An experimental feasibility study evaluating the adequacy of sportswear-type wearables for recording physical exercise intensity

Sportswear-type wearables with integrated inertial sensors and electrocardiogram (ECG) electrodes, have been developed. We examined the feasibility of using sportswear-type wearables to evaluate exercise intensity within a controlled laboratory setting. Six male college athletes were asked to don a sportswear-type wearable while performing a treadmill test that reached up to 20 km/h. The magnitude of the filtered tri-axial acceleration signal, recorded by the inertial sensor, was used to calculate the acceleration index. The R-R intervals of ECG were used to determine heart rate; the external validity of the heart rate was then evaluated according to oxygen uptake, which is the gold standard physiological exercise intensity. Single regression analyses between treadmill speed and the acceleration index in each participant showed that the slope of the regression line was significantly greater than zero with a high coefficient of determination (walking, 0.95; jogging, 0.96; running, 0.90). Another single regression analyses between heart rate and oxygen uptake showed that the slope of the regression line was significantly greater than zero, with a high coefficient of determination (0.96). Together, these results indicate that sportswear-type wearables are a feasible technology for evaluating physical and physiological exercise intensity across a wide range of physical activities and sport performances.

Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells

Exercise is good for health, quality of life, and maintenance of human muscles. Dumbbells are popular indoor exercise equipment with several benefits such as low cost, high flexibility in space and time, easy operation, and suitability for people of all ages. Facilitated by advances in the Internet of Things, smart dumbbells that provide automatic counting and motion monitoring functions have been developed. To perform these tasks, the key process is identification of exercise mode. This study proposes a method to identify essential muscle groups’ (biceps, triceps, and deltoids) exercise modes of a dumbbell using an inertial measurement unit to provide three-axis angular velocities and accelerations. The motion angles were estimated from the axial acceleration and angular velocity. Phase diagrams and time plots of the axial angle, angular velocity, and acceleration were used to extract significant features of each exercise. Machine Learning and weighting functions were developed to combine these features into an identification index value for accurate identification and classification of the exercise modes. An algorithm was developed to verify the exercise mode identification. The results show that the proposed method and weighting function can successfully identify the six exercise modes. The identification algorithm was 99.5% accurate. The exercise mode identification of the dumbbell is confirmed.

Digging into the behaviour of an active hunting predator: arctic fox prey caching events revealed by accelerometry

Background Biologging now allows detailed recording of animal movement, thus informing behavioural ecology in ways unthinkable just a few years ago. In particular, combining GPS and accelerometry allows spatially explicit tracking of various behaviours, including predation events in large terrestrial mammalian predators. Specifically, identification of location clusters resulting from prey handling allows efficient location of killing events. For small predators with short prey handling times, however, identifying predation events through technology remains unresolved. We propose that a promising avenue emerges when specific foraging behaviours generate diagnostic acceleration patterns. One such example is the caching behaviour of the arctic fox (Vulpes lagopus), an active hunting predator strongly relying on food storage when living in proximity to bird colonies. Methods We equipped 16 Arctic foxes from Bylot Island (Nunavut, Canada) with GPS and accelerometers, yielding 23 fox-summers of movement data. Accelerometers recorded tri-axial acceleration at 50 Hz while we obtained a sample of simultaneous video recordings of fox behaviour. Multiple supervised machine learning algorithms were tested to classify accelerometry data into 4 behaviours: motionless, running, walking and digging, the latter being associated with food caching. Finally, we assessed the spatio-temporal concordance of fox digging and greater snow goose (Anser caerulescens antlanticus) nesting, to test the ecological relevance of our behavioural classification in a well-known study system dominated by top-down trophic interactions. Results The random forest model yielded the best behavioural classification, with accuracies for each behaviour over 96%. Overall, arctic foxes spent 49% of the time motionless, 34% running, 9% walking, and 8% digging. The probability of digging increased with goose nest density and this result held during both goose egg incubation and brooding periods. Conclusions Accelerometry combined with GPS allowed us to track across space and time a critical foraging behaviour from a small active hunting predator, informing on spatio-temporal distribution of predation risk in an Arctic vertebrate community. Our study opens new possibilities for assessing the foraging behaviour of terrestrial predators, a key step to disentangle the subtle mechanisms structuring many predator–prey interactions and trophic networks.

