Redefining safety through passive, real-time protection powered by wearables.
Event FigBuild 2026 Design-A-Thon
Team Seylon Versalles-Shiggs & Thadar Htet
My Role UX/UI Design, Concept Development
Tools Figma Make, Figma, Claude Code
Timeline 72 hours
Deliverables Concept system, three interactive prototypes, research-backed presentation
The Problem
Female athletes experience significantly higher ACL injury rates than males in many sports due to a combination of biomechanical, anatomical, and hormonal factors. Many of these injuries occur during non-contact movements such as landing, cutting, or pivoting, where neuromuscular control and proprioception play a critical role in stabilizing the knee joint.
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(Groner, 2018; Sherman et al., 2024) (Ntanasis-Stathopoulos et al., 2013; Kacprzak et al., 2024). (Ntanasis-Stathopoulos et al., 2013; Kacprzak et al., 2024).
Opportunity
Preventing Injury Before It Happens
Traditional Training Relies on Observation
Strength programs, rehabilitation protocols, and coaching cues aim to correct biomechanics. However, these approaches rely on periodic observation rather than continuous monitoring during real athletic movement.

(Groner, 2018; Sherman et al., 2024)
Athletes Receive No Continuous Feedback on Joint Mechanics
Without real-time monitoring, dangerous movement patterns often go unnoticed until injury occurs
(Wang & Lee, 2026)
Augmented Proprioception Can Close the Gap
Wearable sensors combined with intelligent feedback systems can continuously monitor biomechanical signals.
 This creates augmented proprioception, allowing athletes and coaches to detect instability and intervene before dangerous movement patterns lead to injury.
(Lee et al., 2024; Wang & Lee, 2026)
Our Solution
The Athena System
Athena is a three-component Augmented Proprioception system designed to detect biomechanical breakdown and guide safer training decisions.
Athena Sleeve
During on-field or on-court training, athletes wear the Athena Sleeve, which augments proprioception through subtle sensory haptic feedback that helps correct unstable movement patterns in real time.
Athena Pod
For weight training sessions, athletes step into the Athena Pod for a rapid scan assessing joint stability, fatigue, and strength readiness. The system then recommends personalized training plans.
Athena App
Insights from both components are delivered through the Athena App, allowing athletes, coaches, and training staff to monitor asymmetry, fatigue, and injury risk over time and set performance goals.
Research that shaped our design decisions
The Knee Already Has Sensors — Athena Makes Them Tangible
The ACL contains mechanoreceptors that detect changes in knee position, tension, and movement . These sensory signals help coordinate neuromuscular reflexes that stabilize the joint during dynamic activity. Athena builds on this natural sensing system by capturing and amplifying these biomechanical signals directly at the knee via haptic signaling to its user.
(Kacprzak et al., 2024; Sherman et al., 2024) (Sherman et al., 2024; Criss et al., 2021)
01
ACL Sensors Detect
Joint Movement
Specialized receptors inside the ACL sense changes in knee position and tension.
02
Signal Travels 
to the
Spinal 
Cord and Brain
Sensory signals are sent through the nervous system to coordinate 
joint stability.
03
Muscles React 
to
Protect the Knee
Surrounding muscles activate to stabilize the joint and resist 
dangerous movement.
Uneven Limb Loading Creates Dangerous Joint Stress
Athletes frequently develop asymmetrical loading patterns, especially after fatigue or previous injury. When one leg absorbs disproportionately greater ground reaction forces during landing or cutting, it places excessive stress on the knee joint and ACL.(Kacprzak et al., 2024; Sherman et al., 2024)
Key takeaway
Injury risk rises sharply as limb asymmetry increases.


Athletes with greater than 15% asymmetry experience nearly four times the injury rate compared to athletes with balanced loading patterns.
Before Athena
Injuries are often preceded by biomechanical signals athletes cannot perceive.
Before most injuries occur, the body produces subtle warning signals such as fatigue-related instability, limb loading asymmetry, and reduced proprioceptive feedback (Kacprzak et al., 2024; Sherman et al., 2024).
Athletes appear healthy during early training phases, but fatigue can quietly alter movement mechanics before symptoms appear.
Subtle biomechanical changes such as joint instability and asymmetrical loading accumulate over time without being consciously perceived.
By the time pain or visible dysfunction occurs, the underlying movement patterns have often been deteriorating for weeks or months.
Most injuries are not sudden events. They are the final outcome of signals the body was producing all along.
After Athena
Athena introduces a new layer of sensory awareness for athletes.
Using real-time movement analysis, Athena detects early instability patterns and surfaces them via haptic signals before athletes experience discomfort or injury.
Athena detects subtle biomechanical changes as they begin to emerge, turning previously invisible instability into real-time awareness.
By surfacing instability patterns early, athletes and coaches can adjust training loads and movement patterns before discomfort develops.
Instead of reacting to pain after damage occurs, athletes can intervene early and maintain healthy movement patterns over time.
By making invisible signals visible, Athena allows athletes to intervene before injury begins.
Explore Athena
Athena Pod Interface
What athletes see when they step out of the Pod. After a rapid pre-session scan, the interface surfaces three body insights - joint instability level, loading symmetry, and hamstring fatigue — alongside a Movement Stability Score and three personalized training focus points for that session.
Athena Coach Dashboard
The tablet-optimized team view. Coaches see team readiness at a glance — average score, high-fatigue athletes, injury risk flags, and scan compliance — then drill into any individual athlete for cycle tracking, movement asymmetry, and individual training planning. Data access is limited to what each athlete has explicitly shared.
Athena Athlete App
The athlete-facing mobile experience. The dashboard shows the readiness score, live load asymmetry, joint stability, and left/right balance which are all pulled from her morning Pod scan. Designed to give athletes a clear, non-alarming read of their body before they step onto the field.
Designing for trust, not anxiety
Athlete-controlled data
Biometric data is encrypted and athlete-owned. Coaches access only what the athlete explicitly shares. Recruiters, scouts, and insurers are permanently blocked from the data layer. Athletes can delete their full history at any time.
Actionable, not alarming
Athena doesn't diagnose injury. It signals biomechanical risk. Onboarding educates athletes on what each haptic pattern means, separating normal fatigue cues from meaningful instability signals. Athletes can enable Focus Mode during competition to reduce haptic sensitivity when full concentration is needed.
Signal, not diagnosis
Persistent risk flags prompt a recommendation to consult a certified athletic trainer — not a clinical conclusion. The system is designed to complement professional judgment, not replace it.
Reflection
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I designed across three interfaces simultaneously. Every decision had to account for how information flowed between the Sleeve, Pod, and App, and how it would read differently for an athlete versus a coach.
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I learned that the 48-hour constraint forced deliberate prioritization, choosing what to prototype in full fidelity and what to leave conceptual is a discipline I want to carry into every project.
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I'm most proud of the safeguards section. I designed Athena to be athlete-controlled, actionable rather than alarming, and explicitly non-diagnostic, which felt like the only responsible direction for wearable health technology.
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If I continued this project, I would conduct usability testing with female athletes and athletic trainers, validate the haptic feedback language, and explore edge cases like returning athletes post-injury and multi-sport athletes with varying movement profiles.