A fluorescently-tagged tick kinin neuropeptide triggers peristalsis and labels tick midgut muscles.

Ticks are blood-feeding arthropods that require heme for their successful reproduction. During feeding they also acquire pathogens that are subsequently transmitted to humans, wildlife and/or livestock. Understanding the regulation of tick midgut is important for blood meal digestion, heme and nutrient absorption processes and for aspects of pathogen biology in the host. We previously demonstrated the activity of tick kinins on the cognate G protein-coupled receptor. Herein we uncovered the physiological role of the kinin receptor in the tick midgut. A fluorescently-labeled kinin peptide with the endogenous kinin 8 sequence (TMR-RK8), identical in the ticks Rhipicephalus microplus and R. sanguineus, activated and labeled the recombinant R. microplus receptor expressed in CHO-K1 cells. When applied to the live midgut the TMR-RK8 labeled the kinin receptor in muscles while the labeled peptide with the scrambled-sequence of kinin 8 (TMR-Scrambled) did not. The unlabeled kinin 8 peptide competed TMR-RK8, decreasing confocal microscopy signal intensity, indicating TMR-RK8 specificity to muscles. TMR-RK8 was active, inducing significant midgut peristalsis that was video-recorded and evaluated with video tracking software. The TMR-Scrambled peptide used as a negative control did not elicit peristalsis. The myotropic function of kinins in eliciting tick midgut peristalsis was established.

Estimation of lower back muscle force in a lifting task using wearable IMUs.

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

Scaling of buccal mass growth and muscle activation determine the duration of feeding behaviours in the marine mollusc Aplysia californica.

The mechanical forces experienced during movement and the time constants of muscle activation are important determinants of the durations of behaviours, which may both be affected by size-dependent scaling. The mechanics of slow movements in small animals are dominated by elastic forces and are thus quasistatic (i.e. always near mechanical equilibrium). Muscular forces producing movement and elastic forces resisting movement should scale identically (proportional to mass2/3), leaving the scaling of the time constant of muscle activation to play a critical role in determining behavioural duration. We tested this hypothesis by measuring the duration of feeding behaviours in the marine mollusc Aplysia californica whose body sizes spanned three orders of magnitude. The duration of muscle activation was determined by measuring the time it took for muscles to produce maximum force as A. californica attempted to feed on tethered inedible seaweed, which provided an in vivo approximation of an isometric contraction. The timing of muscle activation scaled with mass0.3. The total duration of biting behaviours scaled identically, with mass0.3, indicating a lack of additional mechanical effects. The duration of swallowing behaviour, however, exhibited a shallower scaling of mass0.17. We suggest that this was due to the allometric growth of the anterior retractor muscle during development, as measured by micro-computed tomography (micro-CT) scans of buccal masses. Consequently, larger A. californica did not need to activate their muscles as fully to produce equivalent forces. These results indicate that muscle activation may be an important determinant of the scaling of behavioural durations in quasistatic systems.

Machine learning reveals the control mechanics of an insect wing hinge.

Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder-decoder4 that predicts the role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres that are remarkably similar to those of free-flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably among the most sophisticated and evolutionarily important skeletal structures in the natural world.

Muscle and joint mechanics during maximum force biting following total temporomandibular joint replacement surgery.

Total temporomandibular joint replacement (TMJR) surgery is the established treatment for severe temporomandibular joint disorders. While TMJR surgery is known to increase mouth-opening capacity, reduce pain and improve quality of life, little is known about post-surgical jaw function during activities of daily living such as biting and chewing. The aim of this study was to use subject-specific 3D bite force measurements to evaluate the magnitude and direction of joint loading in unilateral total TMJR patients and compare these data to those in healthy control subjects. An optoelectronic tracking system was used to measure jaw kinematics while biting a rubber sample for 5 unilateral total TMJR patients and 8 controls. Finite element simulations driven by the measured kinematics were employed to calculate the resultant bite force generated when compressing the rubber between teeth during biting tasks. Subject-specific musculoskeletal models were subsequently used to calculate muscle and TMJ loading. Unilateral total TMJR patients generated a bite force of 249.6 ± 24.4 N and 164.2 ± 62.3 N when biting on the contralateral and ipsilateral molars, respectively. In contrast, controls generated a bite force of 317.1 ± 206.6 N. Unilateral total TMJR patients biting on the contralateral molars had a significantly higher lateral TMJ force direction (median difference: 63.6°, p = 0.028) and a significantly lower ratio of working TMJ force to bite force (median difference: 0.17, p = 0.049) than controls. Results of this study may guide TMJ prosthesis design and evaluation of dental implants.