The effect of horseshoes and surfaces on horse and jockey centre of mass displacements at gallop

Horseshoes influence how horses’ hooves interact with different ground surfaces, during the impact, loading and push-off phases of a stride cycle. Consequently, they impact on the biomechanics of horses’ proximal limb segments and upper body. By implication, different shoe and surface combinations could drive changes in the magnitude and stability of movement patterns in horse-jockey dyads. This study aimed to quantify centre of mass (COM) displacements in horse-jockey dyads galloping on turf and artificial tracks in four shoeing conditions: 1) aluminium; 2) barefoot; 3) GluShu; and 4) steel. Thirteen retired racehorses and two jockeys at the British Racing School were recruited for this intervention study. Tri-axial acceleration data were collected close to the COM for the horse (girth) and jockey (kidney-belt), using iPhones (Apple Inc.) equipped with an iOS app (SensorLog, sample rate = 50 Hz). Shoe-surface combinations were tested in a randomized order and horse-jockey pairings remained constant. Tri-axial acceleration data from gallop runs were filtered using bandpass Butterworth filters with cut-off frequencies of 15 Hz and 1 Hz, then integrated for displacement using Matlab. Peak displacement was assessed in both directions (positive ‘maxima’, negative ‘minima’) along the cranio-caudal (CC, positive = forwards), medio-lateral (ML, positive = right) and dorso-ventral (DV, positive = up) axes for all strides with frequency ≥2 Hz (mean = 2.06 Hz). Linear mixed-models determined whether surfaces, shoes or shoe-surface interactions (fixed factors) significantly affected the displacement patterns observed, with day, run and horse-jockey pairs included as random factors; significance was set at p<0.05. Data indicated that surface-type significantly affected peak COM displacements in all directions for the horse (p<0.0005) and for all directions (p≤0.008) but forwards in the jockey. The largest differences were observed in the DV-axis, with an additional 5.7 mm and 2.5 mm of downwards displacement for the horse and jockey, respectively, on the artificial surface. Shoeing condition significantly affected all displacement parameters except ML-axis minima for the horse (p≤0.007), and all displacement parameters for the jockey (p<0.0005). Absolute differences were again largest vertically, with notable similarities amongst displacements from barefoot and aluminium trials compared to GluShu and steel. Shoe-surface interactions affected all but CC-axis minima for the jockey (p≤0.002), but only the ML-axis minima and maxima and DV-axis maxima for the horse (p≤0.008). The results support the idea that hoof-surface interface interventions can significantly affect horse and jockey upper-body displacements. Greater sink of hooves on impact, combined with increased push-off during the propulsive phase, could explain the higher vertical displacements on the artificial track. Variations in distal limb mass associated with shoe-type may drive compensatory COM displacements to minimize the energetic cost of movement. The artificial surface and steel shoes provoked the least CC-axis movement of the jockey, so may promote greatest stability. However, differences between horse and jockey mean displacements indicated DV-axis and CC-axis offsets with compensatory increases and decreases, suggesting the dyad might operate within displacement limits to maintain stability. Further work is needed to relate COM displacements to hoof kinematics and to determine whether there is an optimum configuration of COM displacement to optimise performance and minimise injury.

Dynamic analysis and vibration reduction of mechanical-hydraulic coupled tunnel boring machine (TBM) main drive system

Tunnel Boring Machine always works in the changeable geologies with multiple drivers, which leads to severe vibration of the TBM main drive system and key component failures. The vibration characteristics of TBM under different working conditions and the vibration reduction analysis have important meanings. First of all, by considering the time-varying random loads of the cutters, the contact force of the gears, the stiffness of the main bearing, and the stiffness of the cylinders, a mechanical-hydraulic coupling nonlinear dynamic model of the TBM main drive system was built according to the assembly relationship and load transmission path of the main drive system. Secondly, the dynamic model of the TBM main drive system is verified by comparing the theoretical vibration with the real vibration of the TBM main drive system. The error of the vibration acceleration is 10% to 30%. Three typical loads are defined under typical working conditions, and the vibrations of the TBM main drive system under three typical loads were analyzed. Finally, the sensitivity analysis of the cylinder damping shows that the damping at the position of the propulsion cylinder has a great influence on the vibration of the TBM main drive system. The results show that when the damping coefficient is 2.5 × 106 N·s/m, the maximum reduction of axial acceleration of cutterhead is 0.64 g, and that of the main beam front section is 0.55 g. The variable damping coefficient vibration reduction strategies under three typical loads are verified.

Utilizing High-Frequency In-Bit Sensor Data Improves Drillbit Design and Modelling

Modern drill bits designs have become more efficient using static modelling, and in more advanced cases, time-based dynamic modelling. These methods have created improved cutting structures that fail rock more effectively, however, at-bit vibrations are difficult to estimate because of the high-frequency nature of the vibration and its proximity to typical vibration sensors. In conventional applications, vibration is not measured near the bit. A solution to capture this data on conventional assemblies and use the data in an actual bit design is presented in this paper with subsequent performance and vibration results. The relative efficiency, bit dull grading, and vibration performance are compared across these designs and explored in depth. This new generation of vibration tool fits inside the bit pin, enabling accurate at-bit vibration measurements by a suite of sensors. The tool includes a tri-axis accelerometer that measures lateral and axial acceleration, and gyro sensors to measure rpm and torsional acceleration. Together, these outputs combine with the rig surface data to have time- and depth-based vibration data in the context of the run. When used to quantify the dynamic model, this represents a modelling calibration that improves bit design performance. The lower-vibration environment created by the new bit design enables the operator to run increased parameters with a lower likelihood for measurement-while-drilling (MWD) failures, motor failures, and premature catastrophic bit failures leading to faster run times and less nonproductive time (NPT). These results also prove that meaningful bit design changes can take place more frequently than through traditional means, translating value to the operator in the form more successful BHA improvements and less drilling time. Using the new in-bit sensor in a baseline design to start the design cycle, a baseline mechanical specific energy (MSE) and vibration model was developed foot-by-foot. The worst areas of vibration were seen as the bit became dull in the lower section of the drilling interval. A new dull bit model was created in parallel to capture this section of data. A new design was proposed to Whiting Petroleum to improve both sharp and dull efficiency and vibration, and subsequently run with sensor in an offset well.

