  This paper introduces a simple yet effective strategy for exercise
classification and muscle group activation prediction (MGAP). These tasks have
significant implications for personal fitness, facilitating more affordable,
accessible, safer, and simpler exercise routines. This is particularly relevant
for novices and individuals with disabilities. Previous research in the field
is mostly dominated by the reliance on mounted sensors and a limited scope of
exercises, reducing practicality for everyday use. Furthermore, existing MGAP
methodologies suffer from a similar dependency on sensors and a restricted
range of muscle groups, often excluding strength training exercises, which are
pivotal for a comprehensive fitness regimen. Addressing these limitations, our
research employs a video-based deep learning framework that encompasses a broad
spectrum of exercises and muscle groups, including those vital for strength
training. Utilizing the "Workout/Exercises Video" dataset, our approach
integrates the X3D and SlowFast video activity recognition models in an
effective way to enhance exercise classification and MGAP performance. Our
findings demonstrate that this hybrid method obtained via weighted ensemble
outperforms existing baseline models in accuracy. Pretrained models play a
crucial role in enhancing overall performance, with optimal channel reduction
values for the SlowFast model identified near 10. Through an ablation study
that explores fine-tuning, we further elucidate the interrelation between the
two tasks. Our composite model, a weighted-average ensemble of X3D and
SlowFast, sets a new benchmark in both exercise classification and MGAP across
all evaluated categories, offering a robust solution to the limitations of
previous approaches.