Prediction of trunk muscle activation and spinal forces in adolescent idiopathic scoliosis during simulated trunk motion: A musculoskeletal modelling study.

Due to lack of reference validation data, the common strategy in characterizing adolescent idiopathic scoliosis (AIS) by musculoskeletal modelling approach consists in adapting structure and parameters of validated body models of adult individuals with physiological alignments. Until now, only static postures have been replicated and investigated in AIS subjects. When aiming to simulate trunk motion, two critical factors need consideration: how distributing movement along the vertebral motion levels (lumbar spine rhythm), and if neglecting or accounting for the contribution of the stiffness of the motion segments (disc stiffness). The present study investigates the effect of three different lumbar spine rhythms and absence/presence of disc stiffness on trunk muscle imbalance in the lumbar region and on intervertebral lateral shear at different levels of the thoracolumbar/lumbar scoliotic curve, during simulated trunk motions in the three anatomical planes (flexion/extension, lateral bending, and axial rotation). A spine model with articulated ribcage previously developed in AnyBody software and adapted to replicate the spinal alignment in AIS subjects is employed. An existing dataset of 100 subjects with mild and moderate scoliosis is exploited. The results pointed out the significant impact of lumbar spine rhythm configuration and disc stiffness on changes in the evaluated outputs, as well as a relationship with scoliosis severity. Unfortunately, no optimal settings can be identified due to lack of reference validation data. According to that, extreme caution is recommended when aiming to adapt models of adult individuals with physiological alignments to adolescent subjects with scoliotic deformity.

Mathematical modeling of exercise fatigability in the severe domain: A unifying integrative framework in isokinetic condition.

Muscle fatigue is the decay in the ability of muscles to generate force, and results from neural and metabolic perturbations. This article presents an integrative mathematical model that describes the decrease in maximal force capacity (i.e. fatigue) over exercises performed at intensities above the critical force Fc (i.e. severe domain). The model unifies the previous Critical Power Model and All-Out Model and can be applied to any exercise described by a changing force F over time. The assumptions of the model are (i) isokinetic conditions, an intensity domain of Fc

High-Stroke, High-Output-Force, Fabric-Lattice Artificial Muscles for Soft Robots.

Artificial muscles, providing safe and close interaction between humans and machines, are essential in soft robotics. However, their insufficient deformation, output force, or configurability usually limits their applications. Herein, this work presents a class of lightweight fabric-lattice artificial muscles (FAMs) that are pneumatically actuated with large contraction ratios (up to 87.5%) and considerable output forces (up to a load of 20 kg, force-to-weight ratio of over 250). The developed FAMs consist of a group of active air chambers that are zigzag connected into a lattice through passive connecting layers. The geometry of these fabric components is programmable to convert the in-plane lattice of FAMs into out-of-plane configurations (e.g., arched and cylindrical) capable of linear/radial contraction. This work further demonstrates that FAMs can be configured for various soft robotic applications, including the powerful robotic elbow with large motion range and high load capability, the well-fitting assistive shoulder exosuit that can reduce muscle activity during abduction, and the adaptive soft gripper that can grasp irregular objects. These results show the unique features and broad potential of FAMs for high-performance soft robots.

A comparison of dissection and 3D approaches to estimate muscle physiological cross-sectional area, validated by in vivo bite forces.

