Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data

Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a new sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we draw on nature to identify targeted neural-inspired sensors on a flapping wing to detect body rotation. This task is particularly challenging as the rotational twisting mode is three orders-of-magnitude smaller than the flapping modes. We show that nonlinear filtering in time, built to mimic strain-sensitive neurons, is essential to detect rotation, whereas instantaneous measurements fail. Optimized sparse sensor placement results in efficient classification with approximately ten sensors, achieving the same accuracy and noise robustness as full measurements consisting of hundreds of sensors. Sparse sensing with neural inspired encoding establishes a new paradigm in hyper-efficient, embodied sensing of spatiotemporal data and sheds light on principles of biological sensing for agile flight control.Comment: 21 pages, 19 figure

Leveraging Temporal Dynamics with Neural-Inspired Sensing and Control

Thesis (Ph.D.)--University of Washington, 2020Flying insects are known for their fast and robust control while being challenged with sensory delays, an unsteady environment and by having limited computation power. One important component of this robust control is the sensory feedback from arrays of mechanoreceptors found on wings and wing-derived halteres. By combining structural simulation with experimentally derived neural processing models we gain insight into mechanisms involved in detecting body rotation by mechanosensory oscillating appendages. I found that it is the combination of the temporal encoding of strain by mechanoreceptors with the spatial layout of the sensors on the wing that allows for the detection of minute rotation-induced differences in wing deformation. Although several studies have presented analytical models of haltere deformation, a high fidelity Finite Element Analysis (FEA) revealed novel deformation modes resulting from haltere asymmetry. Using a neuronal spiking model on the strain from the FEA simulations, we found spike timing along the circumference of the haltere base changed with body rotation. The timing change was larger than the experimentally-observed timing variability of the individual mechanosensors at all but the top and bottom of the haltere base. This gives credence to the hypothesis of timing-based detection and encoding of rotation, in addition to the recruitment based detection commonly described in the literature. The importance of timing in mechanosensation in insect flight led to the investigation of a timing-based feedforward controller that I tested on a the partially denied inverted pendulum. Using this timing-based feedforward controller, a close-to-optimal controller could be learned in much fewer trials than a brute force search. This neural-inspired controller holds promise for engineered systems where the number of trials is limited and state measurements are denied in parts of it's state space

Bio-inspired inertial sensing by flapping wings

How hawk moths and many other flying insects acquire information on body rotations outside of the visual system is still unknown. Vision is important for flight stability, but generally believed to be too slow to explain fast reflexes during maneuvers and hovering for species such as the Manduca sexta. Insects in the Diptera order (the true flies) acquire angular velocity information with their halteres, club-like organs that once evolved from their hind wings, with strain sensors at their base to detect deformation during turns. The hawk moth wing is richly equipped with strain sensors, but the function of these sensors is still unknown. Could these wing-based strain sensors be used to detect deformation caused by body rotation? To investigate this hypothesis, an Euler-Lagrange model, a Finite-element model, and a robotic model of a flapping at plate were subjected to inertial rotations. The difference in strain between the left and the right side of the wing base indicated wing twist in all computational models. Bending strain is two orders larger than the strain due to twist, making experimentally detecting twist challenging. Wing twist was confirmed for three out of four rotation conditions, be it at different frequencies than expected from simulation. Two strain gauges measuring twist at the wing base proved to be capable of detecting wing twist, but not sufficiently robust to act as an angular velocity sensor. Future work could shed light on whether it is in fact the large array of sensors found on insect wings that allows a more robust sensing of wing deformation as a result of inertial rotations.Aerospace Engineerin

