Tuesday 23 September 2025
A new approach to fitness tracking has been unveiled, using video footage of people exercising to identify and classify various physical activities. This innovative system could revolutionize the way we monitor and improve our health.
The researchers behind this project have developed a deep learning framework that uses pre-trained convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to analyze videos of people performing different exercises. The system is designed to capture both spatial and temporal dynamics, allowing it to identify fine-grained movements and recognize actions even when they are performed in similar environments.
The team has tested their approach on a dataset of 35 fitness-related activity classes, including exercises such as squats, lunges, and push-ups. They used a combination of pre-trained models, including Vision Transformers (ViT) and EfficientNets, which were fine-tuned for the specific task of fitness tracking.
The results are impressive, with the best-performing model achieving an accuracy rate of 93.34%. This means that it was able to correctly identify the exercise being performed in nearly 94% of cases.
One of the key advantages of this system is its ability to recognize exercises even when they are performed in similar environments or with varying degrees of intensity. For example, it can distinguish between a person doing squats and someone else performing lunges, even if they are both standing in the same room.
The researchers believe that their approach could have significant implications for healthcare and fitness tracking. It could be used to provide personalized feedback and coaching to individuals, helping them to improve their physical fitness and reduce the risk of injury.
The system is also designed to be scalable and efficient, making it suitable for use on a wide range of devices, from smartphones to wearable fitness trackers. This means that it has the potential to revolutionize the way we track our physical activity and provide us with personalized feedback and guidance.
Overall, this new approach to fitness tracking is an exciting development in the field of artificial intelligence and human-computer interaction. It has the potential to make a significant impact on our health and wellbeing, and could be used in a wide range of applications, from healthcare to fitness and beyond.
Cite this article: “AI-Powered Fitness Tracking System Revolutionizes Exercise Identification”, The Science Archive, 2025.
Fitness Tracking, Video Analysis, Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks, Long Short-Term Memory Networks, Physical Activity, Health Monitoring, Wearable Devices