Performance traits such as bite forces are crucial to fitness and relate to the niche and adaptation of species. However, for many insects it is not possible to directly measure bite forces because they are too small. Biomechanical models of bite forces are therefore relevant to test hypotheses of adaptation in insects and other small organisms. Although such models are based on classical mechanics, combining forces, material properties and laws of levers, it is currently unknown how various models relate to bite forces measured in vivo. One critical component of these models is the physiological cross-sectional area (PCSA) of muscles, which relates to the maximum amount of force they can produce. Here, using the grasshopper Schistocerca gregaria, we compare various ways to obtain PCSA values and use in vivo measurements of bite forces to validate the biomechanical models. We show that most approaches used to derive PCSA (dissection, 3D muscle convex hull volume, muscle attachment area) are consistent with the expected relationships between PCSA and bite force, as well as with the muscle stress values known for insects. The only exception to this are PCSA values estimated by direct 3D muscle volume computation, which could be explained by noisy variation produced by shrinkage. This method therefore produces PCSA values which are uncorrelated to in vivo bite forces. Furthermore, despite the fact that all other methods do not significantly differ from expectations, their derived PCSA values vary widely, suggesting a lack of comparability between studies relying on different methods.

Air-Working Electrochromic Artificial Muscles.

Artificial muscles are indispensable components for next-generation robotics to mimic the sophisticated movements of living systems and provide higher output energies when compared with real muscles. However, artificial muscles actuated by electrochemical ion injection have problems with single actuation properties and difficulties in stable operation in air. Here, air-working electrochromic artificial muscles (EAMs) with both color-changing and actuation functions are reported, which are constructed based on vanadium pentoxide nanowires and carbon tube yarn. Each EAM can generate a contractile stroke of ≈12% during stable operation in the air with multiple color changes (yellow-green-gray) under ±4 V actuation voltages. The reflectance contrast is as high as 51%, demonstrating the excellent versatility of the EAMs. In addition, a torroidal EAM arrangement with fast response and high resilience is constructed. The EAM's contractile stroke can be displayed through visual color changes, which provides new ideas for future artificial muscle applications in soft robots and artificial limbs.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

An Accurate Fiducial Marker for Aligning EMG signals with Swallow Onset.

Swallowing involves the precise coordination of a large number of muscles. This coordination can be quantified non-invasively by electromyographic (EMG) time-series analysis of swallowing events. The temporal alignment of swallow events is critical for defining coordination patterns. Here, a new framework was developed to use the acoustic signal associated with the opening of the Eustachian tube as a fiducial marker to align EMG signals with swallowing. To investigate its accuracy, manometry, audio from the Eustachian tube, and EMG were simultaneously recorded from two participants while performing different swallowing maneuvers. Eustachian tube opening consistently occurred alongside EMG activations and within 0.025 ± 0.022 s of the gold standard manometry-determined functional swallowing onset. A comparison with two traditional EMG alignment methods based on the integrated and rectified EMG signals was then performed over eight participants. Discrepancies of between 0.2 to 0.3 s were found between the initiation of swallowing and the onset or peak EMG activity. Eustachian tube opening served as a more accurate fiducial marker for temporal data alignment, compared to the traditional EMG alignment methods that were based on EMG parameters.Clinical Relevance- The proposed method will allow EMG recordings to be directly associated with the functional onset of swallowing. This provides a more accurate foundation for time-series analysis of muscle coordination and thus the identification of EMG biomarkers associated with healthy and dysphagic swallowing.

Kinematic Motor Synergy Analysis to Understand Lock Dance Choreographies.

Lock dance, or locking, is one of the popular old-school street dance styles featuring sharp, sudden, and isolated body movements through intricate control and coordination of joints and muscles. This work aims to understand the complex lock dance motions based on kinematic motor synergy analysis. Lock dance motions performed by three experienced dancers were measured with a markerless human motion capture technique. The motor synergies were identified and summarized using principle component analysis (PCA). The motion complexity, joint contributions, and motor coordination of ten basic lock dance choreographies were analyzed based on the synergy patterns and their activations. The results enhance our understanding of complex dance motions and serve as a step toward future applications to, e.g. dance skill or injury risk assessments.

Hand Force Estimation from Acoustic Myography Using Deep Wavelet Scattering Transform and Long Short-Term Memory.

