can derive other biomechanical metrics describing the jump performance, such as force, power, velocity, and center-of-mass position. Force data derived from inertial sensors have been shown to agree well with simultaneously recorded force plate data. 16 However, although jump heights derived from
Malachy P. McHugh, Tom Clifford, Will Abbott, Susan Y. Kwiecien, Ian J. Kremenic, Joseph J. DeVita, and Glyn Howatson
Lindsey Tulipani, Mark G. Boocock, Karen V. Lomond, Mahmoud El-Gohary, Duncan A. Reid, and Sharon M. Henry
new to the field of PT is inertial sensors, which are emerging as a useful tool for monitoring multiple segments in all planes of motion simultaneously. 6 Initial investigations into inertial sensor accuracy for PT applications compared inertial sensor data with data collected from motion analysis
Ilaria Masci, Giuseppe Vannozzi, Nancy Getchell, and Aurelio Cappozzo
Assessing movement skills is a fundamental issue in motor development. Current process-oriented assessments, such as developmental sequences, are based on subjective judgments; if paired with quantitative assessments, a better understanding of movement performance and developmental change could be obtained. Our purpose was to examine the use of inertial sensors to evaluate developmental differences in hopping over distance. Forty children executed the task wearing the inertial sensor and relevant time durations and 3D accelerations were obtained. Subjects were also categorized in different developmental levels according to the hopping developmental sequence. Results indicated that some time and kinematic parameters changed with some developmental levels, possibly as a function of anthropometry and previous motor experience. We concluded that, since inertial sensors were suitable in describing hopping performance and sensitive to developmental changes, this technology is promising as an in-field and user-independent motor development assessment tool.
Barry S. Mason, James M. Rhodes, and Victoria L. Goosey-Tolfrey
The purpose of the current study was to determine the validity and reliability of an inertial sensor for assessing speed specific to athletes competing in the wheelchair court sports (basketball, rugby, and tennis). A wireless inertial sensor was attached to the axle of a sports wheelchair. Over two separate sessions, the sensor was tested across a range of treadmill speeds reflective of the court sports (1.0 to 6.0 m/s). At each test speed, ten 10-second trials were recorded and were compared with the treadmill (criterion). A further session explored the dynamic validity and reliability of the sensor during a sprinting task on a wheelchair ergometer compared with high-speed video (criterion). During session one, the sensor marginally overestimated speed, whereas during session two these speeds were underestimated slightly. However, systematic bias and absolute random errors never exceeded 0.058 m/s and 0.086 m/s, respectively, across both sessions. The sensor was also shown to be a reliable device with coefficients of variation (% CV) never exceeding 0.9 at any speed. During maximal sprinting, the sensor also provided a valid representation of the peak speeds reached (1.6% CV). Slight random errors in timing led to larger random errors in the detection of deceleration values. The results of this investigation have demonstrated that an inertial sensor developed for sports wheelchair applications provided a valid and reliable assessment of the speeds typically experienced by wheelchair athletes. As such, this device will be a valuable monitoring tool for assessing aspects of linear wheelchair performance.
David Whiteside, Olivia Cant, Molly Connolly, and Machar Reid
Quantifying external workload is fundamental to training prescription in sport. In tennis, global positioning data are imprecise and fail to capture hitting loads. The current gold standard (manual notation) is time intensive and often not possible given players’ heavy travel schedules.
To develop an automated stroke-classification system to help quantify hitting load in tennis.
Nineteen athletes wore an inertial measurement unit (IMU) on their wrist during 66 video-recorded training sessions. Video footage was manually notated such that known shot type (serve, rally forehand, slice forehand, forehand volley, rally backhand, slice backhand, backhand volley, smash, or false positive) was associated with the corresponding IMU data for 28,582 shots. Six types of machine-learning models were then constructed to classify true shot type from the IMU signals.
Across 10-fold cross-validation, a cubic-kernel support vector machine classified binned shots (overhead, forehand, or backhand) with an accuracy of 97.4%. A second cubic-kernel support vector machine achieved 93.2% accuracy when classifying all 9 shot types.
With a view to monitoring external load, the combination of miniature inertial sensors and machine learning offers a practical and automated method of quantifying shot counts and discriminating shot types in elite tennis players.
Rienk M.A. van der Slikke, Daan J.J. Bregman, Monique A.M. Berger, Annemarie M.H. de Witte, and Dirk-Jan (H.) E.J. Veeger
on performance and to explore the relationship between match and best performance, a single outcome measure should be used in both conditions. A recently introduced method based on inertial sensors allows for objective performance estimations in both match and best conditions in a reliable and
Jonathan S. Akins, Nicholas R. Heebner, Mita Lovalekar, and Timothy C. Sell
Ankle ligament sprains are the most common injury in soccer. The high rate of these injuries demonstrates a need for novel data collection methodologies. Therefore, soccer shoes and shin guards were instrumented with inertial sensors to measure ankle joint kinematics in the field. The purpose of this study was to assess test-retest reliability and concurrent criterion validity of a kinematic assessment using the instrumented soccer equipment. Twelve soccer athletes performed athletic maneuvers in the laboratory and field during 2 sessions. In the laboratory, ankle joint kinematics were simultaneously measured with the instrumented equipment and a conventional motion analysis system. Reliability was assessed using ICC and validity was assessed using correlation coefficients and RMSE. While our design criteria of good test-retest reliability was not supported (ICC > .80), sagittal plane ICCs were mostly fair to good and similar to motion analysis results; and sagittal plane data were valid (r = .90−.98; RMSE < 5°). Frontal and transverse plane data were not valid (r < .562; RMSE > 3°). Our results indicate that the instrumented soccer equipment can be used to measure sagittal plane ankle joint kinematics. Biomechanical studies support the utility of sagittal plane measures for lower extremity injury prevention.
Live S. Luteberget, Benjamin R. Holme, and Matt Spencer
indoors, thus not useable for indoors sports such as team handball. In recent years, an inertial measurement unit (IMU) has been integrated into GPS devices, to provide additional information relating to physical loads during games and training. IMUs consist of the inertial sensors accelerometers and
Vincent Shieh, Ashwini Sansare, Minal Jain, Thomas Bulea, Martina Mancini, and Cris Zampieri
eventually in clinical practice. Systems that combine wearable inertial sensors with the software to collect and automatically analyze various motor tasks may have particular use in clinical research and practice. One such system, which is commercially available, is the MobilityLab system (APDM, Inc
Martin Buchheit and Ben Michael Simpson
With the ongoing development of microtechnology, player tracking has become one of the most important components of load monitoring in team sports. The 3 main objectives of player tracking are better understanding of practice (provide an objective, a posteriori evaluation of external load and locomotor demands of any given session or match), optimization of training-load patterns at the team level, and decision making on individual players’ training programs to improve performance and prevent injuries (eg, top-up training vs unloading sequences, return to play progression). This paper discusses the basics of a simple tracking approach and the need to integrate multiple systems. The limitations of some of the most used variables in the field (including metabolic-power measures) are debated, and innovative and potentially new powerful variables are presented. The foundations of a successful player-monitoring system are probably laid on the pitch first, in the way practitioners collect their own tracking data, given the limitations of each variable, and how they report and use all this information, rather than in the technology and the variables per se. Overall, the decision to use any tracking technology or new variable should always be considered with a cost/benefit approach (ie, cost, ease of use, portability, manpower/ability to affect the training program).