Transforming traditional games into predictive health data using modern edge AI and 9-axis sensor fusion for elderly fall prevention.
Preventing age-related balance loss by bridging the latency gap in cognitive-motor loops.
Aging causes a subtle disconnect between visual input and motor response. A 200ms lag during a sudden slip is the critical difference between recovering balance or sustaining a severe fracture.
Traditional "Knucklebone" or beanbag catching drills require precise, high-speed neural signaling. We modernize these centuries-old coordination games as clinical diagnostic systems.
By capturing high-frequency physical dynamics, our TinyML models analyze motion micro-jitter, identifying early neuro-degenerative markers decades before clinical symptoms present.
Deploying on-device artificial intelligence directly at the edge of rehabilitation devices.
Embedded with a high-fidelity 9-axis inertial measurement unit (IMU) to capture linear acceleration and angular velocity at a robust 104Hz sampling rate.
On-device neural network models classify coordinate toss trajectories and capture muscle instability signatures in real-time without cloud latencies.
Retraining cognitive pathways through interactive, gamified coordination protocols to build neurological resilience and reduce falls by up to 45%.
Test your reflexes and analyze on-device TinyML performance diagnostics.
| Date | Mode | Avg Reaction | Fall Risk |
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| Date | Mode | Avg Reaction | Fall Risk |
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Visual-motor tracking shows excellent neural conduction velocity. Your reaction threshold matches the active baseline, indicating a highly resilient motor stabilization mechanism and minimal risk of balance failure under loading.