Random Forest for Automatic Feature Importance Estimation and Selection for Explainable Postural Stability of a Multi-Factor Clinical Test

Falling is a common incident that affects the health of elder adults worldwide. Postural instability is one of the major contributors to this problem. In this study, we propose a supplementary method for measuring postural stability that reduces doctor intervention. We used simple clinical tests, including the timed-up and go test (TUG), short form berg balance scale (SFBBS), and short portable mental status questionnaire (SPMSQ) to measure different factors related to postural stability that have been found to increase the risk of falling. We attached an inertial sensor to the lower back of a group of elderly subjects while they performed the TUG test, providing us with a tri-axial acceleration signal, which we used to extract a set of features, including multi-scale entropy (MSE), permutation entropy (PE), and statistical features. Using the score for each clinical test, we classified our participants into fallers or non-fallers in order to (1) compare the features calculated from the inertial sensor data, and (2) compare the screening capabilities of the multifactor clinical test against each individual test. We use random forest to select features and classify subjects across all scenarios. The results show that the combination of MSE and statistic features overall provide the best classification results. Meanwhile, PE is not an important feature in any scenario in our study. In addition, a t-test shows that the multifactor test of TUG and BBS is a better classifier of subjects in this study.

Unsteady shock wave dynamics in accelerating and decelerating flight

Increasingly agile manoeuvre is an advantage in the flight of aircraft, missiles and aerial vehicles, but the principles of accelerating aerodynamics in the transonic regime are only now being fully investigated. This study contributes to the understanding of shock and separation effects on drag during axial acceleration, using a simple geometric configuration. Unsteady shock wave behavior was numerically investigated for an axisymmetric cone-cylinder using a commercial solver and the Moving Reference Frame acceleration technique. This acceleration technique was validated using unsteady numerical and experimental methods. The cone-cylinder was accelerated from Mach number 0.6 to Mach number 1.2 at 100g constant and deceleration was from Mach number 1.2 until Mach number 0.6 at –100g constant. Three cone angles were tested for the cone-cylinder with uniform cylinder diameter. Acceleration through the transonic Mach regime was characterised by a delayed and gradual shock wave development when compared to steady state, demonstrating a clear flow history effect. Deceleration through the transonic Mach regime was characterised by shock wave propagation from the base to the nose. New flow structures appeared during deceleration that do not have counterparts in the steady state, including shock interactions and propagating expansion-compression features. Gross changes in the unsteady drag coefficient curves for each cone-angle are explained with reference to unsteady shock wave behaviour for accelerating and decelerating motion.

Validating Star-Oddi heart rate and acceleration data storage tags for use in Atlantic salmon (Salmo salar)

Background Data storage tags (DSTs) record and store information about animals and their environment, and can provide important data relevant to fish culture, ecology and conservation. A DST has recently been developed that records heart rate (fH), electrocardiograms (ECGs), tri-axial acceleration and temperature. However, at the time of this study, no research using these tags had been performed on fish or determined the quality of the data collected. Thus, our research asked: do these DSTs provide reliable and meaningful data? To examine this question, Atlantic salmon (1.4 ± 0.7 kg) were implanted with DSTs, then swam at increasing speeds in a swim tunnel after 1 week of recovery. Further, in two separate experiments, salmon (2.4 ± 0.1 kg) were implanted with DSTs and held in a large tank with conspecifics for 1 week at 11 °C or 6 weeks at 8–12 °C. Results External acceleration (EA) and variation in EA (VAR) increased exponentially with swimming speed and tail beat frequency. The quality index (QI) assigned to ECG recordings (where QI0 means very good quality, and QI1, QI2 and QI3 are of reduced quality) did not change significantly with increasing swimming speed (QI0 ~ 60–80%). However, we found that the accuracy of the tag algorithm in estimating fH from ECGs was reduced when QI>0. Diurnal patterns of fH and EA were evident from the time the salmon were placed in the tank. Heart rate appeared to stabilize by ~ 4 days post-surgery in the first experiment, but extended holding showed that fH declined for 2–3 weeks. During extended holding, the tag had difficulty recording low fH values < 30 bpm, and for this reason, in addition to the fact that the algorithm can miscalculate fH, it is highly recommended that ECGs be saved when possible for quality control and so that fH values with QI>0 can be manually calculated. Conclusions With these DSTs, parameters of acceleration can be used to monitor the activity of free-swimming salmon. Further, changes in fH and heart rate variability (HRV) due to diurnal rhythms, and in response to temperature, activity and stressors, can be recorded.