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Sensorized grip device used to measure multi-digit forces and torques in 3 dimensions. From: Zhang et al. (2012). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c3f9a510ebbe886df685ecc/1547672191791/1.jpg Sensorized Grip Devices Figure 3. Sensorized grip device used to measure grip and load forces exerted by a soft-synergy myoelectric prosthetic hand (SoftHand Pro). From: Gailey et al. (2017). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c3f97124ae237be5ed91f06/1547671915688/1.jpg Sensorized Grip Devices Figure 1. Sensorized grip device used to measure forces and torques in 3 dimensions, and computation of fingertip center of pressure (precision grip). From: Fu et al. (2010). https://www.neural-control.org/haptics daily 0.75 2019-01-31 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c40d80b562fa72bb0827f33/1547753539452/1.jpg Haptics Figure 6. Electronics and mechanics of the 2-DoF tactile actuators described in Chinello et al., 2018 (under review). A: linear gear. B: stand for tracking. C: normal motor. D: finger stand. E: lateral motor. F: lateral platform and force sensor. https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c40cff3aa4a99ab3c040b68/1547751444008/1.jpg Haptics Figure 5. Devices used to render haptic feedback associated with grasping an object. Phantom haptic devices were used to provide non-tactile feedback in response to forces generated by the subject. The wearable haptic devices were used to provide tactile inputs to the finger pads matching the skin deformation associated with exertion of fingertip forces. From: Toma et al. (2019). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c40cf234ae237ef69e1c040/1547751306021/1.jpg Haptics Figure 4. Haptic and visual rendering of dexterous manipulation using Phantom devices. From: Fu and Santello (2014). https://www.neural-control.org/home-1 daily 1.0 2019-01-31 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48cdab1ae6cfa56b538a48/1548275121157/Overview.jpg Home https://www.neural-control.org/posters daily 0.75 2019-01-23 https://www.neural-control.org/hand-prosthetics daily 0.75 2020-05-09 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c40db307924e87657725353/1547754305404/1.jpg Hand Prosthetics Figure 8. “Standard” single-gain controller (top) and hybrid gain controller (bottom) of the SHP. The hybrid gain controller uses two gains: a high gain is used during SHP free-motion and release, whereas the low gain is used following contact with the object. From: Fu and Santello (2018). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c40dad72b6a284ab4e6b470/1547754204546/1.jpg Hand Prosthetics Figure 7. Testing reproducibility of SHP opening-closing. Movement cycles are driven by a computer. From: Fani et al. (2016). https://www.neural-control.org/muscle-activity daily 0.75 2020-10-19 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c42be89b914435393a23bdf/1588985400264/1.jpg Muscle Activity Figure 3. Experimental set-up and data analysis to quantify the amount of perceptual variance explained by the across trials modulation of few patterns of muscle synergy. From: d’Avella et al. (2003). https://www.neural-control.org/digit-forces daily 0.75 2020-10-19 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c42bbe288251ba07951af10/1547877353994/1.jpg Digit Forces Figure 2. Experimental design and set-up employed to quantify the contribution of tactile and non-tactile input of force for the estimation of finger relative position. From: Toma et al. (2019). https://www.neural-control.org/interaction-between-visual-feedback daily 0.75 2020-10-19 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c42b9404d7a9ccfd0b4e4f9/1547876723362/1.jpg Interaction Between Visual Feedback Figure 1. Experimental set-up employed to quantify the role of vision of hand-held object movement for grip force control. From: Toma & Santello (2017). https://www.neural-control.org/control-of-hand-shape daily 0.75 2020-05-09 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48d8ffbba223630daf5cba/1548278020824/1.jpg Control of Hand Shape Figure 4. Grip devices used to study grasping at constrained and unconstrained contacts. The output of the 6D force/torque sensors was used to compute center of pressure of thumb and index fingertip. From: Fu, Zhang and Santello (1998). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48d64c4d7a9ca636399baf/1548277333131/3.jpg Control of Hand Shape Figure 3. Left: Multi-digit contact points distribution as a function of center of mass and its predictability. Right: time course of object roll (A) and peak object roll (B) as a function of center of mass and its predictability. From: Lukos, Ansuini and Santello (2007). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48d88b758d46280becbeae/1548277916111/ Control of Hand Shape Figure 5. Trial-to-trial covariation of digit load force distribution (dLF) with vertical distance between digit center of pressure (CoP). The three centers of mass (left, center, and right) are color coded (red, black, and blue, respectively). From: Fu, Zhang and Santello (1998). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48d57721c67c9156323d77/1548277125571/1.jpg Control of Hand Shape Figure 2. Left: Grip device used to measure multi-digit normal and tangential (load) forces. Right: Distribution of phase angles as a function of frequency of normal forces exerted by all digit pairs. From: Santello and Soechting (2000). https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c48cb72352f534aa63e0d0f/1548274557721/2.jpg Control of Hand Shape Figure 1. Left: Covariation of finger joint angle pairs recorded across 57 hand shapes used to grasp imagined objects. Right: First two principal components of hand postures. From: Santello, Flanders and Soechting (1998). https://www.neural-control.org/human-machine daily 0.75 2020-05-09 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c52b653352f53c1b855f517/1548924511917/1.jpg Human-Machine Figure 6. Top: Control (bimanual) and experimental groups (dyads) used for the study of joint manipulation. Bottom: Performance (object roll) of each dyad (y-axis) as a function of relative performance of each subject in the dyad plotted for all subject groups (a) and separately with data from each subject group (b-e). From: Mojtahedi, Fu and Santello (2017). https://www.neural-control.org/sensorimotor-learning daily 0.75 2020-05-09 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5c525a53b8a045df0919ab1d/1548900976215/1.jpg Sensorimotor Learning Figure 5. Left: Experimental protocol and sequence of grasp contexts (left or right handle of the U-shaped object). Right: Compensatory torque (Tcom) and peak object roll across trials from each grasp context. From: Fu and Santello (2012). https://www.neural-control.org/grasp-context daily 0.75 2020-05-11 https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5eb5fbd82ffa577e022ced74/1588985695519/Grasp+Context+-+Fig.+1.jpg Grasp Context Figure 1. Effect of cTBS on digit load force, grip force, and position across all experimental conditions. (A) From top to bottom, traces denote time course of the difference between thumb and index finger load force, grip force averaged across thumb and index finger, and the vertical distance between thumb and index finger center of pressure (d y ) from contact (“0”) to object lift onset. Data are averages of the last 5 trials prior to cTBS (Pre5) and first trial following cTBS (Post1). d Y data are plotted from the time at which they can be accurately estimated using force and torque sensors (Fu et al. 2010) . Data from each experimental group are shown across columns. Shaded plots denote Tcom variables that were significantly affected by cTBS. (B) Data from Pre5 and each post-cTBS trial are shown for each Tcom variable and experimental group. ** denotes P < 0.0125. Data are averages (± SE) of all subjects. https://static1.squarespace.com/static/5b89aac22714e5d79c0b7785/t/5eb5fc3d75c2e1070ed932d2/1588985020562/Grasp+Context+-+Fig.+2.jpg Grasp Context Figure 2. Cortical sensorimotor mechanisms for neural control of dexterous manipulation. Prior to object contact, interactions between M1, sensory, as well as premotor and parietal cortical areas, lead to hand shaping and positioning the digits at remembered locations used in previous manipulations. Somatosensory and visual inputs contribute to guiding the hand towards the planned contact points on the object. Following contact, the roles of M1 and S1 for the control of dexterous manipulation differ according to whether contact points are constrained or unconstrained. https://www.neural-control.org/news daily 0.75 2020-10-19