Computation on Demand: Action-Specific Representations of Visual Task Features Arise during Distinct Movement Phases.

The intention to act influences the computations of various task-relevant features. However, little is known about the time course of these computations. Furthermore, it is commonly held that these computations are governed by conjunctive neural representations of the features. But, support for this view comes from paradigms arbitrarily combining task features and affordances, thus requiring representations in working memory. Therefore, the present study used electroencephalography and a well-rehearsed task with features that afford minimal working memory representations to investigate the temporal evolution of feature representations and their potential integration in the brain. Female and male human participants grasped objects or touched them with a knuckle. Objects had different shapes and were made of heavy or light materials with shape and weight being relevant for grasping, not for "knuckling." Using multivariate analysis showed that representations of object shape were similar for grasping and knuckling. However, only for grasping did early shape representations reactivate at later phases of grasp planning, suggesting that sensorimotor control signals feed back to the early visual cortex. Grasp-specific representations of material/weight only arose during grasp execution after object contact during the load phase. A trend for integrated representations of shape and material also became grasp-specific but only briefly during the movement onset. These results suggest that the brain generates action-specific representations of relevant features as required for the different subcomponents of its action computations. Our results argue against the view that goal-directed actions inevitably join all features of a task into a sustained and unified neural representation.

Phase-Dependent Visual and Sensorimotor Integration of Features for Grasp Computations before and after Effector Specification.

The simple act of viewing and grasping an object involves complex sensorimotor control mechanisms that have been shown to vary as a function of multiple object and other task features such as object size, shape, weight, and wrist orientation. However, these features have been mostly studied in isolation. In contrast, given the nonlinearity of motor control, its computations require multiple features to be incorporated concurrently. Therefore, the present study tested the hypothesis that grasp computations integrate multiple task features superadditively in particular when these features are relevant for the same action phase. We asked male and female human participants to reach-to-grasp objects of different shapes and sizes with different wrist orientations. Also, we delayed the movement onset using auditory signals to specify which effector to use. Using electroencephalography and representative dissimilarity analysis to map the time course of cortical activity, we found that grasp computations formed superadditive integrated representations of grasp features during different planning phases of grasping. Shape-by-size representations and size-by-orientation representations occurred before and after effector specification, respectively, and could not be explained by single-feature models. These observations are consistent with the brain performing different preparatory, phase-specific computations; visual object analysis to identify grasp points at abstract visual levels; and downstream sensorimotor preparatory computations for reach-to-grasp trajectories. Our results suggest the brain adheres to the needs of nonlinear motor control for integration. Furthermore, they show that examining the superadditive influence of integrated representations can serve as a novel lens to map the computations underlying sensorimotor control.

Active entanglement enables stochastic, topological grasping.

Grasping, in both biological and engineered mechanisms, can be highly sensitive to the gripper and object morphology, as well as perception and motion planning. Here, we circumvent the need for feedback or precise planning by using an array of fluidically actuated slender hollow elastomeric filaments to actively entangle with objects that vary in geometric and topological complexity. The resulting stochastic interactions enable a unique soft and conformable grasping strategy across a range of target objects that vary in size, weight, and shape. We experimentally evaluate the grasping performance of our strategy and use a computational framework for the collective mechanics of flexible filaments in contact with complex objects to explain our findings. Overall, our study highlights how active collective entanglement of a filament array via an uncontrolled, spatially distributed scheme provides options for soft, adaptable grasping.

Grasping with a Twist: Dissociating Action Goals from Motor Actions in Human Frontoparietal Circuits.

