Inertial Sensors in Swimming: Detection of Stroke Phases through 3D Wrist Trajectory.

Author:Cortesi, Matteo
 
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Introduction

The upper body actions in front crawl produce the major contribution to forward displacement (Gatta et al., 2012). More specifically, the arm-stroke technique is a key factor for the improvement of swimmer performance using the hand as thrust (Rouboa et al. 2006). Indeed, the arm-stroke cycle in front crawl is typically segmented into four phases (entry; pull; push; recovery) using the hand position relative to the swimmer's body or to the water surface as identification of the distinct phases (Chollet et al., 2000; Keskinen and Komi, 1993). The duration of the different arm-stroke phases varies from one swimmer to another and many investigations showed that swimmers adjust the time spent in each of the stroke phases to achieve performance objectives (Keskinen and Komi, 1993). In particular, the entry phase duration has the larger variation among the stroke phases due to the swimmer specialization (McCabe et al., 2011). Moreover, the thrust performance of the swimmer also depends on the timing between the actions of the hands, namely inter-limb coordination. The arm-stroke phases identification also allows to characterize the coordination model of the swimmer: Chollet et al. (2000) assessed inter-arm coordination in front crawl, using an index of coordination (IdC) which quantifies the time gap between two propulsive arm actions.

The three-dimensional (3D) video-based is the most used technique for both motion analysis of the swimmer's hand (Cesaracciu et al., 2011; Samson et al., 2015a) and stroke phases detections (Psycharakis and Sanders, 2010). Despite the reliability and validity, this method presents some limitations that force the coach to prefer the most accessible 2D video-analysis (Mooney et al., 2016): the costs of the cameras are expensive; the automatic process of data acquisition is very complex (Ceccon et al., 2013); data processing requires long time procedure, limiting the direct feedback and the swimmer's learning (Magalhaes et al, 2015); the water environment negatively affects the signal accuracy (Cortesi et al., 2014; Gourgoulis et al., 2008). To overcome the limitations of video-based motion analysis in sport, modern wearable technologies have introduced an alternative approach based on inertial and magnetic measurement units (IMMUs). IMMUs have small size, transmit data wireless, perform short-time analysis, do not require complex calibrations and can be worn easily. The IMMUs can analyze and monitor the whole swimming trial continuously without specified spatial limitation, a typical feature of the video analysis. The specificity of water leads to a spontaneous emergence of movements to satisfy the surrounding constraints that concurrently create positive (propulsion) and negative (drag) effects on the performance. The continuous manipulation of these constraints could lead new functional adaptations of swimming and the variability of the movements could be useful for the performance through circular relationships between perception and action (Guignard et al., 2017). Thus, IMMUs may allow to collect additional information on the variability of swimmer's coordination dynamics and on the swimmers' adaptability to surrounding constraints. For these reasons, in the last ten years some research groups have directed the scientific interest on the biomechanical analysis of the swimmer using IMMUs (Guignard et al., 2017, Camomilla et al., 2018). Considering the detection of the stroke phases through IMMUs, Dadashi et al. (2013) and Callaway (2015) proposed two algorithms based on the angular velocity of the forearm and on the body roll position, respectively. Despite their validity reported by comparison against a gold standard video-based system, the angular velocity method could be critical for the stroke phase detection. The entry start event was indeed identified and described in conjunction with video analysis. Moreover, additional swimming stroke configurations like different velocities or expertise of participants should be explored using the angular velocity method. According to Chollet et al. (2000), the detection of the starting events for each stroke phase could be identified by the hand spatial position. Therefore, the 3D underwater motion analysis using IMMUs technology based on spatial position could increase and complete the amount of information available for the swimmer stroke analysis, especially in terms of intra-cyclic stroke variability and stroke-by-stoke variability.

As the literature suggests, we hypothesized that the hand position could lead to more accurate stroke phases detection in swimming and consider specific swimmer technique. Considering the accuracy of the wrist joint angles estimated using IMMUs and the encumbrance of a sensor on the hand (Fantozzi et al., 2016), the mid-point between radial and ulna styloids can be considered the closest point to the hand estimated with sufficient reliability and with a minimal burden for the swimmer. Since the decisive improvement offered by the IMMUs is to assess the swimming motion continuously, the aim of the present study is to propose and to validate a novel approach for automatic stroke phase detection based on 3D wrist trajectory in front crawl swimming using IMMUs.

