A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units

Dadashi, Farzin; Arami, Arash; Crettenand, Florent; Millet, Grégoire P.; Komar, John; Seifert, Ludovic; Aminian, Kamiar

doi:10.1109/bsn.2013.6575461

Cited by 36 publications

(14 citation statements)

References 16 publications

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“…Figure 30 provides an example of a typical system architecture which is emerging as a reference design for these systems and is reflective of the most commonly described systems in the literature. Many of the systems described are prototype systems that have been developed specifically for use in swimming research [ 53 , 74 , 83 ]. Additionally, various commercially available sensor devices such as Physilog (BioAGM, Switzerland) [ 64 ]; FreeSense (Sensorize, Italy) [ 47 ]; Minimax X (Catapult Sports, Australia) [ 46 , 86 ] and Shimmer (Shimmer, Ireland) [ 78 , 94 ] have also been used.…”

Section: Discussionmentioning

confidence: 99%

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

Mooney

Corley

Godfrey

et al. 2015

Sensors

118

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Technical evaluation of swimming performance is an essential factor of elite athletic preparation. Novel methods of analysis, incorporating body worn inertial sensors (i.e., Microelectromechanical systems, or MEMS, accelerometers and gyroscopes), have received much attention recently from both research and commercial communities as an alternative to video-based approaches. This technology may allow for improved analysis of stroke mechanics, race performance and energy expenditure, as well as real-time feedback to the coach, potentially enabling more efficient, competitive and quantitative coaching. The aim of this paper is to provide a systematic review of the literature related to the use of inertial sensors for the technical analysis of swimming performance. This paper focuses on providing an evaluation of the accuracy of different feature detection algorithms described in the literature for the analysis of different phases of swimming, specifically starts, turns and free-swimming. The consequences associated with different sensor attachment locations are also considered for both single and multiple sensor configurations. Additional information such as this should help practitioners to select the most appropriate systems and methods for extracting the key performance related parameters that are important to them for analysing their swimmers’ performance and may serve to inform both applied and research practices.

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Section: Discussionmentioning

confidence: 99%

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

Mooney

Corley

Godfrey

et al. 2015

Sensors

118

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“…Evaluations were performed in both offline and online settings. Dadashi et al [14] carried out detection of important breaststroke swimming events automatically by using Hidden Markov model (HMM) and wearable sensors. Parkka et al [15] used accelerometers and gyroscopes attached to ankle, wrist and hip to estimate intensity of physical activity.…”

Section: Related Workmentioning

confidence: 99%

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Ballı¹,

Sağbaş²

2017

Advances in Statistical Methodologies and Their Application to Real Problems

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The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.

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“…However, in this study using kalman filter, the cost of calculation is high. Automatic detection of the swimming stages with the accelerometer sensors installed on the arms and legs of the people was performed [12]. In addition to smart phones, there are several studies on the smart watch accelerometer data.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Methods for Determining Activity from Physical Movements

Çalişan

Talu

2021

Politeknik Dergisi

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The performances of the approaches that can recognize the physical movements of people are evaluated ❖ Accelerometer data of 6 different actions from 10 different people are obtained (from IMU) ❖ Accelerometer data ise divided into packets and their properties are extracted and classical approaches ❖ A new CNN-based approach to action detection is proposed ❖ Down, up, sitting, standing, walking and running actions were applied to YSA, SVM, k-NN and CNN

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A Hidden Markov Model of the breaststroke swimming temporal phases using wearable inertial measurement units

Cited by 36 publications

References 16 publications

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

Inertial Sensor Technology for Elite Swimming Performance Analysis: A Systematic Review

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

Comparison of Methods for Determining Activity from Physical Movements

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