In daily life, prehension is typically not the end goal of hand-object interactions but a precursor for manipulation. Nevertheless, functional MRI (fMRI) studies investigating manual manipulation have primarily relied on prehension as the end goal of an action. Here, we used slow event-related fMRI to investigate differences in neural activation patterns between prehension in isolation and prehension for object manipulation. Sixteen (seven males and nine females) participants were instructed either to simply grasp the handle of a rotatable dial (isolated prehension) or to grasp and turn it (prehension for object manipulation). We used representational similarity analysis (RSA) to investigate whether the experimental conditions could be discriminated from each other based on differences in task-related brain activation patterns. We also used temporal multivoxel pattern analysis (tMVPA) to examine the evolution of regional activation patterns over time. Importantly, we were able to differentiate isolated prehension and prehension for manipulation from activation patterns in the early visual cortex, the caudal intraparietal sulcus (cIPS), and the superior parietal lobule (SPL). Our findings indicate that object manipulation extends beyond the putative cortical grasping network (anterior intraparietal sulcus, premotor and motor cortices) to include the superior parietal lobule and early visual cortex.SIGNIFICANCE STATEMENT A simple act such as turning an oven dial requires not only that the CNS encode the initial state (starting dial orientation) of the object but also the appropriate posture to grasp it to achieve the desired end state (final dial orientation) and the motor commands to achieve that state. Using advanced temporal neuroimaging analysis techniques, we reveal how such actions unfold over time and how they differ between object manipulation (turning a dial) versus grasping alone. We find that a combination of brain areas implicated in visual processing and sensorimotor integration can distinguish between the complex and simple tasks during planning, with neural patterns that approximate those during the actual execution of the action.

Distinct Neural Components of Visually Guided Grasping during Planning and Execution.

Selecting suitable grasps on three-dimensional objects is a challenging visuomotor computation, which involves combining information about an object (e.g., its shape, size, and mass) with information about the actor's body (e.g., the optimal grasp aperture and hand posture for comfortable manipulation). Here, we used functional magnetic resonance imaging to investigate brain networks associated with these distinct aspects during grasp planning and execution. Human participants of either sex viewed and then executed preselected grasps on L-shaped objects made of wood and/or brass. By leveraging a computational approach that accurately predicts human grasp locations, we selected grasp points that disentangled the role of multiple grasp-relevant factors, that is, grasp axis, grasp size, and object mass. Representational Similarity Analysis revealed that grasp axis was encoded along dorsal-stream regions during grasp planning. Grasp size was first encoded in ventral stream areas during grasp planning then in premotor regions during grasp execution. Object mass was encoded in ventral stream and (pre)motor regions only during grasp execution. Premotor regions further encoded visual predictions of grasp comfort, whereas the ventral stream encoded grasp comfort during execution, suggesting its involvement in haptic evaluation. These shifts in neural representations thus capture the sensorimotor transformations that allow humans to grasp objects.SIGNIFICANCE STATEMENT Grasping requires integrating object properties with constraints on hand and arm postures. Using a computational approach that accurately predicts human grasp locations by combining such constraints, we selected grasps on objects that disentangled the relative contributions of object mass, grasp size, and grasp axis during grasp planning and execution in a neuroimaging study. Our findings reveal a greater role of dorsal-stream visuomotor areas during grasp planning, and, surprisingly, increasing ventral stream engagement during execution. We propose that during planning, visuomotor representations initially encode grasp axis and size. Perceptual representations of object material properties become more relevant instead as the hand approaches the object and motor programs are refined with estimates of the grip forces required to successfully lift the object.

Bursting with Potential: How Sensorimotor Beta Bursts Develop from Infancy to Adulthood.

Beta activity is thought to play a critical role in sensorimotor processes. However, little is known about how activity in this frequency band develops. Here, we investigated the developmental trajectory of sensorimotor beta activity from infancy to adulthood. We recorded EEG from 9-month-old, 12-month-old, and adult humans (male and female) while they observed and executed grasping movements. We analyzed "beta burst" activity using a novel method that combines time-frequency decomposition and principal component analysis. We then examined the changes in burst rate and waveform motifs along the selected principal components. Our results reveal systematic changes in beta activity during action execution across development. We found a decrease in beta burst rate during movement execution in all age groups, with the greatest decrease observed in adults. Additionally, we identified three principal components that defined waveform motifs that systematically changed throughout the trial. We found that bursts with waveform shapes closer to the median waveform were not rate-modulated, whereas those with waveform shapes further from the median were differentially rate-modulated. Interestingly, the decrease in the rate of certain burst motifs occurred earlier during movement and was more lateralized in adults than in infants, suggesting that the rate modulation of specific types of beta bursts becomes increasingly refined with age.SIGNIFICANCE STATEMENT We demonstrate that, like in adults, sensorimotor beta activity in infants during reaching and grasping movements occurs in bursts, not oscillations like thought traditionally. Furthermore, different beta waveform shapes were differentially modulated with age, including more lateralization in adults. Aberrant beta activity characterizes various developmental disorders and motor difficulties linked to early brain injury, so looking at burst waveform shape could provide more sensitivity for early identification and treatment of affected individuals before any behavioral symptoms emerge. More generally, comparison of beta burst activity in typical versus atypical motor development will also be instrumental in teasing apart the mechanistic functional roles of different types of beta bursts.

