Nose picking (rhinotillexis) is a common behaviour in humans which remains, however, poorly studied. Several species of primates are known to pick their nose and ingest the nasal mucus suggesting that this behaviour may actually be beneficial and showing it is not restricted to humans. Here, we review relevant literature and online sources, and document the species of primates observed to pick their nose. We also present the first occurrence of this behaviour in a species of strepsirrhine primate (lemurs and relatives) with a unique video showing an aye‐aye picking its nose. While doing so this animal inserts the entire length of its extra‐long, skinny and highly mobile middle finger into the nasal passages and then licks the nasal mucus collected. We further investigate the internal anatomy of the nasal cavity of the aye‐aye in order to understand how it can introduce its entire finger in its nasal cavity and discover that the finger likely descends into the pharynx. We show that this behaviour is present in at least 12 species of primates, most of them also showing great manipulative/tool use skills and may have some associated benefits that need to be further investigated. Further comparative studies examining nose picking and mucophagy in other primate species and vertebrates in general may shed additional light on its evolution and possible functional role.
Aye-aye’s middle finger kinematic modeling during tap-scanning
The aye-aye (Daubentonia madagascariensis) is a nocturnal lemur native to the island of Madagascar with a special thin middle finger. The aye-aye’s third digit (the slenderest one) has a remarkably specific adaptation, allowing it to perform tap-scanning (Finger tapping) to locate small cavities beneath tree bark and extract woodboring
larvae from it. This finger, as an exceptional active acoustic actuator, makes an aye-aye’s biological system an attractive model for Nondestructive Evaluation (NDE) methods and robotic systems. Despite the important aspects of the topic in engineering sensory and NDE, little is known about the mechanism and movement of this unique finger. In this paper a simplified kinematic model was proposed to simulate the aye-aye’s middle finger motion.
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- Award ID(s):
- 2047033
- NSF-PAR ID:
- 10343980
- Editor(s):
- Lakhtakia, Akhlesh; Martín-Palma, Raúl J.; Knez, Mato
- Date Published:
- Journal Name:
- SPIE, Smart Structures/NDE, 300 Long Beach Blvd, Long Beach, CA, 2022
- Page Range / eLocation ID:
- 7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)The snap of a finger has been used as a form of communication and music for millennia across human cultures. However, a systematic analysis of the dynamics of this rapid motion has not yet been performed. Using high-speed imaging and force sensors, we analyse the dynamics of the finger snap. We discover that the finger snap achieves peak angular accelerations of 1.6 × 10 6 ° s −2 in 7 ms, making it one of the fastest recorded angular accelerations the human body produces (exceeding professional baseball pitches). Our analysis reveals the central role of skin friction in mediating the snap dynamics by acting as a latch to control the resulting high velocities and accelerations. We evaluate the role of this frictional latch experimentally, by covering the thumb and middle finger with different materials to produce different friction coefficients and varying compressibility. In doing so, we reveal that the compressible, frictional latch of the finger pads likely operates in a regime optimally tuned for both friction and compression. We also develop a soft, compressible friction-based latch-mediated spring actuated model to further elucidate the key role of friction and how it interacts with a compressible latch. Our mathematical model reveals that friction plays a dual role in the finger snap, both aiding in force loading and energy storage while hindering energy release. Our work reveals how friction between surfaces can be harnessed as a tunable latch system and provides design insight towards the frictional complexity in many robotic and ultra-fast energy-release structures.more » « less
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A reliable and functional neural interface is necessary to control individual finger movements of assistive robotic hands. Non-invasive surface electromyogram (sEMG) can be used to predict fingertip forces and joint kinematics continuously. However, concurrent prediction of kinematic and dynamic variables in a continuous manner remains a challenge. The purpose of this study was to develop a neural decoding algorithm capable of concurrent prediction of fingertip forces and finger dynamic movements. High-density electromyogram (HD-EMG) signal was collected during finger flexion tasks using either the index or middle finger: isometric, dynamic, and combined tasks. Based on the data obtained from the two first tasks, motor unit (MU) firing activities associated with individual fingers and tasks were derived using a blind source separation method. MUs assigned to the same tasks and fingers were pooled together to form MU pools. Twenty MUs were then refined using EMG data of a combined trial. The refined MUs were applied to a testing dataset of the combined task, and were divided into five groups based on the similarity of firing patterns, and the populational discharge frequency was determined for each group. Using the summated firing frequencies obtained from five groups of MUs in a multivariate linear regression model, fingertip forces and joint angles were derived concurrently. The decoding performance was compared to the conventional EMG amplitude-based approach. In both joint angles and fingertip forces, MU-based approach outperformed the EMG amplitude approach with a smaller prediction error (Force: 5.36±0.47 vs 6.89±0.39 %MVC, Joint Angle: 5.0±0.27° vs 12.76±0.40°) and a higher correlation (Force: 0.87±0.05 vs 0.73±0.1, Joint Angle: 0.92±0.05 vs 0.45±0.05) between the predicted and recorded motor output. The outcomes provide a functional and accurate neural interface for continuous control of assistive robotic hands.more » « less
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null (Ed.)Abstract In this study, a numerical framework for joint rotation configuration models of a finger is proposed. The basic idea is to replicate the finger’s geometric posture observed when the human hand grasps a cylindrical object with various cross sections. In the model development, objects with the cross section adopted from the curves of order two (the family of conic sections) are taken into consideration to realize various finger postures. In addition, four different grasp styles, which simulate the individual-specific contact pattern between the surfaces of object and finger, are modeled and applied for the formulation of numerical models. An idea on how to change flexion/extension patterns in the middle of excursion of movement is proposed and discussed. Series of numerical studies have been conducted and analyzed to evaluate the proposed models. From the results, one can see the models’ feasibility and viability as a solution to describing finger’s flexion/extension movements (FEMs) for grasping patterns.more » « less
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Abstract Objective. Brain–machine interfaces (BMIs) have shown promise in extracting upper extremity movement intention from the thoughts of nonhuman primates and people with tetraplegia. Attempts to restore a user’s own hand and arm function have employed functional electrical stimulation (FES), but most work has restored discrete grasps. Little is known about how well FES can control continuous finger movements. Here, we use a low-power brain-controlled functional electrical stimulation (BCFES) system to restore continuous volitional control of finger positions to a monkey with a temporarily paralyzed hand. Approach. We delivered a nerve block to the median, radial, and ulnar nerves just proximal to the elbow to simulate finger paralysis, then used a closed-loop BMI to predict finger movements the monkey was attempting to make in two tasks. The BCFES task was one-dimensional in which all fingers moved together, and we used the BMI’s predictions to control FES of the monkey’s finger muscles. The virtual two-finger task was two-dimensional in which the index finger moved simultaneously and independently from the middle, ring, and small fingers, and we used the BMI’s predictions to control movements of virtual fingers, with no FES. Main results. In the BCFES task, the monkey improved his success rate to 83% (1.5 s median acquisition time) when using the BCFES system during temporary paralysis from 8.8% (9.5 s median acquisition time, equal to the trial timeout) when attempting to use his temporarily paralyzed hand. In one monkey performing the virtual two-finger task with no FES, we found BMI performance (task success rate and completion time) could be completely recovered following temporary paralysis by executing recalibrated feedback-intention training one time. Significance. These results suggest that BCFES can restore continuous finger function during temporary paralysis using existing low-power technologies and brain-control may not be the limiting factor in a BCFES neuroprosthesis.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1117/12.2612943
