Resistance training (RT) is widely known to improve health and athlete performance by increasing muscle mass, maximal strength and power output (Beattie et al., 2014; Schoenfeld et al., 2016; Suchomel et al., 2016). The World Health Organization (WHO) recommends RT at least twice a week for adults. RT is crucial for enhancing sport-specific tasks and reducing injury risks.

A key challenge in RT is monitoring intensity and load to maximize benefits and minimize injury risks. Velocity-based training (VBT) is highlighted as a method to assess RT intensity by examining the relationship between movement velocity and load. However, VBT requires devices to measure lifting velocity, with linear position transducers (LPTs) often considered the gold standard. The document mentions recent research validating the use of smartwatches for VBT monitoring.

The research aims to bridge VBT monitoring using smartwatches and musculoskeletal modeling to assess mechanical demands on muscles and joints. The focus is on the "bounce strategy" in exercises like the back squat, which involves increasing velocity during the descent to enhance performance. The research seeks to understand how joint moments and muscle contributions vary with different techniques and loads, especially under higher intensities.

The proposed study is divided into three phases:

1. Data Collection: Recording back squat sessions with and without the bounce strategy, using smartwatches to monitor lifting velocity.
2. Assessment of Net Joint Moments and Muscle Contribution: Using musculoskeletal models to analyze the biomechanical demands during these exercises.
3. Integration and Analysis: Combining VBT data with musculoskeletal modeling to explore the relationship between velocity profiles and biomechanical effects.

The research aims to validate mobile monitoring methods, provide insights into muscle contributions during different techniques, and enhance training safety by promoting evidence-based lifting practices.