Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review

Abstract

Recent technological developments have led to the production of inexpensive, non-invasive, miniature magneto-inertial sensors, ideal for obtaining sport performance measures during training or competition. This systematic review evaluates current evidence and the future potential of their use in sport performance evaluation. Articles published in English (April 2017) were searched in Web-of-Science, Scopus, Pubmed, and Sport-Discus databases. A keyword search of titles, abstracts and keywords which included studies using accelerometers, gyroscopes and/or magnetometers to analyse sport motor-tasks performed by athletes (excluding risk of injury, physical activity, and energy expenditure) resulted in 2040 papers. Papers and reference list screening led to the selection of 286 studies and 23 reviews. Information on sport, motor-tasks, participants, device characteristics, sensor position and fixing, experimental setting and performance indicators was extracted. The selected papers dealt with motor capacity assessment (51 papers), technique analysis (163), activity classification (19), and physical demands assessment (61). Focus was placed mainly on elite and sub-elite athletes (59%) performing their sport in-field during training (62%) and competition (7%). Measuring movement outdoors created opportunities in winter sports (8%), water sports (16%), team sports (25%), and other outdoor activities (27%). Indications on the reliability of sensor-based performance indicators are provided, together with critical considerations and future trends.

On-field player workload exposure and knee injury risk monitoring via deep learning

“…The current generation of wearable sensors are limited by low-fidelity, low resolution, or uni-dimensional data analysis (e.g. velocity) based on gross assumptions of linear regression, which overfit to a simple movement pattern or participant cohort [7], however, researchers have reported success deriving kinematics from these devices for movement classification [40,52]. To improve on these methods, a number of research teams have sought to leverage computer vision and data science techniques, and while initial results appear promising, to date they lack validation to ground truth data, or relevance to specific sporting related tasks [8,49,53].…”

A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU

“…For the input data of our joint impulse estimation models, we use inertial measurement unit (IMU) sensors since they are easy to use outside the lab. IMU sensors are often used in human motion analysis for this reason (Bussmann et al, 2001;Weyand et al, 2001;Alvarez et al, 2008;Camomilla et al, 2018). In addition, they are relatively inexpensive to buy compared to the lab equipment needed to calculate joint contact forces.…”

“…Ideally, such a system would be based on inexpensive, wearable sensors that the patients can easily use at the clinician's practice or even at home. Inertial measurement unit (IMU) sensors and electromyography (EMG) sensors are ideal candidates for this purpose as they are relatively cheap and have been applied successfully in a wide range of human movement analysis tasks (Zhang et al, 2011;Camomilla et al, 2018;De Brabandere et al, 2018;Op De Beéck et al, 2018). Designing such a system requires collecting data in a lab setting where a subject performs the relevant exercises while simultaneously recording data from the cheap portable sensors and the expensive, standard lab sensors.…”

Healthcare 4.0: A review of frontiers in digital health

“…IoT wearable monitoring is an emerging area in Healthcare 4.0 and it is changing the way we capture and process our biological data. The application domain includes remote patient monitoring services, smart home (Alaa, Zaidan, Zaidan, Talal, & Kiah, 2017), hospital patient monitoring, rehabilitation (Bisio, Delfino, Lavagetto, & Sciarrone, 2017), self-tracking, performance sports (Camomilla, Bergamini, Fantozzi, & Vannozzi, 2018), and children, youths and elderly care (Misra, Mukherjee, & Roy, 2018). IoT wearable devices supported by AI-driven intelligent data processing techniques have great potential for early detection of physiological and behavioral changes and identify clinical episodes of several chronic diseases (A. R. M. Forkan, Khalil, Tari, Foufou, & Bouras, 2015).…”