The energy-sparing spring theory of the foot’s arch has become central to interpretations of the foot’s mechanical function and evolution. Using a novel insole technique that restricted compression of the foot’s longitudinal arch, this study provides the first direct evidence that arch compression/recoil during locomotion contributes to lowering energy cost. Restricting arch compression near maximally (~80%) during moderate-speed (2.7 ms⁻¹) level running increased metabolic cost by + 6.0% (p < 0.001, d = 0.67; unaffected by foot strike technique). A simple model shows that the metabolic energy saved by the arch is largely explained by the passive-elastic work it supplies that would otherwise be done by active muscle. Both experimental and model data confirm that it is the end-range of arch compression that dictates the energy-saving role of the arch. Restricting arch compression had no effect on the cost of walking or incline running (3°), commensurate with the smaller role of passive-elastic mechanics in these gaits. These findings substantiate the elastic energy-saving role of the longitudinal arch during running and suggest that arch supports used in some footwear and orthotics may increase the cost of running.

Objective: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and wider deployment. An informative next step towards this goal would be a new method to obtain ground kinetics in the field. Methods: Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict in-game near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. Results: The proposed deep learning workbench achieved correlations to ground truth, by maximum discrete GRF component, of vertical Fz 0.97, anterior Fy 0.96 (both running), and lateral Fx 0.87 (sidestepping), with the strongest mean recorded across GRF components 0.89, and for GRM 0.65 (both sidestepping). Conclusion: These best-case correlations indicate the plausibility of the approach although the range of results was disappointing. The goal to accurately estimate near real-time on-field GRF/M will be improved by the lessons learned in this study. Significance: Coaching, medical, and allied health staff could ultimately use this technology to monitor a range of joint loading indicators during game play, with the aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings—the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.

Elastic energy returned from passive-elastic structures of the lower limb is fundamental in lowering the mechanical demand on muscles during running. The purpose of this study was to investigate the two length-modulating mechanisms of the plantar fascia, namely medial longitudinal arch compression and metatarsophalangeal joint (MPJ) excursion, and to determine how these mechanisms modulate strain, and thus elastic energy storage/return of the plantar fascia during running. Eighteen runners (9 forefoot and 9 rearfoot strike) performed three treadmill running trials; unrestricted shod, shod with restricted arch compression (via an orthotic-style insert), and barefoot. Three-dimensional motion capture and ground reaction force data were used to calculate lower limb kinematics and kinetics including MPJ angles, moments, powers and work. Estimates of plantar fascia strain due to arch compression and MPJ excursion were derived using a geometric model of the arch and a subject-specific musculoskeletal model of the plantar fascia, respectively. The plantar fascia exhibited a typical elastic stretch-shortening cycle with the majority of strain generated via arch compression. This strategy was similar in fore- and rear-foot strike runners. Restricting arch compression, and hence the elastic-spring function of the arch, was not compensated for by an increase in MPJ-derived strain. In the second half of stance the plantar fascia was found to transfer energy between the MPJ (energy absorption) and the arch (energy production during recoil). This previously unreported energy transfer mechanism reduces the strain required by the plantar fascia in generating useful positive mechanical work at the arch during running.

Nvolvement: foot-up (FU), foot-back (FB), and minimal leg drive (ARM), as performed by 12 high-performance male players were recorded using a 12-camera Vicon MX motion analysis system operating at 250 Hz. A discriminant analysis determined the lower-limb kinematics that distinguished serve technique, and by extension, leg drive. A total of 18 one-way ANOVA ascertained statistically significant differences in the kinematic and kinetic variables considered to relate to or represent shoulder joint loading in FU, FB, and ARM serves. Results: The lower-limb kinematics shown to best discriminate between service techniques were range of rear and front knee joint extension, and peak angular velocity of rear knee joint extension. The forward swings of the FU (43.6 ± 3.0 m·s−1, P < 0.05) and FB (42.6 ± 3.1 m·s−1, P < 0.05) techniques were characterized by higher peak racket speeds than those generated in the ARM (39.4 ± 3.4 m·s−1) serve. Regardless of stance and leg drive, similar pre- and postimpact shoulder joint kinetics were developed. Conclusion: Knowledge of a server's range of front and rear knee joint extension as well as his/her peak angular velocity of rear knee joint extension is sufficient to ascertain the stance and quality of leg drive used. When facilitated by a leg drive, high-performance players generate similar resultant preimpact racket velocities independent of stance. With no leg drive, players develop lower resultant racket velocities. Comparable shoulder joint kinetics, however, evolved from the differential lower-limb mechanics that characterized the FU, FB, and ARM techniques....

The accurate prediction of three-dimensional (3-D) ground reaction forces and moments (GRF/Ms) outside the laboratory setting would represent a watershed for on-field biomechanical analysis. To extricate the biomechanist's reliance on ground embedded force plates, this study sought to improve on an earlier partial least squares (PLS) approach by using deep learning to predict 3-D GRF/Ms from legacy marker based motion capture sidestepping trials, ranking multivariate regression of GRF/Ms from five convolutional neural network (CNN) models. In a possible first for biomechanics, tactical feature engineering techniques were used to compress space-time and facilitate fine-tuning from three pretrained CNNs, from which a model derivative of ImageNet called “CaffeNet” achieved the strongest average correlation to ground truth GRF/Ms _r (F _mean ) 0.9881 and _r (M _mean ) 0.9715 ( _r RMSE 4.31 and 7.04%). These results demonstrate the power of CNN models to facilitate real-world multivariate regression with practical application for spatio-temporal sports analytics.