Dexterous manipulation: differential sensitivity of manipulation and grasp forces to task requirements.

How humans coordinate digit forces to perform dexterous manipulation is not well understood. This gap is due to the use of tasks devoid of dexterity requirements and/or the use of analytical techniques that cannot isolate the roles that digit forces play in preventing object slip and controlling object position and orientation (pose). In our recent work, we used a dexterous manipulation task and decomposed digit forces into FG, the internal force that prevents object slip, and FM, the force responsible for object pose control. Unlike FG, FM was modulated from object lift onset to hold, suggesting their different sensitivity to sensory feedback acquired during object lift. However, the extent to which FG and FM can be controlled independently remains to be determined. Importantly, how FG and FM change as a function of object property is mathematically indeterminate and therefore requires active modulation. To address this gap, we systematically changed either object mass or external torque. The FM normal component responsible for object orientation control was modulated to changes in object torque but not mass. In contrast, FG was distinctly modulated to changes in object mass and torque. These findings point to a differential sensitivity of FG and FM to task requirements and provide novel insights into the neural control of dexterous manipulation. Importantly, our results indicate that the proposed digit force decomposition has the potential to capture important differences in how sensory inputs are processed and integrated to simultaneously ensure grasp stability and dexterous object pose control.NEW & NOTEWORTHY Successful dexterous object manipulation requires simultaneous prevention of object slip and object pose control. How these two task goals are attained can be investigated by decomposing digit forces into grasp and manipulation forces, respectively. We found that these forces were characterized by differential sensitivity to changes in object properties (mass and torque). This finding suggests the involvement of distinct sensorimotor mechanisms that, combined, simultaneously ensure grasp stability and dexterous control of object pose.

Healthy adults favor stable left/right hand choices over performance at an unconstrained reach-to-grasp task.

Reach-to-grasp actions are fundamental to the daily activities of human life, but few methods exist to assess individuals' reaching and grasping actions in unconstrained environments. The Block Building Task (BBT) provides an opportunity to directly observe and quantify these actions, including left/right hand choices. Here we sought to investigate the motor and non-motor causes of left/right hand choices, and optimize the design of the BBT, by manipulating motor and non-motor difficulty in the BBT's unconstrained reach-to-grasp task. We hypothesized that greater motor and non-motor (e.g. cognitive/perceptual) difficulty would drive increased usage of the dominant hand. To test this hypothesis, we modulated block size (large vs. small) to influence motor difficulty, and model complexity (10 vs. 5 blocks per model) to influence non-motor difficulty, in healthy adults (n = 57). Our data revealed that increased motor and non-motor difficulty led to lower task performance (slower task speed), but participants only increased use of their dominant hand only under the most difficult combination of conditions: in other words, participants allowed their performance to degrade before changing hand choices, even though participants were instructed only to optimize performance. These results demonstrate that hand choices during reach-to grasp actions are more stable than motor performance in healthy right-handed adults, but tasks with multifaceted difficulties can drive individuals to rely more on their dominant hand.

Complementary contribution of the medial and lateral human parietal cortex to grasping: a repetitive TMS study.

