Anterior cruciate ligament injury and the associated long-term sequelae, such as immediate reductions in physical inactivity, increased adiposity and increased risk of osteoarthritis throughout adulthood, are a major health concern for adolescent athletes. Current interventions for injury prevention may have limited effectiveness, are susceptible to issues of compliance and have not achieved the widespread acceptance necessary to promote full adoption. Neuromuscular training (NMT) is a well-established training intervention introduced to affect change in modifiable biomechanical risk factors to reduce the risk of injury in these athletes. Despite moderate success, neuromuscular training is still limited by its reliance on subjective feedback and after the fact (i.e., offline) objective feedback techniques. The purpose of this commentary is to discuss technological tools that could be used to enhance and objectify targeted biofeedback interventions to complement NMT. Electromyography, force plates, motion sensors, and camera-based motion capture systems are innovative tools that may have realistic feasibility for integration as biofeedback into NMT programs to improve training outcomes. Improved functional deficit identification and corrective analysis may further improve and optimize athletic performance, and decrease the risk of sports-related injury during sport performance.

• Specific, targeted interventions that isolate injury risk factors and can help correct modifiable neuromuscular deficits are essential.

• Current training interventions for anterior cruciate ligament (ACL) injury prevention have only demonstrated limited effectiveness and have not achieved the widespread acceptance necessary to promote full adoption to reduce ACL injury rates.

• The paper provides an overview of innovative strategies and technological tools that could be used to enhance and objectify targeted biofeedback interventions to complement neuromuscular training (NMT) including electromyography, force plates, motion sensors, and camera-based motion capture systems.

• These strategies utilize biomechanical, physiological, or neuromotor variables for training, automate the quantitative measurement of those variables through a variety of technological modalities, and then feed those measured variables via software to provide information in simplified form for online, visual biofeedback displays.