Dimensionality Reduction in Control and Coordination of the Human Hand

Vinjamuri, Ramana; Sun, Mingui; Chang, Cheng-Chun; Lee, Heung-No; Sclabassi, R.J.; Mao, Zhi-Hong

doi:10.1109/tbme.2009.2032532

Cited by 67 publications

(88 citation statements)

References 28 publications

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“…Also shown in the figure are the hand postures of the reconstructed movement across time. As these synergies preserve both the temporal structure and the postural information these are termed as temporalposturalsynergies ( Vinjamuri et al (2010a;). …”

Section: What Are Temporal Postural Synergies?mentioning

confidence: 99%

“…In this case, PCs are the synergies which are most commonly used across different movements. Moreover these PCs when graphically visualized revealed anatomical implications of physiological properties of human hand prehension ( Vinjamuri et al (2010a;). …”

Section: Principal Component Analysismentioning

confidence: 99%

“…l 1 -norm minimization was used to reconstruct natural and ASL movements from the extracted synergies. This method with illustrations was already presented in Vinjamuri et al (2010a). We have included a brief explanation here for readability and for the sake of completeness.…”

Section: Reconstruction Of Natural and Asl Movementsmentioning

confidence: 99%

“…We have systematically investigated the use of linear dimensionality reduction methods in extracting movement primitives or synergies in hand movements in Vinjamuri et al (2010a;2011). In this chapter, we applied linear (PCA and Multidimensional Scaling (MDS)) and nonlinear (kPCA, Isomap, LLE) dimensionality reduction methods in extracting kinematic synergies in grasping tasks of the human hand.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Application of Linear and Nonlinear Dimensionality Reduction Methods

Vinjamuri¹,

WeiWang²,

Sun³

et al. 2012

Principal Component Analysis

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Section: What Are Temporal Postural Synergies?mentioning

confidence: 99%

Section: Principal Component Analysismentioning

confidence: 99%

Section: Reconstruction Of Natural and Asl Movementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Linear and Nonlinear Dimensionality Reduction Methods

Vinjamuri¹,

WeiWang²,

Sun³

et al. 2012

Principal Component Analysis

Self Cite

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“…Explicit representations for dynamic shape manifold were studied on moving humans to recognize actions using low-dimensional embedding by locality preserving projections (LPP) [5]. Mao proposed the concept of kinematic synergies to address the dimensionality reduction problem for hand movement control and coordination [6]. Optimization technique was applied to find the weights and firing timings of extracted synergies, decomposing basic motions.…”

Section: Introductionmentioning

confidence: 99%

Representation and Symbolization of Motion Captured Human Action by Locality Preserving Projections

Lee¹,

Heo²,

Lee³

2014

Appl. Math. Inf. Sci.

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Abstract:Human motion analysis and assessment are important in determining Parkinsons disease and stroke, or in measuring skill quality in basic motions. Reduced space is useful in representing motion segments and finding basic behavioral patterns for humanoid robot control using the modularized approach. In the current paper, we represent motion-captured data of human action in a reduced space of nonlinear degrees of freedom in which the original motion is characterized. First, we represent high-dimensional data, such as motion sequence of the position of joints in Cartesian space, in a reduced space using the locality preserving projection (LPP) method. Second, we find a similarity measure between the actions. Finally, we assess human motions using a similarity measure to find the most similar one. The LPP is a linear dimensionality reduction algorithm that builds a graph for neighborhood information and maps data points to a reduced space. The reason for using LPP in our study is that it is defined globally, and any new data element can be mapped in the reduced space. Our method includes the generation of symbolic code sequence corresponding to complex, high-dimensional motion. Interdisciplinary synergy combined with information technology and wearable sensor systems can broaden the possible future applications in rehabilitation engineering.

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Low‐Dimensional Embeddings for Interaction Design

Rusu

Schött

Williamson

et al. 2021

Advanced Intelligent Systems

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From the coffee mug we put to our mouths, to our fingers finding their way to the correct letters on our keyboard, hand movements are ever-present. Using data gloves, optical tracking, or accelerometers, a lot of information about these habitual interactions can be captured, showing that, as well as dominating everyday life, they can also be quite complex. [4] Designing for more interactive objects, with hand gestures acting as one of the main controls, is an area of increasing research and commercial interest. While the degrees of freedom (DOF) the human hand possesses allow for "flexibility to perform skilled finger movements" [5] in real life, they pose technological challenges for digital interfaces, as most typical interaction design processes are not yet well adapted to high-dimensional input. [6] The goal when prototyping such controls is to find gestures that are easy for the user to recall and perform reliably, and easy for the computer to distinguish from one another. However, gestures possess a high degree of complexity and human movement possesses a variability [7] that needs to be known and incorporated into the design process. Confronting designers with the raw high-dimensional data as input and asking them to design robust, usable interactive systems has proven to be challenging. This has limited the impact of novel sensing systems that capturing more of the complexity of real-life interactions. Such systems' datasets possess higher dimensionalities, which makes them more challenging for designers to understand and utilize. Lu et al., for example, are developing a system that can capture the full 25 dimensions hand movement possesses with the help of multiple inertial sensors. [8] Further, large amounts of high-dimensional data can be generated from the deployment of multiple sensors across physical objects, as is the case for the WebBike Project, where the researchers equipped e-bikes with sensors. [9] The highly complex datasets created by those novel sensing systems require a layer of simplification to become comprehensible for researchers and designers and to proactively apply design processes appropriately. We explore the role of lowdimensional embeddings in conceptualizing and visualizing high-dimensional movement data by answering the following research question.Can Autoencoder-Based Dimensionality Reduction Simplify the Data-Driven Design Process?Our findings suggest that the low-dimensional representation of complex movement data is more convenient to work with than high-dimensional data, which builds a basis for analyzing and visualizing hand movements and interactions among team members during the design process. Further, we define

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Dimensionality Reduction in Control and Coordination of the Human Hand

Cited by 67 publications

References 28 publications

Application of Linear and Nonlinear Dimensionality Reduction Methods

Application of Linear and Nonlinear Dimensionality Reduction Methods

Representation and Symbolization of Motion Captured Human Action by Locality Preserving Projections

Low‐Dimensional Embeddings for Interaction Design

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