The fitness industry has been in the process of digital transformation for years. New training programs and trends are presented to an audience of millions via YouTube, training progress is tracked and evaluated using apps, and virtual training and home workouts have enjoyed massive popularity since the start of the corona crisis at the latest. Particularly in weight training, fitness trainers are vital for support in studios because of the high risk of injury – until now. While it is already common practice today to check one’s own posture and position during training via video, computer vision makes it possible to evaluate and assess video material in this field more accurately than the human eye.

A technology is used that is similar to the Attention Tracking already introduced in the retail industry. Human Pose Estimation enables an algorithm to recognize and estimate the posture and pose of people on video. For this purpose, the position of the joints and their position in relation to each other is determined. Since the algorithm has learned what the ideal and safe execution of a fitness exercise should look like, deviations from it can be detected and highlighted automatically. Implemented in a smartphone app, this can be done in real-time and with an immediate warning signal and warn in time of dangerous errors instead of analyzing movements only afterward. This promises to significantly reduce the risk of injury during strength training, make training without a fitness trainer safer, and reduce the cost of safe strength training.

Human Pose Estimation is a further step towards digital fitness training. Smartphones are already well established in fitness training, and apps that make training safer are likely to be well received by the broad user base.