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How extinct, non-avian theropod dinosaurs moved is a subject of considerable interest and controversy. A better understanding of non-avian theropod locomotion can be achieved by better understanding terrestrial locomotor biomechanics in their modern descendants, birds. Despite much research on the subject, avian terrestrial locomotion remains little explored in regards to how kinematic and kinetic factors vary together with speed and body size. Here, terrestrial locomotion was investigated in twelve species of ground-dwelling bird, spanning a 1,780-fold range in body mass, across almost their entire speed range. Particular attention was devoted to the ground reaction force (GRF), the force that the feet exert upon the ground. Comparable data for the only other extant obligate, striding biped, humans, were also collected and studied. In birds, all kinematic and kinetic parameters examined changed continuously with increasing speed, while in humans all but one of those same parameters changed abruptly at the walk-run transition. This result supports previous studies that show birds to have a highly continuous locomotor repertoire compared to humans, where discrete 'walking' and 'running' gaits are not easily distinguished based on kinematic patterns alone. The influences of speed and body size on kinematic and kinetic factors in birds are developed into a set of predictive relationships that may be applied to extinct, non-avian theropods. The resulting predictive model is able to explain 79-93% of the observed variation in kinematics and 69-83% of the observed variation in GRFs, and also performs well in extrapolation tests. However, this study also found that the location of the whole-body centre of mass may exert an important influence on the nature of the GRF, and hence some caution is warranted, in lieu of further investigation.

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.

Active video games that require physical exertion during game play have been shown to confer health benefits. Typically, energy expended during game play is measured using devices attached to players, such as accelerometers, or portable gas analyzers. Since 2010, active video gaming technology incorporates marker-less motion capture devices to simulate human movement into game play. Using the Kinect Sensor and Microsoft SDK this research aimed to estimate the mechanical work performed by the human body and estimate subsequent metabolic energy using predictive algorithmic models. Nineteen University students participated in a repeated measures experiment performing four fundamental movements (arm swings, standing jumps, body-weight squats, and jumping jacks). Metabolic energy was captured using a Cortex Metamax 3B automated gas analysis system with mechanical movement captured by the combined motion data from two Kinect cameras. Estimations of the body segment properties, such as segment mass, length, centre of mass position, and radius of gyration, were calculated from the Zatsiorsky-Seluyanov's equations of de Leva, with adjustment made for posture cost. GPML toolbox implementation of the Gaussian Process Regression, a locally weighted k-Nearest Neighbour Regression, and a linear regression technique were evaluated for their performance on predicting the metabolic cost from new feature vectors. The experimental results show that Gaussian Process Regression outperformed the other two techniques by a small margin. This study demonstrated that physical activity energy expenditure during exercise, using the Kinect camera as a motion capture system, can be estimated from segmental mechanical work. Estimates for high-energy activities, such as standing jumps and jumping jacks, can be made accurately, but for low-energy activities, such as squatting, the posture of static poses should be considered as a contributing factor. When translated into the active video gaming environment, the results could be incorporated into game play to more accurately control the energy expenditure requirements.