Swimming Stroke Kinematic Analysis with BSN

Pansiot, Julien; Lo, Benny; Yang, Guang‐Zhong

doi:10.1109/bsn.2010.11

Cited by 67 publications

(36 citation statements)

References 12 publications

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“…It detects turns and strokes of the four main stroke types and can therefore be used as a lap counter, training tracker and efficiency monitor. Our works overcomes some of the limitations in [6] and provides an alternative data analysis approach with pattern recognition methods. We conducted a research study to provide a head-worn swimming analysis system capable of detecting the state of the swimmer, turning events and the four main swimming styles.…”

Section: Introductionmentioning

confidence: 99%

“…However, the data processing was performed offline and no instant feedback was given during exercise. The single unobtrusive sensor position on the head was first investigated in 2010 [6]. The authors proved to be able to detect three swimming styles, certain events like wall push-offs and turns as well as important parameters like stroke count and stroke duration.…”

Section: Introductionmentioning

confidence: 99%

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Classification of kinematic swimming data with emphasis on resource consumption

Jensen¹,

Prade²,

Eskofier³

2013

2013 IEEE International Conference on Body Sensor Networks

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Abstract-The collection of kinematic data with a head-worn sensor is a promising approach for swimming data analysis in the context of athlete support systems. We present a new approach of analyzing these data and describe a system that segments the lanes of a swimming session and classifies the swimming style of each lane. Special emphasis was put on the algorithm efficiency and the analysis of the resource demands to be able to port the implementation to an embedded microcontroller. For developing the system, data of twelve subjects was collected. The data incorporated two different turn styles that mark the end of a lane as well as the four main swimming styles backstroke, breaststroke, butterfly and freestyle. All turns were successfully identified from the turn detection. Our fully automatic swimming style classification reached a classification rate of 95.0%. The results from the resource consumption analysis can be used to support the decision for the embedded target hardware of a headworn swimming training system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Classification of kinematic swimming data with emphasis on resource consumption

Jensen¹,

Prade²,

Eskofier³

2013

2013 IEEE International Conference on Body Sensor Networks

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“…These constraints prompt behavioral adaptations, such as (i) increasing SR to overcome the drag created by the swimmer in the next lane, (ii) regulating swimming speed when approaching the wall to perform a turn, or (iii) showing fatigue at the end of a race (Dadashi et al, 2016). Also, the acceleration data recorded over a training session can help coaches to distinguish between swimming styles (Pansiot et al, 2010; Hou, 2012; Jensen et al, 2013; Ohgi et al, 2014; Mooney et al, 2015b), but not between two different signals emerging from the same swimming style. This means that a single sensor positioned on the chest can distinguish the signals obtained in freestyle and backstroke from those obtained in butterfly and breaststroke through the general shape of the acceleration versus time curves (Le Sage et al, 2011; Jensen et al, 2013; Ohgi et al, 2014) (please refer to Le Sage et al, 2011; Ohgi et al, 2014, for a depiction of these curves).…”

Section: Investigation Of High-order Parameters To Characterize Coordmentioning

confidence: 99%

Behavioral Dynamics in Swimming: The Appropriate Use of Inertial Measurement Units

Guignard¹,

Rouard

Chollet

et al. 2017

Front. Psychol.

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Motor control in swimming can be analyzed using low- and high-order parameters of behavior. Low-order parameters generally refer to the superficial aspects of movement (i.e., position, velocity, acceleration), whereas high-order parameters capture the dynamics of movement coordination. To assess human aquatic behavior, both types have usually been investigated with multi-camera systems, as they offer high three-dimensional spatial accuracy. Research in ecological dynamics has shown that movement system variability can be viewed as a functional property of skilled performers, helping them adapt their movements to the surrounding constraints. Yet to determine the variability of swimming behavior, a large number of stroke cycles (i.e., inter-cyclic variability) has to be analyzed, which is impossible with camera-based systems as they simply record behaviors over restricted volumes of water. Inertial measurement units (IMUs) were designed to explore the parameters and variability of coordination dynamics. These light, transportable and easy-to-use devices offer new perspectives for swimming research because they can record low- to high-order behavioral parameters over long periods. We first review how the low-order behavioral parameters (i.e., speed, stroke length, stroke rate) of human aquatic locomotion and their variability can be assessed using IMUs. We then review the way high-order parameters are assessed and the adaptive role of movement and coordination variability in swimming. We give special focus to the circumstances in which determining the variability between stroke cycles provides insight into how behavior oscillates between stable and flexible states to functionally respond to environmental and task constraints. The last section of the review is dedicated to practical recommendations for coaches on using IMUs to monitor swimming performance. We therefore highlight the need for rigor in dealing with these sensors appropriately in water. We explain the fundamental and mandatory steps to follow for accurate results with IMUs, from data acquisition (e.g., waterproofing procedures) to interpretation (e.g., drift correction).

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“…In the measurement vector z k = [ s , a ] T , s is obtained by the WLAN RSSI-based Horus system [21], while a is calculated based on readings from the inertial sensors [24]. The observation equation is

z_{k} = H_{k} X_{k} + n_{k}

where H k is the observation matrix and n k is the measurement noise that is determined empirically.…”

Section: Proposed Handover Schemementioning

confidence: 99%

An Optimized Handover Scheme with Movement Trend Awareness for Body Sensor Networks

Sun

Zhang

et al. 2013

Sensors

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When a body sensor network (BSN) that is linked to the backbone via a wireless network interface moves from one coverage zone to another, a handover is required to maintain network connectivity. This paper presents an optimized handover scheme with movement trend awareness for BSNs. The proposed scheme predicts the future position of a BSN user using the movement trend extracted from the historical position, and adjusts the handover decision accordingly. Handover initiation time is optimized when the unnecessary handover rate is estimated to meet the requirement and the outage probability is minimized. The proposed handover scheme is simulated in a BSN deployment area in a hospital environment in UK. Simulation results show that the proposed scheme reduces the outage probability by 22% as compared with the existing hysteresis-based handover scheme under the constraint of acceptable handover rate.

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Swimming Stroke Kinematic Analysis with BSN

Cited by 67 publications

References 12 publications

Classification of kinematic swimming data with emphasis on resource consumption

Classification of kinematic swimming data with emphasis on resource consumption

Behavioral Dynamics in Swimming: The Appropriate Use of Inertial Measurement Units

An Optimized Handover Scheme with Movement Trend Awareness for Body Sensor Networks

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