The Use of a Cap-mounted Tri-axial Accelerometer for Measurement of Distance, Lap Times and Stroke Rates in Swim Training

Michaels, Scott; Taunton, D.J.; Forrester, Alexander I. J.; Hudson, Dominic A.; Phillips, Christopher W.G.; Holliss, Ben A.; Turnock, S.R.

doi:10.1016/j.proeng.2016.06.263

Cited by 2 publications

(5 citation statements)

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“…[ 17 , 22 ] proposed a threshold-based methodology from a combination of orientation and component energy to determine the stroke type with 96.1% accuracy. This method, sometimes combined with a gyroscopic signal, has been widely used in the literature for stroke recognition [ 23 , 24 , 25 ]; as well as for the wall push, turn, and touch identification sometimes used for lap time (LT) calculation [ 19 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. LT calculation refers to identified peaks in the acceleration signal.…”

Section: Introductionmentioning

confidence: 99%

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

Delhaye

Bouvet

Guillaume

et al. 2022

Sensors

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This study presents a deep learning model devoted to the analysis of swimming using a single Inertial Measurement Unit (IMU) attached to the sacrum. Gyroscope and accelerometer data were collected from 35 swimmers with various expertise levels during a protocol including the four swimming techniques. The proposed methodology took high inter- and intra-swimmer variability into account and was set up for the purpose of predicting eight swimming classes (the four swimming techniques, rest, wallpush, underwater, and turns) at four swimming velocities ranging from low to maximal. The overall F1-score of classification reached 0.96 with a temporal precision of 0.02 s. Lap times were directly computed from the classifier thanks to a high temporal precision and validated against a video gold standard. The mean absolute percentage error (MAPE) for this model against the video was 1.15%, 1%, and 4.07%, respectively, for starting lap times, middle lap times, and ending lap times. This model is a first step toward a powerful training assistant able to analyze swimmers with various levels of expertise in the context of in situ training monitoring.

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Section: Introductionmentioning

confidence: 99%

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

Delhaye

Bouvet

Guillaume

et al. 2022

Sensors

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“…For validation, in addition to inertial sensors, they attached a video camera for tagging the activities. Regarding the sports activity, as well as the previous example, we observed that multiple solutions have been proposed to assist users in performing activities such as swimming [52], hokey [53], golf [57], or motorcycle VR [58]. Such a system was proposed by Michaels et al [52] in order to coach new users to use inertial sensors.…”

Section: Head Motion Systemsmentioning

confidence: 92%

“…Regarding the sports activity, as well as the previous example, we observed that multiple solutions have been proposed to assist users in performing activities such as swimming [52], hokey [53], golf [57], or motorcycle VR [58]. Such a system was proposed by Michaels et al [52] in order to coach new users to use inertial sensors. Their system used only a tri-axial accelerometer and considered the following requirements: to be lightweight, waterproof, have a long life battery, and to save acquired data for a long period.…”

Section: Head Motion Systemsmentioning

confidence: 92%

“…After the literature review, we noticed that the relevant selected papers had the following distribution: 49% focused on medical problems such as head tremor [34], cerebral palsy [30][31][32][33], fall detection [15,34], vestibular rehabilitation [35], physical and mental activity analysis [20,36,37], forward head posture [17,38], and musculoskeletal disorders [39]; 20% focused on the general problem of human-computer interaction [35,[40][41][42]; and 10% focused on the development of new computational and calibration methods [39,43,44]. The last two topics were the development of sports devices (swimming, hokey, golf, motorcycle ride or spinning exercises: 10%) [45][46][47][48][49][50], and 8% focused on prevention and safety systems (drivers' attention) [51][52][53]. All the selected papers used an electronic device with an inertial sensor placed on a specific part of the head.…”

Section: Review Findingsmentioning

confidence: 99%

“…The reported results depend on the complexity of head motion activity and the acquisition period. In the data acquisition period, this process can take from a few seconds or minutes [23,66,77] to the real condition period (15 min [24,60], half an hour or one hour [41,47,70], 3 days [56], or 15 weeks [52]).…”

Section: Classical Machine Learning Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Using Inertial Sensors to Determine Head Motion—A Review

Severin

Dobrea

2021

J. Imaging

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Human activity recognition and classification are some of the most interesting research fields, especially due to the rising popularity of wearable devices, such as mobile phones and smartwatches, which are present in our daily lives. Determining head motion and activities through wearable devices has applications in different domains, such as medicine, entertainment, health monitoring, and sports training. In addition, understanding head motion is important for modern-day topics, such as metaverse systems, virtual reality, and touchless systems. The wearability and usability of head motion systems are more technologically advanced than those which use information from a sensor connected to other parts of the human body. The current paper presents an overview of the technical literature from the last decade on state-of-the-art head motion monitoring systems based on inertial sensors. This study provides an overview of the existing solutions used to monitor head motion using inertial sensors. The focus of this study was on determining the acquisition methods, prototype structures, preprocessing steps, computational methods, and techniques used to validate these systems. From a preliminary inspection of the technical literature, we observed that this was the first work which looks specifically at head motion systems based on inertial sensors and their techniques. The research was conducted using four internet databases—IEEE Xplore, Elsevier, MDPI, and Springer. According to this survey, most of the studies focused on analyzing general human activity, and less on a specific activity. In addition, this paper provides a thorough overview of the last decade of approaches and machine learning algorithms used to monitor head motion using inertial sensors. For each method, concept, and final solution, this study provides a comprehensive number of references which help prove the advantages and disadvantages of the inertial sensors used to read head motion. The results of this study help to contextualize emerging inertial sensor technology in relation to broader goals to help people suffering from partial or total paralysis of the body.

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The Use of a Cap-mounted Tri-axial Accelerometer for Measurement of Distance, Lap Times and Stroke Rates in Swim Training

Cited by 2 publications

References 4 publications

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

Automatic Swimming Activity Recognition and Lap Time Assessment Based on a Single IMU: A Deep Learning Approach

Using Inertial Sensors to Determine Head Motion—A Review

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