Estimation of Mechanical Power Output Employing Deep Learning on Inertial Measurement Data in Roller Ski Skating

The ability to optimize power generation in sports is imperative, both for understanding and balancing training load correctly, and for optimizing competition performance. In this paper, we aim to estimate mechanical power output by employing a time-sequential information-based deep Long Short-Term Memory (LSTM) neural network from multiple inertial measurement units (IMUs). Thirteen athletes conducted roller ski skating trials on a treadmill with varying incline and speed. The acceleration and gyroscope data collected with the IMUs were run through statistical feature processing, before being used by the deep learning model to estimate power output. The model was thereafter used for prediction of power from test data using two approaches. First, a user-dependent case was explored, reaching a power estimation within 3.5% error. Second, a user-independent case was developed, reaching an error of 11.6% for the power estimation. Finally, the LSTM model was compared to two other machine learning models and was found to be superior. In conclusion, the user-dependent model allows for precise estimation of roller skiing power output after training the model on data from each athlete. The user-independent model provides less accurate estimation; however, the accuracy may be sufficient for providing valuable information for recreational skiers.

A physical model of mantis shrimp for exploring the dynamics of ultrafast systems

Efficient and effective generation of high-acceleration movement in biology requires a process to control energy flow and amplify mechanical power from power density–limited muscle. Until recently, this ability was exclusive to ultrafast, small organisms, and this process was largely ascribed to the high mechanical power density of small elastic recoil mechanisms. In several ultrafast organisms, linkages suddenly initiate rotation when they overcenter and reverse torque; this process mediates the release of stored elastic energy and enhances the mechanical power output of extremely fast, spring-actuated systems. Here we report the discovery of linkage dynamics and geometric latching that reveals how organisms and synthetic systems generate extremely high-acceleration, short-duration movements. Through synergistic analyses of mantis shrimp strikes, a synthetic mantis shrimp robot, and a dynamic mathematical model, we discover that linkages can exhibit distinct dynamic phases that control energy transfer from stored elastic energy to ultrafast movement. These design principles are embodied in a 1.5-g mantis shrimp scale mechanism capable of striking velocities over 26 m s−1 in air and 5 m s−1 in water. The physical, mathematical, and biological datasets establish latching mechanics with four temporal phases and identify a nondimensional performance metric to analyze potential energy transfer. These temporal phases enable control of an extreme cascade of mechanical power amplification. Linkage dynamics and temporal phase characteristics are easily adjusted through linkage design in robotic and mathematical systems and provide a framework to understand the function of linkages and latches in biological systems.

Design and implementation of a power smoothing system for cross-flow current turbines

This study presents an efficient system that smooths fluctuations in electrical power from a cross-flow (i.e., “vertical-axis”) turbine. The proposed solution is a two-stage approach consisting of a low-pass filter and a bi-directional buck-boost converter. The design and stability characteristics of the system are presented, followed by time-domain simulation and validation against small-scale experiments. When this validated simulation is applied to a full-scale system, we demonstrate a 99% root mean square reduction in fluctuating power output with only a 3% drop in electrical system efficiency. This could allow intracycle control strategies to increase mechanical power output without causing electrical power fluctuations that are incompatible with direct use.

Tuna robotics: hydrodynamics of rapid linear accelerations

Fish routinely accelerate during locomotor manoeuvres, yet little is known about the dynamics of acceleration performance. Thunniform fish use their lunate caudal fin to generate lift-based thrust during steady swimming, but the lift is limited during acceleration from rest because required oncoming flows are slow. To investigate what other thrust-generating mechanisms occur during this behaviour, we used the robotic system termed Tunabot Flex, which is a research platform featuring yellowfin tuna-inspired body and tail profiles. We generated linear accelerations from rest of various magnitudes (maximum acceleration of 3.22 m s − 2 at 11.6 Hz tail beat frequency) and recorded instantaneous electrical power consumption. Using particle image velocimetry data, we quantified body kinematics and flow patterns to then compute surface pressures, thrust forces and mechanical power output along the body through time. We found that the head generates net drag and that the posterior body generates significant thrust, which reveals an additional propulsion mechanism to the lift-based caudal fin in this thunniform swimmer during linear accelerations from rest. Studying fish acceleration performance with an experimental platform capable of simultaneously measuring electrical power consumption, kinematics, fluid flow and mechanical power output provides a new opportunity to understand unsteady locomotor behaviours in both fishes and bioinspired aquatic robotic systems.