The dexterous control of our grasping actions relies on the cooperative activation of many brain areas. In the parietal lobe, 2 grasp-related areas collaborate to orchestrate an accurate grasping action: dorsolateral area AIP and dorsomedial area V6A. Single-cell recordings in monkeys and fMRI studies in humans have suggested that both these areas specify grip aperture and wrist orientation, but encode these grasping parameters differently, depending on the context. To elucidate the causal role of phAIP and hV6A, we stimulated these areas, while participants were performing grasping actions (unperturbed grasping). rTMS over phAIP impaired the wrist orientation process, whereas stimulation over hV6A impaired grip aperture encoding. In a small percentage of trials, an unexpected reprogramming of grip aperture or wrist orientation was required (perturbed grasping). In these cases, rTMS over hV6A or over phAIP impaired reprogramming of both grip aperture and wrist orientation. These results represent the first direct demonstration of a different encoding of grasping parameters by 2 grasp-related parietal areas.

Influence of food physical properties and environmental context on manipulative behaviors highlighted by new methodological approaches in zoo-housed bonobos (Pan paniscus).

Research on manipulative abilities in nonhuman primates, in the context of hominid evolution, has mostly focused on manual/pedal postures considered as static behaviors. While these behavioral repertoires highlighted the range of manipulative abilities in many species, manipulation is a dynamic process that mostly involves successive types of grips before reaching its goal. The present study aims to investigate the use of manual/pedal postures in zoo-housed bonobos in diverse dynamic food processing by using an innovative approach: the optimal matching analysis that compares sequences (i.e., succession of grasping postures) with each other. To characterize the manipulative techniques spontaneously employed by bonobos, we performed this sequential analysis of manual/pedal postures during 766 complete feeding sequences of 17 individuals. We analyzed the effectiveness with a score defined by a partial proxy of food intake (i.e., the number of mouthfuls) linked to a handling score measuring both the diversity and changes of manual postures during each sequence. We identified four techniques, used differently depending on the physical substrate on which the individual performed food manipulation and the food physical properties. Our results showed that manipulative techniques were more complex (i.e., higher handling score) for large foods and on substrates with lower stability. But the effectiveness score was not significantly lower for these items since manipulative complexity seemed to be compensated by a greater number of mouthfuls. It appeared that the techniques employed involved a trade-off between manipulative complexity and the amount of food ingested. This study allowed us to test and validate innovative analysis methods that are applicable to diverse ethological studies involving sequential events. Our results bring new data for a better understanding of the evolution of manual abilities in primates in association with different ecological contexts and both terrestrial and arboreal substrates and suggest that social and individual influences need to be explored further.

Field experiment of a telesurgery system using a surgical robot with haptic feedback.

PURPOSE: To verify the usefulness of haptic feedback in telesurgery and improve the safety of telerobotic surgery. METHODS: The surgeon's console was installed at two sites (Fukuoka and Beppu; 140 km apart), and the patient cart was installed in Fukuoka. During the experiment, the surgeon was blinded to the haptic feedback levels and asked to grasp the intestinal tract in an animal model. The surgeon then performed the tasks at each location. RESULTS: No marked differences in task accuracy or average grasping force were observed between the surgeon locations. However, the average task completion time was significantly longer, and the system usability scale (SUS) was significantly lower rating for remote operations than for local ones. No marked differences in task accuracy or task completion time were observed between the haptic feedback levels. However, with haptic feedback, the organ was grasped with a significantly weaker force than that without it. Furthermore, with haptic feedback, experienced surgeons in robotic surgery tended to perform an equivalent task with weaker grasping forces than inexperienced surgeons. CONCLUSION: The haptic feedback function is a tool that allows the surgeon to perform surgery with an appropriate grasping force, both on site and remotely. Improved safety is necessary in telesurgery; haptic feedback will thus be an essential technology in robotic telesurgery going forward.

Robotic Grasping of Unknown Objects Based on Deep Learning-Based Feature Detection.

In recent years, the integration of deep learning into robotic grasping algorithms has led to significant advancements in this field. However, one of the challenges faced by many existing deep learning-based grasping algorithms is their reliance on extensive training data, which makes them less effective when encountering unknown objects not present in the training dataset. This paper presents a simple and effective grasping algorithm that addresses this challenge through the utilization of a deep learning-based object detector, focusing on oriented detection of key features shared among most objects, namely straight edges and corners. By integrating these features with information obtained through image segmentation, the proposed algorithm can logically deduce a grasping pose without being limited by the size of the training dataset. Experimental results on actual robotic grasping of unknown objects over 400 trials show that the proposed method can achieve a higher grasp success rate of 98.25% compared to existing methods.

