater than 90%), through identifying the coordination characteristics of 36 lower limb motor tasks in diverse environments. This finding supports the notion that humans simplify the generation of various motor behaviours through re-using several basic motor modules, rather than developing entirely new modules for each behaviour. Moreover, a potential link is also found between these synergies and the unique biomechanical characteristics of the human musculoskeletal system (muscular-articular connective architecture and bone shape), and the patterns of inter-joint coordination are consistent with the energy-saving mechanism in locomotion by using biarticular muscles as efficient mechanical energy transducers between joints. Altogether, our work helps understand the formation mechanisms of human locomotion from a holistic viewpoint and evokes inspirations for the development of artificial limbs imitating human motor ability.
Collapse
Affiliation(s)
- Bo Huang
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| | - Caihua Xiong
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| | - Wenbin Chen
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| | - Jiejunyi Liang
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| | - Bai-Yang Sun
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| | - Xuan Gong
- Institute of Robotics Research (IR2), State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, People's Republic of China
| |
Collapse
|
12
|
Jarque-Bou NJ, Sancho-Bru JL, Vergara M. Synergy-Based Sensor Reduction for Recording the Whole Hand Kinematics. SENSORS 2021; 21:s21041049. [PMID: 33557063 PMCID: PMC7913855 DOI: 10.3390/s21041049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 12/02/2022]
Abstract
Simultaneous measurement of the kinematics of all hand segments is cumbersome due to sensor placement constraints, occlusions, and environmental disturbances. The aim of this study is to reduce the number of sensors required by using kinematic synergies, which are considered the basic building blocks underlying hand motions. Synergies were identified from the public KIN-MUS UJI database (22 subjects, 26 representative daily activities). Ten synergies per subject were extracted as the principal components explaining at least 95% of the total variance of the angles recorded across all tasks. The 220 resulting synergies were clustered, and candidate angles for estimating the remaining angles were obtained from these groups. Different combinations of candidates were tested and the one providing the lowest error was selected, its goodness being evaluated against kinematic data from another dataset (KINE-ADL BE-UJI). Consequently, the original 16 joint angles were reduced to eight: carpometacarpal flexion and abduction of thumb, metacarpophalangeal and interphalangeal flexion of thumb, proximal interphalangeal flexion of index and ring fingers, metacarpophalangeal flexion of ring finger, and palmar arch. Average estimation errors across joints were below 10% of the range of motion of each joint angle for all the activities. Across activities, errors ranged between 3.1% and 16.8%.
Collapse
|