Inter-individual differences in stride frequencies during running obtained from wearable data.

The purpose of the present study was to identify factors that underlie differences among runners in stride frequency (SF) as a function of running speed. Participants (N = 256; 85.5% males and 14.5% females; 44.1 ± 9.8 years; 181.4 ± 8.4 cm; 75.3 ± 10.6 kg; mean ± SD) shared their wearable data (Garmin Inc). Individual datasets were filtered to obtain representative relationships between stride frequency (SF) and speed per individual, representing in total 16.128 h of data. The group relationship between SF (72.82 to 94.73 strides · min-1) and running speed (V) (from 1.64 to 4.68 m · s-1) was best described with SF = 75.01 + 3.006 V. A generalised linear model with random effects was used to determine variables associated with SF. Variables and their interaction with speed were entered in a stepwise forward procedure. SF was negatively associated with leg length and body mass and an interaction of speed and age indicated that older runners use higher SF at higher speed. Furthermore, run frequency and run duration were positively related to SF. No associations were found with injury incidence, athlete experience or performance. Leg length, body mass, age, run frequency and duration were associated with SFs at given speeds. KEY POINTS On a group level, stride frequency can be described as a linear function of speed: SF (strides · min-1) = 75.01+ 3.006·speed (m · s-1) within the range of 1.64 to 4.68 m · s-1. On an individual level, the SF-speed relation is best described with a second order polynomial. Leg length and body mass were positively related to stride frequency while age was negatively related to stride frequency. Run frequency and run duration were positively related to stride frequency, while running experience, performance and injury incidence were unrelated.

An adaptive, real-time cadence algorithm for unconstrained sensor placement.

This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods. 22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s-1), running (3.4 m s-1), and sprinting (5.2 and 5.7 m s-1), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm. The described algorithm resulted in consistent numbers of true positives (85.6-100.0%) and false positives (0.0-2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ±â€¯SD: 98.9 ±â€¯0.2%), compared to conventional cross-correlation (87.3 ±â€¯13.5%), biased (73.0 ±â€¯16.2) and unbiased (82.2 ±â€¯20.6) autocorrelation procedures. This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions.