Almost every serious fitness app ends up wanting the same screen: a human silhouette with the worked muscles lit up. Users finish a workout and see what they trained. They open an exercise and see what it targets. They tap a muscle and get exercises for it. The feature reads as obvious to users, which is exactly why it’s worth building well, and there are four meaningfully different ways to build it. This post walks through all four with their real tradeoffs.
(Disclosure: we build and sell one of the four at Fitness Visuals, a Rive-based heatmap we ship in our own app. The other three approaches are legitimate and cheaper, and for some apps they’re the right call.)
First, know what the feature actually needs
Before comparing rendering approaches, list your requirements, because they decide the choice for you:
- A data layer. Something has to know that a bench press hits chest, front delts, and triceps. This is a mapping from exercises to muscles, with primary and secondary roles, and no rendering approach provides it for free.
- Front and back views, because lats and glutes exist.
- Male and female body models, if your audience isn’t one demographic.
- Intensity levels, primary vs secondary muscles at minimum, volume-based tiers if you’re ambitious.
- Interactivity, does tapping a muscle need to do anything?
- Theming, does the highlight color need to match your brand or the user’s theme?
- Platforms, one codebase or several?
Approach 1: pre-rendered images
Export a PNG of the body with each muscle group highlighted, show the right image for the exercise. This is how a lot of v1 apps do it, and for an exercise detail page that highlights one muscle group at a time, it’s honestly fine: no libraries, no code, works everywhere.
It stops working the moment you need combinations. A workout that hits chest, triceps, and front delts needs that exact image. With 20+ muscle groups the combinations explode into the millions, so you either constrain the feature to single groups or move on to an approach that composes at runtime.
Approach 2: SVG highlighter libraries
The workhorse of the category. Free, MIT-licensed libraries render the body as SVG polygons and fill the regions you pass: react-native-body-highlighter for React Native, react-body-highlighter for web, bodyheatmap for Flutter. Runtime composition solves the combination problem, most ship male and female models with front and back views, and tap callbacks cover basic interactivity.
The ceiling: highlight changes snap instead of animating, intensity means picking from a fixed color list, the models expose broad regions rather than individual muscles, and each platform needs its own library with its own quirks. We wrote a full breakdown of the SVG highlighter option if this is the route you’re weighing.
Approach 3: Lottie animations
Lottie is excellent for fitness apps, for celebrations, onboarding, and loading states. For a muscle heatmap specifically it’s the awkward middle: a Lottie file plays a fixed animation and doesn’t know about your app’s state, so composing “highlight these five muscles at these intensities” means slicing the body into per-muscle files and orchestrating playback in your own code. You end up hand-building a state machine anyway, just spread across your codebase. The stock Lottie muscle packs on the marketplaces are decorative animations, not interactive components. We’ve written up where Lottie wins and loses in fitness apps in detail.
Approach 4: a state-machine asset
The newest option: ship the body as a single interactive asset that contains its own state machine, built in a tool like Rive. Muscles are inputs on the asset. Your code sets them, the asset handles rendering, transitions, and hit-testing internally. Highlight changes animate smoothly, intensity is a runtime value, taps emit the muscle’s name back to your code, and the same file renders on Flutter, SwiftUI, Android, React, and React Native through official runtimes. The integration is a few lines per platform:
AnatomyHeatmap(
activeMuscles: {Muscle.pectoralisMajor, Muscle.triceps, Muscle.anteriorDeltoid},
)The tradeoffs are the runtime dependency (roughly 200KB) and the fact that building one of these assets is genuine design-plus-engineering work: you need the anatomy artwork, the state machine, and testing across every runtime. That’s the part we sell. Our basic heatmap is $30, the advanced version with four runtime colors and tap-to-identify is $50, both one-time with a lifetime license, and the Flutter integration is open source so you can inspect exactly what you’d be shipping. You can also just poke the heatmap in your browser before deciding anything.
Don’t skip the data layer
Whichever rendering approach you pick, the exercise-to-muscle mapping is a separate problem. You need structured data that says what each exercise works and how hard. Sources: build it yourself from an exercise science reference, pull it from an exercise database API, or buy structured metadata. Model primary and secondary muscles separately from day one, it’s the difference between a heatmap that looks right to lifters and one that lights up half the body for every compound movement.
Picking in one pass
- Single muscle group on a detail page? Pre-rendered images. Done.
- Combinations, zero budget, one platform? An SVG highlighter library.
- Playback flourish, not state? Lottie, but not for the heatmap itself.
- Animated transitions, per-muscle detail, theming, tap-to-identify, or multiple platforms? A state-machine asset, built in-house if you have the design capacity, or bought if you’d rather ship this week.
The feature is worth doing properly: it’s the screen users screenshot and share, and it’s often the visual that separates a polished tracker from a spreadsheet with buttons. Start from your requirements list, and the right approach mostly picks itself.