Ergonomic analysis of the dimension of a precision tool handle: a case study

González, Alfonso González; Rodriguez, Donald; García-Sanz-Calcedo, Justo

doi:10.1016/j.promfg.2017.09.111

Cited by 7 publications

(11 citation statements)

References 24 publications

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“…The novice uses inappropriate grip forces in these strategic finger-hand regions, producing either excessive or insufficient force at the critical sensor locations. The grip force profiles are consistent with anatomical data relative to the evolution of strategic use of functionally specific finger and hand regions for grip and grip force control in manually executed precision tasks [17][18][19][20]. Preconceptions that grip forces may be universally stronger in the dominant hand [18] require a reconsideration in the light of the grip force profiles from this study, which show that the level of expertise and task-device specific factors determine the total amount of grip force deployed by either the dominant or the nondominant hand.…”

Section: Discussionsupporting

confidence: 65%

“…Sensor S5 was positioned on the middle phalanx of the middle finger, critical for strong grip force control, sensor S6 on the middle phalanx of the ring finger, contributes to force modulation less critical for strong grip control, sensor S7 on the middle phalanx of the small finger (pinky) highly critical for subtle, finely tuned grip force control. Experts like surgeons use their pinkies strategically for the fine-tuning of hand-tool interactions [17,19,20]. Sensor S10 was positioned on the metacarpal that joins the thumb to the wrist, important in general grip control (getting hold of the device handles), but not critical for subtle strategic grip force control [17].…”

Section: Resultsmentioning

confidence: 99%

“…It will be useful in the future to exploit individual grip force profiling in the context of comparisons between different camera systems for robot-assisted surgery systems. Finally, the sensor glove system developed for this study here will need to be improved further to ensure that sensor positions can be adapted with high precision to the left and right hands of any group of surgeons or surgical trainees including women surgeons, whose hands are statistically smaller, as discussed previously in systematic anthropometric studies [20].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

Mathelin

Nageotte

Zanne

et al. 2019

Preprint

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STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a flexible robotic system based on the Anubis® platform of Karl Storz for application to intra-luminal surgical procedures. It consists of three cable-driven systems, one endoscope serving as guide and two inserted instruments. The flexible and bendable instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this research, a pair of specific sensor gloves, which ergonomically fit to the master handles of the system was designed and the forces applied by one expert and one novice user during system-specific task execution in a simulator task (4-step-pick-and-drop) were compared. The results show that user expertise is not only reflected by shorter task execution times but also, more importantly, by specific differences in handgrip force profiles for specific sensor locations on anatomically relevant parts of the fingers and hand controlling the surgical instruments of the robotic master/slave system.

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Section: Discussionsupporting

confidence: 65%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

Mathelin

Nageotte

Zanne

et al. 2019

Preprint

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“…The state of the art in experimental studies on grip force control for lifting and manipulating objects [88,89,91,93,94] provides insight into the contributions of each finger to overall grip strength and fine grip force control. The middle finger, for example, has evolved to become the most important contributor to gross total grip force and, therefore, is most important for getting a good grip of heavy objects to lift or carry, while the ring finger and the small (pinky) finger have evolved for the fine control of subtle grip force modulations, which is important in precision tasks [102][103][104][105][106]. Human grip force is governed by self-organizing prehensile synergies [91,92] that involve from-local-to-global functional interactions (property 6) between sensory (low-level) and central (high-level) representations in the somatosensory brain.…”

Section: Seven Properties Of Self-organization In a Somatosensory Neumentioning

confidence: 99%

Seven Properties of Self-Organization in the Human Brain

Dresp-Langley

2020

BDCC

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The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: (1) modular connectivity, (2) unsupervised learning, (3) adaptive ability, (4) functional resiliency, (5) functional plasticity, (6) from-local-to-global functional organization, and (7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward.

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“…While current multi-modal feed-back systems may represent a slight advantage over the not very effective traditional single modality feedback solutions by achieving average grip forces closer to those normally possible with the human hand, the monitoring of individual grip forces of the surgeon (or trainee) during task execution by wearable multisensory systems is by far the superior solution. Real-time grip force sensing by wearable systems can directly help prevent incidents, because it includes the possibility of sending a signal (sound or light) to the operator or surgeon whenever his/her grip force exceeds a critical limit before the damage is done [24][25][26][27][28][29]. Proficiency, or expertise, in the control of a robotic master/slave system designed for minimally invasive surgery is reflected by a lesser grip force during task execution as well as by shorter task execution times [21,22,25,27].…”

Section: Discussionmentioning

confidence: 99%

Correlating Grip Force Signals From Multiple Sensors Highlights Functional Synergies of Manual Control in a Complex Task-User System

Dresp-Langley¹,

Nageotte²,

Zanne³

et al. 2020

Preprint

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Biosensors and wearable sensor systems with transmitting capabilities are currently developed and used for the monitoring of health data, exercise activities, and other performance data. Unlike conventional approaches, these devices enable convenient, continuous, and unobtrusive monitoring of a user’s behavioral signals in real time. Examples include signals relative to hand an finger movement/pressure control reflected by individual grip force data. As will be shown here, these directly translate into task, skill and hand-specific (dominant versus non-dominant hand) grip force profiles for different measurement loci in the fingers and palm of the hand. On the basis of thousands of sensor data from multiple sensor locations, individual grip force profiles of an task expert, a trained user and a highly proficient user (expert) performing an image-guided and robot-assisted precision task with the dominant or the non-dominant hand are analyzed in several steps following Tukey’s “detective work” approach. Correlation analyses (Person’s Product Moment) reveal skill-specific differences in individual grip force profiles across multiple sources of variation, functionally mapped to the somatosensory brain networks which ensure grip force control and its evolution with control expertise. Implications for the real-time monitoring of individual grip force profiles and their evolution with training in complex task-user systems are brought forward.

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Ergonomic analysis of the dimension of a precision tool handle: a case study

Cited by 7 publications

References 24 publications

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements

Seven Properties of Self-Organization in the Human Brain

Correlating Grip Force Signals From Multiple Sensors Highlights Functional Synergies of Manual Control in a Complex Task-User System

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