Determining Stroke and Movement Profiles in Competitive Tennis Match-Play From Wearable Sensor Accelerometry.

ABSTRACT: Perri, T, Reid, M, Murphy, A, Howle, K, and Duffield, R. Determining stroke and movement profiles in competitive tennis match-play from wearable sensor accelerometry. J Strength Cond Res 37(6): 1271-1276, 2023-This study determined stroke and movement accelerometry metrics from a wearable sensor and compared between court surface (grass vs. hard) and match outcome (win vs. loss) during competitive tennis match-play. Eight junior high-performance tennis players wore a trunk-mounted global positioning system, with in-built accelerometer, magnetometer, and gyroscope during singles matches on hard and grass courts. The manufacturer software calculated accelerometer-derived total player load (tPL). A prototype algorithm classified forehands, backhands, serves, and "other" strokes, thereby calculating stroke PL (sPL) from individual strokes. Movement PL (mPL) was calculated as the difference between tPL and sPL, with all metrics reported as absolute and relative (min -1 , %, and ·stroke). Analysis of accelerometer load and stroke count metrics was performed through a two-way (surface [grass vs. hard] × match outcome [win vs. loss]) analysis of variance ( p < 0.05) and effect sizes (Cohen's d ). No interaction effects for surface and match outcome existed for absolute tPL, mPL, and sPL ( p > 0.05). Increased mPL% featured on grass courts, whereas sPL% was increased on hard courts ( p = 0.04, d = 1.18[0.31-2.02]). Elevated sPL·min -1 existed on hard courts ( p = 0.04, d = 1.19[0.32-2.04]), but no differences in tPL·min -1 and mPL·min -1 were evident for surface or outcome ( p > 0.05). Relative forehand sPL (FH-sPL·min -1 ) was higher on hard courts ( p = 0.03, d = 1.18[0.31-2.02]) alongside higher forehand counts ( p = 0.01, d = 1.29[0.40-2.14]). Hitting demands are heightened on hard courts from increased sPL and counts. Conversely, increased mPL% on grass courts likely reflects the specific movement demands from point-play. Physical preparation strategies during training blocks can be tailored toward movement or hitting loads to suit competitive surfaces.

Differentiating Stroke and Movement Accelerometer Profiles to Improve Prescription of Tennis Training Drills.

ABSTRACT: Perri, T, Reid, M, Murphy, A, Howle, K, and Duffield, R. Differentiating stroke and movement accelerometer profiles to improve prescription of tennis training drills. J Strength Cond Res 37(3): 646-651, 2023-This study compared the movement- and stroke-related accelerometer profiles and stroke counts between common on-court tennis training drills. Ten, junior-elite, male tennis players wore a cervical-mounted global positioning systems, with in-built accelerometer, gyroscope, and magnetometer during hard court training sessions ( n = 189). Individual training drills were classified into 8 categories based on previous research descriptions. Manufacturer software calculated total player load (tPL), while a prototype algorithm detected forehand (FH), backhands (BH), and serves and then calculated a stroke player load (sPL) from individual strokes. Movement player load (mPL) was calculated as the difference between tPL and sPL. Drill categories were compared for relative ( . min -1 ) tPL, sPL, mPL, and stroke counts via a 1-way analysis of variance with effect sizes (Cohen's d ) and 95% confidence intervals. Highest tPL . min -1 existed in accuracy and recovery or defensive drills ( p < 0.05), with lowest tPL·min -1 values observed in match-play simulation ( p < 0.05). For sPL·min -1 , accuracy drills elicited greater values compared with all other drill types ( p < 0.05), partly via greater FH-sPL·min -1 ( p < 0.05), with lowest sPL·min -1 existing for match-play ( p < 0.05). Accuracy, open, and recovery or defensive drills result in greater BH-sPL·min -1 and BH . min -1 ( p < 0.05). Serve-sPL·min -1 is highest in technical and match-play drills ( p < 0.05). Higher mPL·min -1 existed in accuracy, recovery or defensive, 2v1 net, open, and 2v1 baseline ( p < 0.05). Furthermore, mPL·min -1 in points drills was greater than technical and match-play simulation drills ( p < 0.05). Higher hitting-based accelerometer loads (sPL·min -1 ) exist in accuracy drills, whereas technical and match-play drills show the lowest movement demands (mPL·min -1 ). These findings can aid individual drill prescription for targeting movement or hitting load.

Prototype Machine Learning Algorithms from Wearable Technology to Detect Tennis Stroke and Movement Actions.

This study evaluated the accuracy of tennis-specific stroke and movement event detection algorithms from a cervically mounted wearable sensor containing a triaxial accelerometer, gyroscope and magnetometer. Stroke and movement data from up to eight high-performance tennis players were captured in match-play and movement drills. Prototype algorithms classified stroke (i.e., forehand, backhand, serve) and movement (i.e., "Alert", "Dynamic", "Running", "Low Intensity") events. Manual coding evaluated stroke actions in three classes (i.e., forehand, backhand and serve), with additional descriptors of spin (e.g., slice). Movement data was classified according to the specific locomotion performed (e.g., lateral shuffling). The algorithm output for strokes were analysed against manual coding via absolute (n) and relative (%) error rates. Coded movements were grouped according to their frequency within the algorithm's four movement classifications. Highest stroke accuracy was evident for serves (98%), followed by groundstrokes (94%). Backhand slice events showed 74% accuracy, while volleys remained mostly undetected (41-44%). Tennis-specific footwork patterns were predominantly grouped as "Dynamic" (63% of total events), alongside successful linear "Running" classifications (74% of running events). Concurrent stroke and movement data from wearable sensors allows detailed and long-term monitoring of tennis training for coaches and players. Improvements in movement classification sensitivity using tennis-specific language appear warranted.

