Quantifying load during physical activity has been of high interest to the research community. For athletes, it is desirable to optimize their exercises to align the applied training load most closely with the value desired by the training plan. Too much load induces a decrease in force production ability and increases the risk of injuries. Exercise load, e.g., during rehabilitation or recreational sports, is also important to avoid injuries for the general population. Training load can be quantified utilizing internal and external measures. External measures include, e.g., the distance traveled, the travel speed, or the lifted weight. Internal load is often measured as a rating of perceived exertion (RPE), which specifies how exhausting an exercise was for a specific person by reporting a single value on a scale. A standard RPE scale is the so-called Borg scale, which ranges from 6 (not exhausting) to 20 (extremely exhausting) . Retrieving such a rating is quickly done by giving subjects a scale to mark their exhaustion a short time after the load concludes. Given this, we aim to build a system that can automatically predict RPE values based on sensor measurements. We hypothesize that such a system could warn users when a significant training overload is experienced to avoid fatigue injuries. In this initial project, we utilize multiple 3D cameras for motion tracking and methods from machine learning to predict subjective RPE values.
For this project, we aim for the maximum effect on exertion. Therefore, the squat exercise was chosen as it involves large muscle groups. The exercises were performed on a so-called flywheel machine. A flywheel training machine does not use a weight that is accelerated downwards by gravity. Instead, all power generated by the subject standing up is stored in a flywheel, transmitted by a belt. This belt is connected to the participant via a hip harness and wrapped around a transmission shaft fixed to the flywheel. Thus, when the participant stands up, he unwraps the belt from the shaft, spinning up the flywheel. Standing up is the concentric movement in a squat. The belt wraps back around the transmission shaft at the topmost position because the flywheel continues to spin. Thus, during the downwards movement, the participant has to deaccelerate the flywheel back down in the eccentric movement. Finally, the subject will again be in a squatting position, as shown in the following figure. In total, N=21 subjects have participated in our study, performing a specific protocol consisting of several sets with 12 repetitions in each set.