Detecting Transitions from Stability to Instability in Robotic Grasping Based on Tactile Perception.

Robots execute diverse load operations, including carrying, lifting, tilting, and moving objects, involving load changes or transfers. This dynamic process can result in the shift of interactive operations from stability to instability. In this paper, we respond to these dynamic changes by utilizing tactile images captured from tactile sensors during interactions, conducting a study on the dynamic stability and instability in operations, and propose a real-time dynamic state sensing network by integrating convolutional neural networks (CNNs) for spatial feature extraction and long short-term memory (LSTM) networks to capture temporal information. We collect a dataset capturing the entire transition from stable to unstable states during interaction. Employing a sliding window, we sample consecutive frames from the collected dataset and feed them into the network for the state change predictions of robots. The network achieves both real-time temporal sequence prediction at 31.84 ms per inference step and an average classification accuracy of 98.90%. Our experiments demonstrate the network's robustness, maintaining high accuracy even with previously unseen objects.

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

Helping Blind People Grasp: Evaluating a Tactile Bracelet for Remotely Guiding Grasping Movements.

The problem of supporting visually impaired and blind people in meaningful interactions with objects is often neglected. To address this issue, we adapted a tactile belt for enhanced spatial navigation into a bracelet worn on the wrist that allows visually impaired people to grasp target objects. Participants' performance in locating and grasping target items when guided using the bracelet, which provides direction commands via vibrotactile signals, was compared to their performance when receiving auditory instructions. While participants were faster with the auditory commands, they also performed well with the bracelet, encouraging future development of this system and similar systems.

sEMG-Based Robust Recognition of Grasping Postures with a Machine Learning Approach for Low-Cost Hand Control.

The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

Case-based report of graded motor imagery experience in traumatic brachial plexus injury: The art of moving without moving.

BACKGROUND: We reported a 24-year-old woman who sustained multiple upper limb injuries after a traffic accident in March 2017. She sustained a C7-T1 brachial plexus injury and radial nerve injury on the left side diagnosed in November 2017. The patient underwent radial nerve reconstruction. The patient began her comprehensive therapy program in January 2018. PURPOSE: To describe the use of graded motor imagery (GMI) and outcomes after traumatic brachial plexus palsy. We presented changes in electromyographic (EMG) activity of target muscles during task execution and functional status following 10-session GMI therapy. STUDY DESIGN: Case report. METHODS: The program included 4 sessions of motor imagery and 6 sessions of a combination of motor imagery and mirror therapy. RESULTS: The patient successfully participated in the program with reported improvements in EMG activity, functional status, emotional well-being, and body awareness. CONCLUSIONS: GMI therapy appears to have peripheral motor effects, including altered surface EMG activity and contributes to a favorable outcome in the functional level of the affected arm. An improved emotional state and awareness of the affected hand could have a positive effect on function. Future long-term randomized controlled trials are needed to investigate the cumulative peripheral effects of treatment of graded motor imagery and the effects of variables mediating its effects on functional performance in patients with nerve injury.

Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards.

In unstructured environments, robots need to deal with a wide variety of objects with diverse shapes, and often, the instances of these objects are unknown. Traditional methods rely on training with large-scale labeled data, but in environments with continuous and high-dimensional state spaces, the data become sparse, leading to weak generalization ability of the trained models when transferred to real-world applications. To address this challenge, we present an innovative maximum entropy Deep Q-Network (ME-DQN), which leverages an attention mechanism. The framework solves complex and sparse reward tasks through probabilistic reasoning while eliminating the trouble of adjusting hyper-parameters. This approach aims to merge the robust feature extraction capabilities of Fully Convolutional Networks (FCNs) with the efficient feature selection of the attention mechanism across diverse task scenarios. By integrating an advantage function with the reasoning and decision-making of deep reinforcement learning, ME-DQN propels the frontier of robotic grasping and expands the boundaries of intelligent perception and grasping decision-making in unstructured environments. Our simulations demonstrate a remarkable grasping success rate of 91.6%, while maintaining excellent generalization performance in the real world.

