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1. Ru(II)-diimine complexes and cytochrome P450 working hand-in-hand

   https://doi.org/10.1016/j.jinorgbio.2020.111254  

   Eidenschenk, E Cheruzel (September 2020, Journal of inorganic biochemistry)

   null (Ed.)
2. First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset.

   Sangpil Kim, Hyung-Gun Chi (September 2020, BMVC 2020)

   null (Ed.)

   First-person-view videos of hands interacting with tools are widely used in the computer vision industry. However, creating a dataset with pixel-wise segmentation of hands is challenging since most videos are captured with fingertips occluded by the hand dorsum and grasped tools. Current methods often rely on manually segmenting hands to create annotations, which is inefficient and costly. To relieve this challenge, we create a method that utilizes thermal information of hands for efficient pixel-wise hand segmentation to create a multi-modal activity video dataset. Our method is not affected by fingertip and joint occlusions and does not require hand pose ground truth. We show our method to be 24 times faster than the traditional polygon labeling method while maintaining high quality. With the segmentation method, we propose a multi-modal hand activity video dataset with 790 sequences and 401,765 frames of "hands using tools" videos captured by thermal and RGB-D cameras with hand segmentation data. We analyze multiple models for hand segmentation performance and benchmark four segmentation networks. We show that our multi-modal dataset with fusing Long-Wave InfraRed (LWIR) and RGB-D frames achieves 5% better hand IoU performance than using RGB frames. 

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3. Hand–object configuration estimation using particle filters for dexterous in-hand manipulation

   https://doi.org/10.1177/0278364919883343  

   Hang, Kaiyu; Bircher, Walter_G; Morgan, Andrew_S; Dollar, Aaron_M (October 2019, The International Journal of Robotics Research)

   We consider the problem of in-hand dexterous manipulation with a focus on unknown or uncertain hand–object parameters, such as hand configuration, object pose within hand, and contact positions. In particular, in this work we formulate a generic framework for hand–object configuration estimation using underactuated hands as an example. Owing to the passive reconfigurability and the lack of encoders in the hand’s joints, it is challenging to estimate, plan, and actively control underactuated manipulation. By modeling the grasp constraints, we present a particle filter-based framework to estimate the hand configuration. Specifically, given an arbitrary grasp, we start by sampling a set of hand configuration hypotheses and then randomly manipulate the object within the hand. While observing the object’s movements as evidence using an external camera, which is not necessarily calibrated with the hand frame, our estimator calculates the likelihood of each hypothesis to iteratively estimate the hand configuration. Once converged, the estimator is used to track the hand configuration in real time for future manipulations. Thereafter, we develop an algorithm to precisely plan and control the underactuated manipulation to move the grasped object to desired poses. In contrast to most other dexterous manipulation approaches, our framework does not require any tactile sensing or joint encoders, and can directly operate on any novel objects, without requiring a model of the object a priori. We implemented our framework on both the Yale Model O hand and the Yale T42 hand. The results show that the estimation is accurate for different objects, and that the framework can be easily adapted across different underactuated hand models. In the end, we evaluated our planning and control algorithm with handwriting tasks, and demonstrated the effectiveness of the proposed framework. 

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4. Benchmarking In-Hand Manipulation

   https://doi.org/10.1109/LRA.2020.2964160  

   Cruciani, Silvia; Sundaralingam, Balakumar; Hang, Kaiyu; Kumar, Vikash; Hermans, Tucker; Kragic, Danica (April 2020, IEEE Robotics and Automation Letters)
5. Synergistic Activation Patterns of Hand Muscles in Left-and Right-Hand Dominant Individuals

   https://doi.org/10.2478/hukin-2021-0002  

   Liang, Don; Yarossi, Mathew; Jacobs-Skolik, Spencer Lake; Furmanek, Mariusz P.; Brooks, Dana; Erdogmus, Deniz; Tunik, Eugene (January 2021, Journal of Human Kinetics)

   null (Ed.)

   Abstract Handedness has been associated with behavioral asymmetries between limbs that suggest specialized function of dominant and non-dominant hand. Whether patterns of muscle co-activation, representing muscle synergies, also differ between the limbs remains an open question. Previous investigations of proximal upper limb muscle synergies have reported little evidence of limb asymmetry; however, whether the same is true of the distal upper limb and hand remains unknown. This study compared forearm and hand muscle synergies between the dominant and non-dominant limb of left-handed and right-handed participants. Participants formed their hands into the postures of the American Sign Language (ASL) alphabet, while EMG was recorded from hand and forearm muscles. Muscle synergies were extracted for each limb individually by applying non-negative-matrix-factorization (NMF). Extracted synergies were compared between limbs for each individual, and between individuals to assess within and across participant differences. Results indicate no difference between the limbs for individuals, but differences in limb synergies at the population level. Left limb synergies were found to be more similar than right limb synergies across left- and right-handed individuals. Synergies of the left hand of left dominant individuals were found to have greater population level similarity than the other limbs tested. Results are interpreted with respect to known differences in the neuroanatomy and neurophysiology of proximal and distal upper limb motor control. Implications for skill training in sports requiring dexterous control of the hand are discussed. 

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