Today I did a sanity test as I worked on a pipeline to reprocess video captured off lowend devices. I thought I'd share the results. Between sessions comparing rep a to rep the results are okay, the coefficient of variance is 0.084 (about 85%). Within a session
Traditional fitness apps treat training as a static spreadsheet: do 3 sets of 10 reps, rest 2 minutes, and repeat. But your body is a dynamic biological system. On some days, your muscles recover rapidly; on others, fatigue lingers. To bridge this gap, SwiftMo features an Estimated Muscle Fatigue engine
In SwiftMo Power, tracking how much work you perform is only half the equation. To truly optimize your training, you need to understand how your body fatiguing behaves during a set. This is where Performance Decay (shown as Effort Level for colloquial profiles) comes in. 1. What is Performance Decay?
If you’ve spent any time looking at the analytics dashboard in the SwiftMo Power app, you’ve likely encountered a graph that traces a steady downward curve as duration increases. This is your Power Curve—a signature profile of your ability to generate force quickly over time. But what
In strength sports, gymnastics, and calisthenics, we all know that doing a movement slowly can be harder than doing a movement quickly. Slow reps also count! While power nicely captures the difficulty of quick movements, and is very good for observing changes in performance, it can't capture slow
try it out: app It’s still in beta, and there’s a huge list of improvements on the way, along with general polish and user experience upgrades: but it’s here. My massively ambitious fitness app that tells you how powerful you are. In April 2024, I came to
Power is a fundamental training aspect and allows us to infer how well an exercise is done, as well as target the exercise more precisely for different training outcomes as different muscle fiber types and muscular activation patterns change depending on power.