Methods

Participants and design

The experimental protocol was divided into two phases: i) the validation of the 3D wrist trajectory estimation in front-crawl swimming simulated on land using multiples IMMUs and ii) the validation of stroke phases temporal estimation through 3D wrist trajectory in front-crawl swimming in aquatic environment. The validation was performed using video analyses as gold standard: 3D spatial reconstruction and temporal events were considered in phase A and B, respectively.

Fourteen national-level male swimmers participated in the study (23.2 [+ or -] 2.8 years of age; 76.7 [+ or -] 7.6 kg of body mass; 1.81 [+ or -] 0.07 m of stature); at the time the experiments were performed, the weekly training duration of the swimmers was 15 [+ or -] 3 h per week. Short-course 25 m personal front-crawl best times were 11.3 [+ or -] 0.2 s. All fourteen participants took part to the stroke phase detection validation in swimming, while only five completed the 3D swimming wrist trajectory validation in simulated swimming.

The project was approved by the local University Ethics committee and conducted according to the ethical standards of the Declaration of Helsinki. All participants provided written informed consent to participate in the study.

3D wrist trajectory validation in simulated swimming

Each of the five swimmers involved performed a 20 seconds trial of front-crawl simulated swimming with a stroke rate like swimming motion (between 30 to 60 cycles/min). The swimmers were asked, lying on a swim bench, to swim as they would have done in a swimming pool. 150 complete front crawl arm-stroke cycles were collected corresponding to the right and left strokes of the five swimmers involved.

Data collection was performed using an IMMUs system (APDM Opals, 5 units, including tri-axial accelerometers ([+ or -]6 g), tri-axial gyroscopes ([+ or -] 2000[degrees]/s) and tri-axial magnetometers ([+ or -]6 gauss) each, weight

The 3D coordinates of the wrist were computed considering the kinematic chain of three rigid body segments of the upper limb (thorax, upper-arm, forearm, not including the hand). Body segments orientation was estimated applying a protocol adapted and validated for swimming (Cutti et al., 2008; Fantozzi et al., 2016). Body segments length was calculated considering the anatomical landmarks positions measured in the static calibration trial: incisura jugularis and gleno-humeral position for the thorax, gleno-humeral position and mid-point between the humerus epicondyles for the upper-arm, and mid-point between the humerus epicondyles and mid-point between the styloids for the forearm. Thus, a rigid body rototranslation was applied recursively from the thorax to the wrist.

Three different wrist trajectories were computed through: 1) 3D marker-based stereophotogrammetry system (MBS) considered as the gold standard; 2) stereophotogrammetric data applying the kinematic chain model (KMS); and 3) IMMUs data applying the same model (3DIMMU). Specifically, IMMUs data were obtained processing raw accelerometers, gyroscopes and magnetometers data with Madgwick algorithm to obtain the 3D IMMUs orientation and applying the kinematic chain model described above (Fantozzi et al., 2016). The wrist trajectories were computed in the thorax anatomical reference system (Z axis was pointing caudally, X axis pointing to the left of the participant, and Y axis pointing cranially forwards in the swimming direction).

Stroke phase detection validation in swimming

Fourteen participants performed 25 m front-crawl trial in a 25 m indoor swimming pool: eight of them at the intensity of 75% of their maximal velocity (V75%), and six of them at the 100% (V100%). Overall, 146 swim strokes were available after data collection that arise to the right strokes and left strokes of the fourteen swimmers involved (mean of 5 [+ or -] 1 swim strokes per swimmer). Since the first and the last stroke cycles of each trial are usually conditioned by the start and finish patterns, they were excluded in the following analysis. Before the second session, the swimmers completed a 20-minute warm-up period and performed an all-out 25 m front-crawl trial wearing IMMUs to become familiar with the test and to measure the maximal velocity. With the aim to...

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