Validating an algorithm from a trunk-mounted wearable sensor for detecting stroke events in tennis.

This study analysed the accuracy of a prototype algorithm for tennis stroke detection from wearable technology. Strokes from junior-elite tennis players over 10 matches were analysed. Players wore a GPS unit containing an accelerometer, gyroscope and magnetometer. Manufacturer-developed algorithms determined stoke type and count (forehands, backhands, serves and other). Matches were video recorded to manually code ball contacts and shadow swing events for forehands, backhands and serves and further by stroke classifications (i.e., drive, volley, slice, end-range). Comparisons between algorithm and coding were analysed via ANOVA and Bland-Altman plots at the match-level and error rates for specific stroke-types. No significant differences existed for stroke count between the algorithm and manual coding (p > 0.05). Significant (p < 0.0001) overestimation of "Other" strokes were observed from the algorithm, with no difference in groundstrokes and serves (p > 0.05). Serves had the highest accuracy of all stroke types (≥98%). Forehand and backhand "drives" were the most accurate (>86%), with volleys mostly undetected (58-60%) and slices and end-range strokes likely misclassified (49-51%). The prototype algorithm accurately quantifies serves and forehand and backhand "drives" and serves. However, underestimations of shadow swings and overestimations of "other" strokes suggests strokes with reduced trunk rotation have poorer detection accuracy.

Injury Incidence and Workloads during congested Schedules in Football.

This study compared injury incidence and training loads between single and multi-match weeks, and seasons with and without congested scheduling. Measures of internal (session-Rating of Perceived Exertion × duration for training/match and % maximal heart rate) and external load (total, low-, high-, and very high-intensity running distances) along with injury incidence rates were determined from 42 players over 3 seasons; including 1 without and 2 (season 2 and 3) with regular multi-match weeks. Within-player analyses compared 1 (n=214) vs. 2-match (n=86) weeks (>75min in matches), whilst team data was compared between seasons. Total injury rates were increased during multi-match weeks (p=0.001), resulting from increased match and training injuries (50.3, 16.9/1000h). Between-season total injury rates were highest when congested scheduling was greatest in season 3 (27.3/1000h) and season 2 (22.7/1000h) vs. season 1 (14.1/1000h; p=0.021). All external load measures were reduced in multi-match weeks (p<0.05). Furthermore, all internal and external training loads were lowest in seasons with congestion (p<0.05). In conclusion, increased injury rates in training and matches exist. Total loads remain comparable between single and multi-match weeks, though reduce in congested seasons. Whether injuries result from reduced recovery, increased match exposure or the discreet match external loads remain to be elucidated.

Recovery profiles following single and multiple matches per week in professional football.

Objectives: To investigate player responses 48 h post single (SM) and multi-match (MM) weeks on two subjective and three objective outcome measures to infer recovery status. Methods: From 42 professional players over 2 seasons, outcome measures relevant to recovery status were collected 48 h following matches, as well as during pre-season training weeks as a comparative baseline. These included (1) 5-item subjective wellness questionnaire, (2) total quality recovery (TQR) scale, (3) hip adduction squeeze test, ankle knee to wall (KTW) test, and active knee extension (AKE) flexibility test. These outcome measures 48 h post-match were compared for SM (n = 79) and MM (n = 86) weeks where players completed >75 min of match time in only one (SM) or if both matches were played and had <96 h recovery (MM). Internal match load was collected from each match based on session rating of perceived exertion (sRPE) multiplied by match duration. Results: Subjective wellness (specifically fatigue, sleep and soreness), TQR and hip adduction squeeze test were all significantly reduced following match 1 at 48 h post for both SM and MM (p < 0.05), and further reduced following match 2 in MM (p < 0.05). No other outcome measures to infer recovery showed significant differences (p > 0.05) within or between-conditions. Conclusions: Subjective wellness, TQR and hip adduction strength showed reduction 48 h post match for players competing in multiple matches with <96 h recovery. Therefore, these outcome measures may be of use to practitioners to assess readiness to compete during congested competition schedules.

Effects of northbound long-haul international air travel on sleep quantity and subjective jet lag and wellness in professional Australian soccer players.

The current study examined the effects of 10-h northbound air travel across 1 time zone on sleep quantity, together with subjective jet lag and wellness ratings, in 16 male professional Australian football (soccer) players. Player wellness was measured throughout the week before (home training week) and the week of (away travel week) travel from Australia to Japan for a preseason tour. Sleep quantity and subjective jet lag were measured 2 d before (Pre 1 and 2), the day of, and for 5 d after travel (Post 1-5). Sleep duration was significantly reduced during the night before travel (Pre 1; 4.9 [4.2-5.6] h) and night of competition (Post 2; 4.2 [3.7-4.7] h) compared with every other night (P<.01, d>0.90). Moreover, compared with the day before travel, subjective jet lag was significantly greater for the 5 d after travel (P<.05, d>0.90), and player wellness was significantly lower 1 d post-match (Post 3) than at all other time points (P<.05, d>0.90). Results from the current study suggest that sleep disruption, as a result of an early travel departure time (8 PM) and evening match (7:30 PM), and fatigue induced by competition had a greater effect on wellness ratings than long-haul air travel with a minimal time-zone change. Furthermore, subjective jet lag may have been misinterpreted as fatigue from sleep disruption and competition, especially by the less experienced players. Therefore, northbound air travel across 1 time zone from Australia to Asia appears to have negligible effects on player preparedness for subsequent training and competition.