Strength Training Characteristics of Different Loads Based on Acceleration Sensor and Finite Element Simulation

Area of Science:

• Biomechanics
• Sports Science
• Wearable Technology

Background:

• Strength training exercises like deep squats, bench presses, and hard pulls are crucial for physical enhancement.
• Accurate measurement of exercise force is essential for optimizing training intensity and effectiveness.
• The application of sensor technology for quantifying strength training forces remains underexplored.

Purpose of the Study:

• To investigate the dynamic characteristics and appropriate force levels during deep squat, bench press, and hard pull exercises.
• To validate sensor-based strength measurement data using biomechanical simulation software.
• To establish scientifically effective exercise intensity guidelines based on measured forces.

Main Methods:

• Utilized a real-time wireless motion capture system with wearable sensors to collect dynamic data from 30 young men performing three key strength exercises.
• Employed AnyBody 5.2 and ANSYS 19.2 software to simulate and verify the collected tibial data, ensuring authenticity.
• Analyzed the relationship between external forces, joint mechanics, and exercise performance.

Main Results:

• Determined optimal force percentages of one-repetition maximum (1RM) for effective training: 40% 1RM for deep squats (targeting elbow, hip, and knee joints) and bench press, and 80% 1RM for hard pull.
• Confirmed that external forces are the primary drivers of bone adaptation and change.
• Validated that knee joint mechanical characteristics can be accurately simulated using Finite Element Analysis and AnyBody models.

Conclusions:

• Wearable sensor technology provides a valuable tool for measuring exercise dynamics and optimizing strength training.
• Established specific force guidelines (40% 1RM for squats/bench press, 80% 1RM for hard pull) for scientifically effective strength development.
• The study confirms the utility of biomechanical simulations for verifying real-world exercise data and understanding joint mechanics.