The ability to estimate user intention from surface electromyogram (sEMG) signals is a crucial aspect in the design of powered prosthetics. Recently, researchers have been using regression techniques to connect the user's intent, as expressed through sEMG signals, to the force applied at the fingertips in order to achieve a natural and accurate form of control. However, there are still challenges associated with processing sEMG signals that need to be overcome to allow for widespread and clinical implementation of upper limb prostheses. As a result, alternative modalities functioning as promising control signals have been proposed as source of control input rather than the sEMG, such as Acoustic Myography (AMG). In this study, six high sensitivity array microphones were used to acquire AMG signals, with custom-built 3D printed microphone housing. To tackle the challenge of extracting the relevant information from AMG signals, the Wavelet Scattering Transform (WST) was utilized. alongside a Long Short-Term Memory (LSTM) neural network model for predicting the force from the AMG. The subjects were asked to use a hand dynamometer to measure the changes in force and correlate that to the force predicted by using the AMG features. Seven subjects were recruited for data collection in this study, using hardware designed by the research team. the performance results showed that the WST-LSTM model can be robustly utilized across varying window sizes and testing schemes, to achieve average NRMSE results of approximately 8%. These pioneering results suggest that AMG signals can be utilized to reliably estimate the force levels that the muscles are applying.Clinical Relevance- This research presents a new method for controlling upper limb prostheses using Acoustic Myography (AMG) signals. A novel method mapping the AMG signals to force applied by the corresponding muscles is developed. The presented findings have the potential to lead to the development of more natural and accurate control of human-machine interfaces.

Electromyographic study of the arms in competitive karting senior category drivers / Estudio electromiográfico de los brazos en pilotos senior de karting de competición

The main goal of this cross-sectional study was to assess the muscular activity of the upper limbs in competitive kart drivers while driving in a closed karting circuit, using surface electromyography (EMGS). The most significant muscles of the upper limbs while driving were evaluated in thirteen drivers. Linear mixed models adjusted to a gamma distribution were used to evaluate differences in muscle activity based on the arm, number of laps, track characteristics, and kart type (with/without gears). Significant differences were found between muscle activity according to the type of kart (p <0.0001). Although changes were observed in the mean EMGS values, there were no significant differences between the laps of the circuit or the dominant arm. However, the results showed that there was a significant interaction between the type of kart and the dominant arm (p = 0.021). Muscle activity increased more significantly in the curves traced towards the dominant arms of the drivers (AU)

El objetivo del estudio transversal fue analizar la actividad muscular de las extremidades superiores en pilotos senior de karting de competición, mediante electromiografía de superficie (EMGS). Se evaluaron los músculos más significativos de las extremidades superiores durante la conducción en trece conductores. Se utilizaron modelos lineales mixtos ajustados a una distribución gamma para evaluar diferencias de actividad muscular en base al brazo, número de vueltas, características del trazado, y tipo de kart (con/sin marchas). Se encontraron diferencias significativas entre la actividad muscular según el tipo de kart (p<0,0001). Aunque se observaron cambios en los valores medios de EMGS, no hubo diferencias significativas entre las vueltas del circuito o el brazo dominante. Sin embargo, los resultados mostraron que hubo una interacción significativa entre el tipo de kart y el brazo dominante (p=0,021). La actividad muscular aumentó de manera más significativa en las curvas trazadas hacia el brazo dominante de los conductores (AU)

A Novel Tilted-Plane Ergometer System for Subject-Specific Rehabilitation.

Immobilization due to various reasons can lead to disuse muscle atrophy. If prolonged, the circumstance is exacerbated and may lead to joint contracture, dysfunction, and long-term sequela. Thus, a balanced exercise regimen is crucial. While able-bodied individuals can perform a variety of exercises, bedridden patients typically resort to exercising primarily with bicycle ergometers. However, since the pedaling trajectory with ergometers is confined to the sagittal plane, muscles responsible for medial-lateral movement and balance are not effectively trained. Furthermore, the direction of joint reaction forces, which is crucial for specific patients with ligament injuries, recurrent dislocations, and medial osteoarthritis, is not well facilitated. Thus, it would be beneficial for patients without full body weight support ability to train ab-/ad-ductor muscles by altering the direction of extrinsic load via ergometers. In this study, we present a novel Tilted-Plane Ergometer and proof-of-concept experiment with one healthy subject. The results suggest that subtle changes in ergometer configurations lead to different movements, joint alignments, and muscle recruitment patterns.

Joint power, joint work and lower limb muscle activity for transitions between level walking and stair ambulation at three inclinations.

