Various publications discuss the discrepancies of running in triathlons and stand-alone runs. However, those methods, such as analysing step-characteristics or ground-contact time, lack the ability to quantitatively discriminate between subtle running differences. The attractor method can be applied to overcome those shortcomings. The purpose was to detect differences in athletes' running patterns (δM) and movement precision (δD) by comparing a 5,000 m run after a prior cycling session (TRun) with an isolated run over the same distance (IRun). Participants completed the conditions on a track and a stationary trainer, allowing the use of their personal bike to simulate an Olympic triathlon. During each run, three-dimensional acceleration data, using sensors attached to the ankles, were collected. Results showed that both conditions lead to elevated attractor parameters (δM and δD) over the initial five minutes before the athletes found their rhythm. This generates a new perspective because independent of running after a bike session or without preload, an athlete needs certain time to adjust to the running movement. Coaches must consider this factor as another tool to fine-tune pacing and performance. Moreover, the attractor method is a novel approach to gain deeper insight into human cyclic motions in athletic contexts.

Literature mentions two types of models describing cyclic movement-theory and data driven. Theory driven models include anatomical and physiological aspects. They are principally suitable for answering questions about the reasons for movement characteristics, but they are complicated and substantial simplifications do not allow generally valid results. Data driven models allow answering specific questions, but lack the understanding of the general movement characteristic. With this paper we try a compromise without having to rely on anatomy, neurology and muscle function. We hypothesize a general kinematic description of cyclic human motion is possible without having to specify the movement generating processes, and still get the kinematics right. The model proposed consists of a superposition of six contributions-subject's attractor, morphing, short time fluctuation, transient effect, control mechanism and sensor noise, while characterizing numbers and random contributions. We test the model with data from treadmill running and stationary biking. Applying the model in a simulation results in good agreement between measured data and simulation values. We find in all our cases the similarity analysis between measurement and simulation is best for the same subjects-d same sub run > 55% and d same sub bike > 64%. All comparisons between different subjects are 51% > d different sub run and 52% > d different sub bike. This uniquely allows for the identification of each measurement for the associated simulation. However, even different subject comparisons show good agreement between measurement and simulation results of differences δ run = 6.7±4.7% and δ bike = 5.1±4.5%.

Models describing cyclic movement can roughly be divided into the categories theory or data driven. Theory driven models include anatomical and physiological aspects. They are principally suitable for answering questions about the reasons for movement characteristics. But, they are complicated and substantial simplifications do not allow generally valid results. Data driven models allow answering specific questions but lack the understanding of the general movement characteristic. With this paper we try a compromise not having to rely on anatomy, neurology and muscle function. We hypothesize a general kinematic description of cyclic human motion is possible without having to specify the movement generating processes, and still getting the kinematic right. The model proposed consisting of a superposition of six contributions -subject's attractor, morphing, short time fluctuation, transient effect, control mechanism and sensor noise -, with characterizing numbers and random contributions. We test the model with data form treadmill running and stationary biking. Applying the model in form of a simulation results in good agreement between measured data and simulation values.

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person’s identification, in, for example, the growing e-sports movement.

While training and competing as a runner, athletes often sense an unsteady feeling during the first meters on the road. This sensation, termed as transient effect, disappears after a short period as the runners approach their individual running rhythm. The foundation of this work focuses on the detection and quantification of this phenomenon. Thirty athletes ran two sessions over 60 min on a treadmill at moderate speed. Three-dimensional acceleration data were collected using two MEMS sensors attached to the lower limbs. By using the attractor method and Fourier transforms, the transient effect was isolated from noise and further components of human cyclic motion. A substantial transient effect was detected in 81% of all measured runs. On average, the transient effect lasted 5.25 min with a range of less than one minute to a maximum of 31 min. A link to performance data such as running level, experience and weekly training hours could not be found. The presented work provides the methodological basis to detect and quantify the transient effect at moderate running speeds. The acquisition of further physical or metabolic performance data could provide more detailed information about the impact of the transient effect on athletic performance.