Correlational data concerning body centre of mass acceleration, muscle activity, and forces exerted during a suspended lunge under different stability conditions in high-standard track and field athletes

Aguilera-Castells, Joan; Buscà, Bernat; Arboix-Alió, Jordi; McEwan, Gary; Calleja-González, Julio; Peña, Javier

doi:10.1016/j.dib.2019.104912

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…The bandwidth of the accelerometer is 500 Hz. The BIOPAC accelerometer has been fully validated and used as the reference sensor in the development of medical devices ( Costa and Fazendeiro, 2014 ; Aguilera-Castells et al, 2020 ; Jacunski and Rafferty, 2020 ). The dimensions are 16 mm long, 17 mm wide, and 8 mm high, which are appropriate for the simultaneous measurement in different body sites.…”

Section: Methodsmentioning

confidence: 99%

Influences of Sensor Placement Site and Subject Posture on Measurement of Respiratory Frequency Using Triaxial Accelerometers

2020

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Introduction Respiration frequency (RF) could be derived from the respiratory signals recorded by accelerometers which detect chest wall movements. The optimum direction of acceleration for accurate RF measurement is still uncertain. We aim to investigate the effect of measure site, posture, and direction of acceleration on the accuracy of accelerometer-based RF estimation. Methods In supine and seated postures respectively, respiratory signals were measured from 34 healthy subjects in 70 s by triaxial accelerometers located at four sites on the body wall (over the clavicle, laterally on the chest wall, over the pectoral part of the anterior chest wall, on the abdomen in the midline at the umbilicus), with the reference respiratory signal simultaneously recorded by a strain gauge chest belt. RFs were extracted from the accelerometer and reference respiratory signals using wavelet transformation. To investigate the effect of measure site, posture, and direction of acceleration on the accuracy of accelerometer-based RF estimation, repeated measures multivariate analysis of variance, linear regression, Bland-Altman analysis, and Scheirer-Ray-Hare test were performed between reference and accelerometer-based RFs. Results There was no significant difference in accelerometer-based RF estimation between seated and supine postures, among four accelerometer sites, or between seated or supine postures ( p > 0.05 for all). The error of accelerometer-based RF estimation was less than 0.03 Hz (two breaths per minute) at any site or posture, but was significantly smaller in supine posture than in seated posture ( p < 0.05), with narrower limits of agreement in Bland-Altman analysis and higher accuracy in linear regression ( R 2 > 0.61 vs. R 2 < 0.51). Conclusion Respiration frequency can be accurately measured from the acceleration of any direction using triaxial accelerometers placed at the clavicular, pectoral and lateral sites on the chest as well the mid abdominal site. More accurate RF estimation could be achieved in supine posture compared with seated posture.

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Section: Methodsmentioning

confidence: 99%

Influences of Sensor Placement Site and Subject Posture on Measurement of Respiratory Frequency Using Triaxial Accelerometers

2020

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“…This, however, does not mean that free, open, and public datasets from the domain of SST do not exist. There are already a significant number of them (e.g., [53,[166][167][168][169][170][171]), together with websites that publish sanitized datasets (e.g., [172]), it remains a fact that they are not used enough. If researchers are going to avoid using existing datasets, they should try to ensure publishing as much of their private datasets as possible.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

Rajšp

Fister

2020

Applied Sciences

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The rapid transformation of our communities and our way of life due to modern technologies has impacted sports as well. Artificial intelligence, computational intelligence, data mining, the Internet of Things (IoT), and machine learning have had a profound effect on the way we do things. These technologies have brought changes to the way we watch, play, compete, and also train sports. What was once simply training is now a combination of smart IoT sensors, cameras, algorithms, and systems just to achieve a new peak: The optimum one. This paper provides a systematic literature review of smart sport training, presenting 109 identified studies. Intelligent data analysis methods are presented, which are currently used in the field of Smart Sport Training (SST). Sport domains in which SST is already used are presented, and phases of training are identified, together with the maturity of SST methods. Finally, future directions of research are proposed in the emerging field of SST.

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“…The activity data of both sides of the vastus medialis (VM) and vastus lateralis (VL) were collected by using BIOPAC (MP160, Biopac Systems Inc., Goleta, CA, United States) equipment with excellent reliability (ICC = 0.82-0.96). 23 AcqKnowledge 5.0 software (Biopac Systems, Inc., Goleta, CA, United States) was used on a personal laptop for signal recording and processing of raw data. Before attaching the electrodes, the hair on the selected muscles was shaved, and the skin was cleaned with alcohol.…”

Section: Muscle Activitymentioning

confidence: 99%

The effect of types of sensory feedback on the acquisition and retention of squat performance: A randomized, double‐blind, controlled trial

Shin,

Kim,

Cho

2023

Scandinavian Med Sci Sports

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Various sensory feedback methods are considered important for motor learning, but the effect of each sensory feedback method on effective squat learning still needs to be clarified. This study aimed to investigate the effect of sensory feedback types on the acquisition and retention of a squat. A double‐blinded, randomized controlled trial was carried out. Thirty‐healthy people were recruited and randomly assigned to the visual feedback group (VFG = 10), tactile feedback group (TFG = 10), and control group (CG = 10). VFG received visual feedback through video data of the participant performing squats, and TFG received tactile feedback through manual contact with a physical therapist. Both groups received feedback on the movements that needed correction after each set was completed. CG maintained rest without receiving any feedback. The retro‐reflexive marker, force plate, and electromyography were used to measure body angle, foot center of pressure (COP), and muscle activity. All assessments were measured to confirm a squat acquisition. VFG and TFG showed significant differences in neutral knee position (NKP), trunk forward lean (TFL), anterior knee displacement (AKD), and anteroposterior (AP) foot COP (p < 0.050). In addition, the acquisition was retained until 3 days later for NKP and a week later for TFL, AKD, and AP foot COP in VFG (p < 0.050), while the acquisition was not retained in TFG (p > 0.050). There was no statistically significant change in CG (p > 0.050). This study demonstrated that visual feedback positively affects the acquisition and retention of squats. Therefore, we recommend the use of visual feedback for squat acquisition and retention in exercise novices.

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Correlational data concerning body centre of mass acceleration, muscle activity, and forces exerted during a suspended lunge under different stability conditions in high-standard track and field athletes

Cited by 8 publications

References 7 publications

Influences of Sensor Placement Site and Subject Posture on Measurement of Respiratory Frequency Using Triaxial Accelerometers

Influences of Sensor Placement Site and Subject Posture on Measurement of Respiratory Frequency Using Triaxial Accelerometers

A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training

The effect of types of sensory feedback on the acquisition and retention of squat performance: A randomized, double‐blind, controlled trial

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