Strength training is one of the most effective ways to build long-term health—but it also has a common failure mode: form drift without supervision. When you’re lifting alone, small deviations (joint misalignment, compensation, overextension) can compound across reps and increase injury risk.
This paper introduces FlexGuard, a design space for creating on-body feedback systems that help trainees self-correct and train more safely—especially when a coach isn’t present.
The core problem
Most wearable feedback ideas sound good on paper, but they’re hard to design well because:
- You can’t evaluate feedback in a vacuum—you need to feel it while actually lifting.
- Building “real” wearable prototypes early is expensive and slow.
- There’s no clear, shared framework for deciding what to sense, when to trigger feedback, and how to deliver it on the body.
FlexGuard tries to solve this by offering a structured way to think about wearable feedback for strength training—and validating it through multiple stages of study.
How FlexGuard was developed
The research process is refreshingly grounded in practice:
1) Co-design workshops in a gym
The design space was derived from nine co-design workshops where novice trainees and expert trainers paired up to DIY low-fidelity, on-body feedback concepts, then immediately tested them during exercises.
This matters because it surfaces real constraints that don’t show up in lab-only brainstorming:
- comfort vs. restriction
- clarity vs. distraction
- safety vs. “over-supporting” the user
2) “Speed dating” evaluation with storyboards
Instead of building a dozen expensive prototypes, the authors used storyboards that varied design dimensions, letting participants react to many combinations quickly. This helps validate which parts of the design space are meaningful and how people interpret different feedback types.
3) Proof-of-concept prototype for deeper validation
Finally, the team built a proof-of-concept (PoC) system focused on adaptability—especially for intrinsic (material/strap-based) feedback, which is less explored than vibration-only approaches.
The FlexGuard design space (what you can actually use)
FlexGuard is organized into two big buckets:
A) Sensing & Triggering
This is about deciding what the system watches and when it reacts:
- Trigger metric: What signal indicates risk? (e.g., joint angle deviation, movement dynamics, muscle activation, motion path)
- Trigger policy: Who defines “wrong”? (expert-based thresholds vs. personalized baselines)
- Trigger location: Where do you sense? (target joint vs. stabilizing regions vs. compensation regions)
B) Feedback Delivery
This is about how feedback is felt and interpreted:
- Feedback type: extrinsic vs. intrinsic
- Extrinsic: active actuation (vibration, pneumatic pressure, etc.)
- Intrinsic: passive/structural feedback via materials (braces, straps, stiffness, constraints)
- Intensity: subtle cue vs. strong corrective force
- Latency: immediate vs. delayed (timing matters a lot during a rep)
- Location: where on the body feedback is delivered
- Direction: does feedback “push you away” from a bad posture or “guide you toward” a better one?
Connecting sensing to feedback
The interesting part is not just listing dimensions—it’s connecting them so you can reason about end-to-end designs (e.g., “If the risk is shoulder impingement during abduction, what should we sense and what kind of feedback should we deliver, and where?”).
Key takeaways that stuck with me
A few themes from the results feel especially actionable if you’re building fitness wearables:
- Feedback has semantics. People interpret vibration/pressure/constraint differently—not just as “signals,” but as meaning (“warning,” “guidance,” “support,” “restriction”).
- Intensity is a spectrum, not a setting. “Too strong” can feel unsafe or annoying, but “too weak” gets ignored—so adaptable intensity matters.
- Phase matters. A cue that helps during setup may be harmful mid-rep. Strength movements have phases, and good feedback should respect that.
- There’s a tension between safety and learning. Over-correcting or over-supporting can reduce motor learning—so systems should help users improve, not just constrain them.
Why this paper is useful if you’re building products
If you’re working on camera-based form correction, smart straps, haptic bands, or “AI coach” wearables, FlexGuard gives you:
- a vocabulary for design decisions,
- a way to map injury risk → sensing → triggering → feedback,
- and evidence that these dimensions matter in real training contexts.
It’s the kind of framework that can prevent teams from building a demo that “works” but fails in the gym.
What I’d love to see next
Future systems inspired by FlexGuard could go further on:
- personalization over time (learning your baseline and your fatigue patterns),
- context-awareness (exercise type + load + tempo),
- combining intrinsic support with lightweight extrinsic cues,
- and evaluating injury-prevention outcomes longitudinally (not just short lab sessions).
If you’re designing wearables for strength training, FlexGuard is worth reading—not because it gives you one perfect device, but because it gives you a map of the design territory.