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1. Finger stability in precision grips

   https://doi.org/10.1073/pnas.2122903119  

   Sharma, Neelima; Venkadesan, Madhusudhan (March 2022, Proceedings of the National Academy of Sciences)

   Stable precision grips using the fingertips are a cornerstone of human hand dexterity. However, our fingers become unstable sometimes and snap into a hyperextended posture. This is because multilink mechanisms like our fingers can buckle under tip forces. Suppressing this instability is crucial for hand dexterity, but how the neuromuscular system does so is unknown. Here we show that people rely on the stiffness from muscle contraction for finger stability. We measured buckling time constants of 50 ms or less during maximal force application with the index finger—quicker than feedback latencies—which suggests that muscle-induced stiffness may underlie stability. However, a biomechanical model of the finger predicts that muscle-induced stiffness cannot stabilize at maximal force unless we add springs to stiffen the joints or people reduce their force to enable cocontraction. We tested this prediction in 38 volunteers. Upon adding stiffness, maximal force increased by 34 ± 3%, and muscle electromyography readings were 21 ± 3% higher for the finger flexors (mean ± SE). Muscle recordings and mathematical modeling show that adding stiffness offloads the demand for muscle cocontraction, thus freeing up muscle capacity for fingertip force. Hence, people refrain from applying truly maximal force unless an external stabilizing stiffness allows their muscles to apply higher force without losing stability. But more stiffness is not always better. Stiff fingers would affect the ability to adapt passively to complex object geometries and precisely regulate force. Thus, our results show how hand function arises from neurally tuned muscle stiffness that balances finger stability with compliance. 

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2. Acoustic Energy Release During the Laboratory Seismic Cycle: Insights on Laboratory Earthquake Precursors and Prediction

   https://doi.org/10.1029/2019JB018975  

   Bolton, David C.; Shreedharan, Srisharan; Rivière, Jacques; Marone, Chris (August 2020, Journal of Geophysical Research: Solid Earth)

   Abstract Machine learning can predict the timing and magnitude of laboratory earthquakes using statistics of acoustic emissions. The evolution of acoustic energy is critical for lab earthquake prediction; however, the connections between acoustic energy and fault zone processes leading to failure are poorly understood. Here, we document in detail the temporal evolution of acoustic energy during the laboratory seismic cycle. We report on friction experiments for a range of shearing velocities, normal stresses, and granular particle sizes. Acoustic emission data are recorded continuously throughout shear using broadband piezo‐ceramic sensors. The coseismic acoustic energy release scales directly with stress drop and is consistent with concepts of frictional contact mechanics and time‐dependent fault healing. Experiments conducted with larger grains (10.5 μm) show that the temporal evolution of acoustic energy scales directly with fault slip rate. In particular, the acoustic energy is low when the fault is locked and increases to a maximum during coseismic failure. Data from traditional slide‐hold‐slide friction tests confirm that acoustic energy release is closely linked to fault slip rate. Furthermore, variations in the true contact area of fault zone particles play a key role in the generation of acoustic energy. Our data show that acoustic radiation is related primarily to breaking/sliding of frictional contact junctions, which suggests that machine learning‐based laboratory earthquake prediction derives from frictional weakening processes that begin very early in the seismic cycle and well before macroscopic failure. 

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3. Dexterous Manipulation by Two Fingers with Coupled Joints

   Jia, Yan-Bin; Xue, Yuechuan (May 2018, IEEE International Conference on Robotics and Automation)

   This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation. 

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4. Dexterous Manipulation by Two Fingers with Coupled Joints

   Jia, Yan-Bin; Xue, Yuechuan (May 2018, IEEE International Conference on Robotics and Automation)

   This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation. 

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5. Dexterous manipulation by two fingers with coupled joints

   Jia, Yan-Bin; Xue, Yuechuan. (May 2018, IEEE International Conference on Robotics and Automation)

   This paper studies dexterous manipulation in the plane by a two-fingered hand in the plane. The dynamics of each finger, which consists of two links with coupled joints, are derived based on Lagrangian mechanics. As an object is being manipulated, its orientation and the two independent joint angles of the hand constitute the state of the entire system. Contact kinematics, accounting for both stick and slip modes, are combined with dynamics to establish a dependence of the object's linear and angular accelerations on joint accelerations. This allows control of joint torques, under a proportional-derivative (PD) law, to move the object to a target position in a desired orientation. 

   more » « less