Data from: Neural evidence supports a dual sensory-motor role for insect wings

Flying insects use feedback from various sensory modalities including vision and mechanosensation to navigate through their environment. The rapid speed of mechanosensory information acquisition and processing compensates for the slower processing times associated with vision, particularly under low light conditions. While halteres in dipteran species are well known to provide such information for flight control, less is understood about the mechanosensory roles of their evolutionary antecedent, wings. The features that wing mechanosensory neurons (campaniform sensilla) encode remains relatively unexplored. We hypothesized that the wing campaniform sensilla of the hawkmoth, Manduca sexta, rapidly and selectively extract mechanical stimulus features in a manner similar to halteres. We used electrophysiological and computational techniques to characterize the encoding properties of wing campaniform sensilla. To accomplish this, we developed a novel technique for localizing receptive fields using a focused IR laser that elicits changes in the neural activity of mechanoreceptors. We found that (i) most wing mechanosensors encoded mechanical stimulus features rapidly and precisely, (ii) they are selective for specific stimulus features, and (iii) there is diversity in the encoding properties of wing campaniform sensilla. We found that the encoding properties of wing campaniform sensilla are similar to those for haltere neurons. Therefore, it appears that the neural architecture that underlies the haltere sensory function is present in wings, which lends credence to the notion that wings themselves may serve a similar sensory function. Thus, wings may not only function as the primary actuator of the organism but also as sensors of the inertial dynamics of the animal

COVID-19 and healthcare workers: a rapid systematic review into risks and preventive measures

Objective The COVID-19 pandemic is demanding for occupational medicine and for public health. As healthcare workers (HCWs) fight impacts of SARS-CoV-2 on front lines, we must create safe work environments through comprehensive risk assessments, evaluation and effective implementation of counter-measures. We ask: 'What does current literature report on health risks at workplaces regarding COVID-19?' and 'What do current studies report on the effectiveness of enacted preventative recommendations?' Methods As a snapshot of early HCW research, on 26 April 2020, we conducted a rapid systematic literature search in three databases (PubMed, Web of Science and PsycInfo) for COVID-19-related health outcomes and preventive measures in healthcare-associated workplaces. Results 27 studies were identified as relevant for exploring the risk of infection, 11 studies evaluated preventive measures. The studies described that SARS-CoV-2 impacts significantly on HCW's health and well-being, not only through infections (n=6), but also from a mental health perspective (n=16). 4 studies reported indirect risks such as skin injuries, one study described headaches to result from the use of personal protective equipment. Few studies provided information on the effectiveness of prevention strategies. Overall, most studies on health risks as well as on the effectiveness of preventive measures were of a moderate-to-low quality; this was mainly due to limitations in study design, imprecise exposure and outcome assessments. Conclusions Due to widespread exposure of HCW to SARS-CoV-2, workplaces in healthcare must be as safe as possible. Information from HCW can provide valuable insights into how infections spread, into direct and indirect health effects and into how effectively counter-measures mitigate adverse health outcomes. However, available research disallows to judge which counter-measure(s) of a current 'mix' should be prioritised for HCW. To arrive at evidence-based cost-effective prevention strategies, more well-conceived studies on the effectiveness of counter-measures are needed

Wing Video and Reconstruction Data

The “UpSampled_Generalized Base Displacement.mat” and “Generalized Base Displacement.mat” files contain the generalized wing base displacement transformed from the wing tip displacement. The first file is upsampled so that the sampling rate of the data is 40 kHz, while the later file is sampled at 1 kHz. The “Tip Motor Stim.mat” and “Motor Tip Displacement.mat” files contain the wing tip displacements of one 10-second white noise segement (first file) and for 10 10-second white noise repeats (later file). Each tip displacement file is sample at 40 kHz. Each “Moth Wing Video” folder contains the 3D high-speed videography data of that moth wing. A total of four moth wings (2 males (files labeled with M16 or M27) and 2 females (files labeled with M26 or M28)) were used to transform their wing base and tip displacements into a generalizable wing base displacement. Each “Moth Wing Video” folder contains a “DLTcoefs.csv” file (calibration file), two “.cine” files (Video data of each camera sampled at 1000 fps), and a “xyzpts.csv” file that contains the reconstructed 3D coordinates of the digitized points at each frame. The remaining files are output files not used in further analyses. Calibration and Digitization was performed with custom matlab codes by the Hedrick laboratory at UNC. High-speed videography was conducted using phantom software

uCT Wing Campaniform Movie from Neural evidence supports a dual sensory-motor role for insect wings

Micro CT movie of the campaniform sensilla on the wing of Manduca sext