Long and short whiskers differently guide snout/pellet interaction in rat oral grasping.

We studied the role of rat whisker/snout tactile sense during oral grasping, comparing control data with those obtained, respectively, 1-3 and 5-7 days after bilateral long or short whisker trimming and 3-5 and 8-10 days after bilateral infraorbital nerve (ION) severing. Two behavioural phases were identified: whisker-snout contact by nose-N or lip-L and snout-tongue contact. The second phase involved either: snout passing over stationary pellet (Still pellet); pellet rolling as the snout passed over it (Rolling pellet); pellet being pushed forward by the snout (Pushed pellet); or pellet being hit and pushed away (Hit/Lost pellet). In controls, success was 100%, with N-contact prevailing over L-contact in the first phase and Still pellet in the second. In long whisker-trimmed versus controls, success was still 100%, but L-contact increased in frequency, Pushed pellet prevailed and the second phase duration increased. In short whisker-trimmed versus controls, success remained 100%, with increased L-contact frequency; the first phase duration did not change, but the second phase increased since in pushed trials, the pellet rolled around the snout. In ION-severed versus controls, both phases changed drastically: L-contact frequency increased, Pushed pellet prevailed and contact was persistently maintained; Hit/Lost pellet emerged, Still and Rolling pellets disappeared and the oral-grasping sequence was not triggered. These results suggest that long and short whiskers, respectively, optimize the first and second phases of snout-pellet interaction and that whisker/snout sense is necessary to trigger oral grasping. Kinematic trajectory analysis supports the conclusion that movement from whisker to snout contact is an orientation response.

Output force and ratio of laparoscopic graspers: an evaluation of operating room ergonomics.

BACKGROUND: "Laparoscopist's thumb," or thenar paresthesia, can result from prolonged or excessive grip force during laparoscopy, as can more general syndromes, such as carpal tunnel syndrome. This is particularly relevant in gynecology, where laparoscopic procedures are standard. Although this method of injury is well known, there is a paucity of data to guide surgeons in selecting more efficient, ergonomic instruments. OBJECTIVE: This study compared the ratio of applied tissue force and required surgeon input in a sample of common ratcheting laparoscopic graspers in a small-handed surgeon, to provide potential metrics applicable to surgical ergonomics and surgeon instrument choice. STUDY DESIGN: Laparoscopic graspers with varied ratcheting mechanisms and tip shapes were evaluated. Brands included Snowden-Pencer, Covidien, Aesculap, and Ethicon. A Kocher was used as an open instrument comparison. Flexiforce A401 thin-film force sensors were used to measure applied forces. Data were collected and calibrated using an Arduino Uno microcontroller board with Arduino and MATLAB software. Single-handed, complete closure of each device's ratcheting mechanism was performed 3 times. The maximum required input force in Newtons was recorded and averaged. The average output force was measured with a bare sensor and the same sensor between 2 different thicknesses of LifeLike BioTissue. RESULTS: The most ergonomic ratcheting grasper for a small-handed surgeon was identified by the output ratio: the highest output force relative to the required surgeon input (the most force for the least amount of effort). The Kocher required an average input force of 33.66 N, with its highest output ratio of 3.46 (112 N output). The Covidien Endo Grasp was the most ergonomic, with an output ratio of 0.96 on the bare force sensor (31.4 N output). The Snowden-Pencer Wavy grasper was the least ergonomic, with an output ratio of 0.06 when applied to the bare force sensor (5.9 N output). All graspers except for the Endo Grasp had improving output ratios as tissue thickness and subsequent grasper contact area increased. Input force above that provided by the ratcheting mechanisms did not increase output force in a clinically relevant amount for any of the instruments evaluated. CONCLUSION: Laparoscopic graspers vary widely in their ability to provide reliable tissue force without requiring excessive input by the surgeon, and a point of diminishing returns often exists with increased surgeon input over designed ratcheting mechanisms. Output force and output ratio are potential quantitative measures of the efficiency of laparoscopic instruments. Providing users with this type of data could assist in optimizing instrument ergonomics.