Temporal patterns in running

Saito, M.; Kobayashi, Konomi; Miyashita, Mafumi; Hoshikawa, Tamotsu

doi:10.1007/978-1-349-02612-8_15

Cited by 13 publications

(11 citation statements)

References 3 publications

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“…First, we extracted several parameters (p j ) for each step, which were later used as inputs for the speed estimation model. As several studies reported on the association between the changes in the duration of the gait phases and the running speed (Högberg, 1952 ; Saito et al, 1974 ; Nummela et al, 2007 ), we computed the ground contact time (CT), the flight time (FLT), the swing time (SWT), the step duration (STP), and the stride duration (STR) for each step i, where i = 1…N, and N is the total number of steps (Equations 1–5).…”

Section: Methodsmentioning

confidence: 99%

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Falbriard

Soltani

Aminian

2021

Front. Sports Act. Living

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The overground speed is a key component of running analysis. Today, most speed estimation wearable systems are based on GNSS technology. However, these devices can suffer from sparse communication with the satellites and have a high-power consumption. In this study, we propose three different approaches to estimate the overground speed in running based on foot-worn inertial sensors and compare the results against a reference GNSS system. First, a method is proposed by direct strapdown integration of the foot acceleration. Second, a feature-based linear model and finally a personalized online-model based on the recursive least squares' method were devised. We also evaluated the performance differences between two sets of features; one automatically selected set (i.e., optimized) and a set of features based on the existing literature. The data set of this study was recorded in a real-world setting, with 33 healthy individuals running at low, preferred, and high speed. The direct estimation of the running speed achieved an inter-subject mean ± STD accuracy of 0.08 ± 0.1 m/s and a precision of 0.16 ± 0.04 m/s. In comparison, the best feature-based linear model achieved 0.00 ± 0.11 m/s accuracy and 0.11 ± 0.05 m/s precision, while the personalized model obtained a 0.00 ± 0.01 m/s accuracy and 0.09 ± 0.06 m/s precision. The results of this study suggest that (1) the direct estimation of the velocity of the foot are biased, and the error is affected by the overground velocity and the slope; (2) the main limitation of a general linear model is the relatively high inter-subject variance of the bias, which reflects the intrinsic differences in gait patterns among individuals; (3) this inter-subject variance can be nulled using a personalized model.

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Section: Methodsmentioning

confidence: 99%

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Falbriard

Soltani

Aminian

2021

Front. Sports Act. Living

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“…For any given running speed, it is generally known that metabolic effi ciency is optimized through one specifi c combination of SL and SR. Clearly, individuals do modify their running styles. Saito et al [27] showed that trained runners increased their speed to 7 m.s − 1 by lengthening their stride, whereas untrained runners increased SL only up to 5.5 m.s − 1 ; any further increase in running speed was achieved primarily by increasing SR. In the same way, Nelson and Gregor [22] observed that a group of distance runners shortened their strides at a given speed by an average of 7 cm during the 4 year of their varsity careers.…”

Section: Stride Rate Measurementmentioning

confidence: 99%

“…The simple process of shortening or lengthening the stride has an important eff ect on all the active musculature. Each muscle is forced to work on a slightly diff erent region of its force-velocity curve and, as a consequence, changes in effi ciency can be anticipated [27] . Thus, the RS800sd could be used during training to accelerate the adoption of a higher SR for a given speed by providing instantaneous bio-feedback to the athlete.…”

Section: Stride Rate Measurementmentioning

confidence: 99%

Accuracy and Repeatability of the Polar^®RS800sd to Evaluate Stride Rate and Running Speed

Hausswirth¹,

Meur²,

Couturier³

et al. 2009

Int J Sports Med

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The purpose of this study was to evaluate the accuracy and the repeatability of a new running computer system (RS800sd, Polar, Kempele, Finland) which included the measurement of running speed (RS) and stride rate (SR). Eight well-trained triathletes participated in this study. First, they completed an incremental continuous maximum test on a treadmill (from 12 km x h (-1) to 18 km x h (-1)) at 0% grade. Then the subjects took part in a second test to determine RS800sd intra-reproducibility to evaluate running speed. They ran twice during 5 min at a pace corresponding to their maximal lactate steady-state. During these two tests, RS and SR were recorded by the RS800sd system, by an optical sensor system (for RS) and a force-sensitive device (for SR). No difference was found between the RS800sd system and the reference systems both for RS (ICC=0.95) and SR (ICC=0.69). Moreover RS measures were statistically repeatable. This study provided evidence for the validity of the RS800sd system for measuring the kinematic characteristics of running (speed and frequency). Further investigations are needed to replicate these findings at lower running speeds, notably during walking to assess its capacity to evaluate physical activity in natural conditions.

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“…Just as speed affects the technique of walking and running (45), it is likely to affect the biomechanics of load carriage. Locomotion speeds during testing were visually cued at 1.1, 1.3, and 1.5 m/s by the specially designed device placed alongside the volunteer.…”

Section: Experimental Procedures Independent Variablementioning

confidence: 99%

The Effects of Walking Speed on the Biomechanics of Backpack Load Carriage

Harman¹,

Han²,

Frykman³

et al. 2000

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Temporal patterns in running

Cited by 13 publications

References 3 publications

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Accuracy and Repeatability of the Polar^®RS800sd to Evaluate Stride Rate and Running Speed

The Effects of Walking Speed on the Biomechanics of Backpack Load Carriage

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Temporal patterns in running

Cited by 13 publications

References 3 publications

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Running Speed Estimation Using Shoe-Worn Inertial Sensors: Direct Integration, Linear, and Personalized Model

Accuracy and Repeatability of the Polar®RS800sd to Evaluate Stride Rate and Running Speed

The Effects of Walking Speed on the Biomechanics of Backpack Load Carriage

Contact Info

Product

Resources

About

Accuracy and Repeatability of the Polar^®RS800sd to Evaluate Stride Rate and Running Speed