To enhance human mobility, training interventions and assistive lower limb wearable robotic designs must draw insights from movement tasks from daily life. This study aimed to analyze joint peak power, limb and joint work, and muscle activity of the lower limb during a series of stair ambulation conditions. We recruited 12 subjects (25.4±4.5 yrs, 180.1±4.6 cm, 74.6±7.9 kg) and studied steady gait and gait transitions between level walking, stair ascent and stair descent for three staircase inclinations (low 19°, normal 30.4°, high 39.6°). Our analysis revealed that joint peak power, limb and joint work, and muscle activity increased significantly compared to level walking and with increasing stair inclination for most of the conditions analyzed. Transition strides had no increased requirements compared to the maxima found for steady level walking and steady stair ambulation. Stair ascent required increased lower limb joint positive peak power and work, while stair descent required increased lower limb joint negative peak power and work compared to level walking. The most challenging condition was high stair inclination, which required approximately thirteen times the total lower limb joint positive and negative net work during ascent and descent, respectively. These findings suggest that training interventions and lower limb wearable robotic designs must consider the major increases in lower limb joint and muscle effort during stair ambulation, with specific attention to the demands of ascent and descent, to effectively improve human mobility.

OpenCap: Human movement dynamics from smartphone videos.

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

Knee kinematics are primarily determined by implant alignment but knee kinetics are mainly influenced by muscle coordination strategy.

Implant malalignment has been reported to be a primary reason for revision total knee arthroplasty (TKA). In addition, altered muscle coordination patterns are commonly observed in TKA patients, which is thought to alter knee contact loads. A comprehensive understanding of the influence of surgical implantation and muscle recruitment strategies on joint contact mechanics is crucial to improve surgical techniques, increase implant longevity, and inform rehabilitation protocols. In this study, a detailed musculoskeletal model with a 12 degrees of freedom knee was developed to represent a TKA subject from the CAMS-Knee datasets. Using motion capture and ground reaction force data, a level walking cycle was simulated and the joint movement and loading patterns were estimated using a novel technique for concurrent optimization of muscle activations and joint kinematics. In addition, over 12'000 Monte Carlo simulations were performed to predict knee contact mechanics during walking, considering numerous combinations of implant alignment and muscle activation scenarios. Validation of our baseline simulation showed good agreement between the model kinematics and loading patterns against the in vivo data. Our analyses reveal a considerable impact of implant alignment on the joint kinematics, while variation in muscle activation strategies mainly affects knee contact loading. Moreover, our results indicate that high knee compressive forces do not necessarily originate from extreme kinematics and vice versa. This study provides an improved understanding of the complex inter-relationships between loading and movement patterns resulting from different surgical implantation and muscle coordination strategies and presents a validated framework towards population-based modelling in TKA.

Bridging two insect flight modes in evolution, physiology and robophysics.

Since taking flight, insects have undergone repeated evolutionary transitions between two seemingly distinct flight modes1-3. Some insects neurally activate their muscles synchronously with each wingstroke. However, many insects have achieved wingbeat frequencies beyond the speed limit of typical neuromuscular systems by evolving flight muscles that are asynchronous with neural activation and activate in response to mechanical stretch2-8. These modes reflect the two fundamental ways of generating rhythmic movement: time-periodic forcing versus emergent oscillations from self-excitation8-10. How repeated evolutionary transitions have occurred and what governs the switching between these distinct modes remain unknown. Here we find that, despite widespread asynchronous actuation in insects across the phylogeny3,6, asynchrony probably evolved only once at the order level, with many reversions to the ancestral, synchronous mode. A synchronous moth species, evolved from an asynchronous ancestor, still preserves the stretch-activated muscle physiology. Numerical and robophysical analyses of a unified biophysical framework reveal that rather than a dichotomy, these two modes are two regimes of the same dynamics. Insects can transition between flight modes across a bridge in physiological parameter space. Finally, we integrate these two actuation modes into an insect-scale robot11-13 that enables transitions between modes and unlocks a new self-excited wingstroke strategy for engineered flight. Together, this framework accounts for repeated transitions in insect flight evolution and shows how flight modes can flip with changes in physiological parameters.