Validity of a trunk‐mounted accelerometer to assess peak accelerations during walking, jogging and running

Wundersitz, Daniel; Gastin, Paul B.; Richter, Chris; Robertson, Sam; Netto, Kevin

doi:10.1080/17461391.2014.955131

Cited by 72 publications

(69 citation statements)

References 58 publications

(76 reference statements)

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“…Accelerometers have demonstrated excellent reliability in both mechanical and field settings [5,19]. However, recent research has questioned the validity of accelerometers to accurately assess peak impacts in team sports, with accelerometers overestimating concurrently obtained measures [19,30,33,34]. It is well established that measures of human movement can be contaminated with noise [1], contributing to the inaccuracies found.…”

Section: Introduction ▼mentioning

confidence: 99%

Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements

Wundersitz¹,

Gastin²,

Robertson

et al. 2015

Int J Sports Med

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This study assessed the validity of an accelerometer to measure impacts in team sports. 76 participants completed a team sport circuit. Accelerations were collected concurrently at 100 Hz using an accelerometer and a 36-camera motion analysis system. The largest peak accelerations per movement were compared in 2 ways: i) pooled together and filtered at 13 different cut-off frequencies (range 6-25 Hz) to identify the optimal filtering frequency, and ii) the optimal cut-off frequency split into the 7 movements performed (n=532). Raw and 25-16 Hz filtering frequencies significantly overestimated and 6 Hz underestimated motion analysis peak accelerations (P <0.007). The 12 Hz filtered accelerometer data revealed the strongest relationship with motion analysis data (accuracy - 0.01±0.27 g, effect size - 0.01, agreement - 0.55 to 0.53 g, precision 0.27 g, and relative error 5.5%; P=1.00). The accelerometer underestimated peak accelerations during tackling and jumping, and overestimated during walking, jogging, sprinting and change of direction. Lower agreement and reduced precision were associated with sprinting, jumping and tackling. The accelerometer demonstrated an acceptable level of concurrent validity compared to a motion analysis system when filtered at a cut-off frequency of 12 Hz. The results advocate the use of accelerometers to measure movements in team sport.

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Section: Introduction ▼mentioning

confidence: 99%

Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements

Wundersitz¹,

Gastin²,

Robertson

et al. 2015

Int J Sports Med

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“…The effect of low-pass frequency filtering on the accuracy of the acceleration data has been investigated for an accelerometer attached at the upper back during walking and running on a treadmill using a camera motion analysis system as criterion method for acceleration. 24 This study demonstrated large measurement error with unfiltered raw acceleration data, while the highest accuracy was achieved by applying 8-10 Hz low-pass filter. However, these results are not directly applicable to waist acceleration data and do not explain differences between children and adults.…”

Section: F I G U R E 3 Replication Of Thementioning

confidence: 71%

“…None of these approaches use a low‐pass filter to attenuate irrelevant acceleration signals. The effect of low‐pass frequency filtering on the accuracy of the acceleration data has been investigated for an accelerometer attached at the upper back during walking and running on a treadmill using a camera motion analysis system as criterion method for acceleration . This study demonstrated large measurement error with unfiltered raw acceleration data, while the highest accuracy was achieved by applying 8‐10 Hz low‐pass filter.…”

Section: Processing Of Acceleration Datamentioning

confidence: 99%

Re‐examination of accelerometer data processing and calibration for the assessment of physical activity intensity

Arvidsson

Fridolfsson

Börjesson

et al. 2019

Scandinavian Med Sci Sports

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This review re‐examines the use of accelerometer and oxygen uptake data for the assessment of activity intensity. Accelerometers capture mechanical work, while oxygen uptake captures the energy cost of this work. Frequency filtering needs to be considered when processing acceleration data. A too restrictive filter attenuates the acceleration signal for walking and, to a higher degree, for running. This measurement error affects shorter (children) more than taller (adults) individuals due to their higher movement frequency. Less restrictive filtering includes more movement‐related signals and provides measures that better capture mechanical work, but may include more noise. An optimal filter cut‐point is determined where most relevant acceleration signals are included. Further, accelerometer placement affects what part of mechanical work being captured. While the waist placement captures total mechanical work and therefore contributes to measures of activity intensity equivalent by age and stature, the thigh and wrist placements capture more internal work and do not provide equivalent measures. Value calibration of accelerometer measures is usually performed using measured oxygen uptake with the metabolic equivalent of task (MET) as reference measure of activity intensity. However, the use of MET is not stringent and is not a measure of activity intensity equivalent by age and stature. A candidate measure is the mass‐specific net oxygen uptake, VO2net (VO2tot − VO2stand). To improve measurement of physical activity intensity using accelerometers, research developments are suggested concerning the processing of accelerometer data, use of energy expenditure as reference for activity intensity, and calibration procedure with absolute versus relative intensity.

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“…8,15,17 However, it is well-established that the velocity extrema-based timings (method "b") will produce an overestimate of the flight time due to the extrema occurring prior to takeoff (maximum velocity) and after touchdown (minimum velocity). 8,15,[17][18][19][20] Due to the noise caused by the movement of the sensor relative to the body during takeoff and landing, 8,29 it is challenging to utilize the acceleration trace-based flight-time method (method "a"), and this approach remains scarcely explored in the literature. Due to the one-to-one proportionality between acceleration and force, one might expect a smaller bias against a contact mat with this method compared to the velocity extrema method for determining flight time.…”

Section: Introductionmentioning

confidence: 99%

Jump height from inertial recordings: A tutorial for a sports scientist

Rantalainen

Finni

Walker

2019

Scandinavian Med Sci Sports

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Jump performance provides meaningful information both for sporting and clinical needs. Current state of the art in jump performance assessment is laboratory-bound; however, out-of-the-laboratory methods are desirable. Therefore, the purposes of the present investigation were (a) to explore whether utilizing a novel analytical approach minimizes the bias between inertial recording unit (IMU)-based and jump mat-based jump height estimates and (b) to provide a thorough tutorial for a sport scientist (see Appendix S1) to facilitate standardization of jump height estimation.Forty-one women, men, and boys aged 6 to 77 years completed three maximal countermovement jumps without arm swing, which were concurrently registered with a jump mat, and an IMU worn in low lumbar region. Excellent agreement between the novel IMU-based jump height and jump mat jump height was observed (mean IMU 22.6 [8.3] cm, mean jump mat 22.7 [8.9], mean bias −0.1 cm [95% limits of agreement −4.5 cm to 4.4 cm; P = .826], and intra-class correlation coefficient 0.97 [95% CI 0.94 to 0.98, P < .001]). In conclusion, inertial recordings conducted with lightweight IMUs worn on the hip provide a valid and feasible assessment of jump height among people with varying athletic ability. Inertial signals have the potential to afford (at least semi-) automated analysis pipeline with low labor cost thus being potentially feasible in applied settings such as in professional sports or in the clinics. K E Y W O R D Saccelerometer, gyroscope, inertial measurement unit, performance, signal processing, wearable

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Validity of a trunk‐mounted accelerometer to assess peak accelerations during walking, jogging and running

Cited by 72 publications

References 58 publications

Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements

Validation of a Trunk-mounted Accelerometer to Measure Peak Impacts during Team Sport Movements

Re‐examination of accelerometer data processing and calibration for the assessment of physical activity intensity

Jump height from inertial recordings: A tutorial for a sports scientist

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