Nonlinear elasticity and damping govern ultrafast dynamics in click beetles

Many small animals use springs and latches to overcome the mechanical power output limitations of their muscles. Click beetles use springs and latches to bend their bodies at the thoracic hinge and then unbend extremely quickly, resulting in a clicking motion. When unconstrained, this quick clicking motion results in a jump. While the jumping motion has been studied in depth, the physical mechanisms enabling fast unbending have not. Here, we first identify and quantify the phases of the clicking motion: latching, loading, and energy release. We detail the motion kinematics and investigate the governing dynamics (forces) of the energy release. We use high-speed synchrotron X-ray imaging to observe and analyze the motion of the hinge’s internal structures of four Elater abruptus specimens. We show evidence that soft cuticle in the hinge contributes to the spring mechanism through rapid recoil. Using spectral analysis and nonlinear system identification, we determine the equation of motion and model the beetle as a nonlinear single-degree-of-freedom oscillator. Quadratic damping and snap-through buckling are identified to be the dominant damping and elastic forces, respectively, driving the angular position during the energy release phase. The methods used in this study provide experimental and analytical guidelines for the analysis of extreme motion, starting from motion observation to identifying the forces causing the movement. The tools demonstrated here can be applied to other organisms to enhance our understanding of the energy storage and release strategies small animals use to achieve extreme accelerations repeatedly.

Noncircular Chainrings Do Not Influence Physiological Responses During Submaximal Cycling

Pedal speed and mechanical power output account for 99% of metabolic cost during submaximal cycling. Noncircular chainrings can alter instantaneous crank angular velocity and thereby pedal speed. Reducing pedal speed during the portion of the cycle in which most power is produced could reduce metabolic cost and increase metabolic efficiency. Purpose: To determine the separate contributions of pedal speed and chainring shape/eccentricity to the metabolic cost of producing power and evaluate joint-specific kinematics and kinetics during submaximal cycling across 3 chainring eccentricities (CON = 1.0; LOW = 1.13; HIGH = 1.24). Methods: Eight cyclists performed submaximal cycling at power outputs eliciting 30%, 60%, and 90% of their individual lactate threshold at pedaling rates of 80 rpm under each chainring condition (CON80rpm; LOW80rpm; HIGH80rpm) and at pedaling rates for the CON chainring chosen to match pedal speeds of the noncircular chainrings (CON78rpm to LOW80rpm; CON75rpm to HIGH80rpm). Physiological measures, metabolic cost, and gross efficiency were determined by indirect calorimetry. Pedal and joint-specific powers were determined using pedal forces and limb kinematics. Results: Physiological and metabolic measures were not influenced by eccentricity and pedal speed (all Ps > .05). Angular velocities produced during knee and hip extension were lower with the HIGH80rpm condition compared with the CON80rpm condition (all Ps < .05), while angular velocity produced during ankle plantar flexion remained unchanged. Conclusions: Despite the noncircular chainrings imposing their eccentricity on joint angular kinematics, they did not reduce metabolic cost or increase gross efficiency. Our results suggest that noncircular chainrings neither improve nor compromise submaximal cycling performance in trained cyclists.

Timing precision in fly flight control: integrating mechanosensory input with muscle physiology

Animals rapidly collect and act on incoming information to navigate complex environments, making the precise timing of sensory feedback critical in the context of neural circuit function. Moreover, the timing of sensory input determines the biomechanical properties of muscles that undergo cyclic length changes, as during locomotion. Both of these issues come to a head in the case of flying insects, as these animals execute steering manoeuvres at timescales approaching the upper limits of performance for neuromechanical systems. Among insects, flies stand out as especially adept given their ability to execute manoeuvres that require sub-millisecond control of steering muscles. Although vision is critical, here I review the role of rapid, wingbeat-synchronous mechanosensory feedback from the wings and structures unique to flies, the halteres. The visual system and descending interneurons of the brain employ a spike rate coding scheme to relay commands to the wing steering system. By contrast, mechanosensory feedback operates at faster timescales and in the language of motor neurons, i.e. spike timing, allowing wing and haltere input to dynamically structure the output of the wing steering system. Although the halteres have been long known to provide essential input to the wing steering system as gyroscopic sensors, recent evidence suggests that the feedback from these vestigial hindwings is under active control. Thus, flies may accomplish manoeuvres through a conserved hindwing circuit, regulating the firing phase—and thus, the mechanical power output—of the wing steering muscles.

Development of a Feedback System to Control Power in Cycling

Here we seek to control mechanical power output in outdoor cycling by adjusting commanded cadence of a cyclist. To understand cyclist’s dynamic behavior, we had one participant match their cadence to a range of commanded cadences. We then developed a mathematical model that predicts the actual mechanical power as a function of commanded cadence. The average absolute error between the predicted power of our model and the actual power was 15.9 ± 11.7%. We used this model to simulate our closed-loop controller and optimize for proportional and integral controller gains. With these gains in outdoor cycling experiments, the average absolute error between the target and the actual power was 3.2 ± 1.2% and the average variability in power was 2.9 ± 1.3%. The average responsiveness, defined as the required time for the actual power to reach 95% of the target power following changes in target power, was 7.4 ± 2.0 s.