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ReFlex

ReFlex is a wearable sensor that captures electrical signals from muscles during strength‑training workouts, providing real‑time feedback on activation, fatigue, and time‑under‑tension for each rep. The device automatically detects repetitions, summarizes performance scores after each set, and tracks trends over time, helping users identify plateaus and adjust their training for better results.

HQ unknown
Founded 2024310+ followers
Updated 1 month ago

Funding

Funding not disclosed

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Many strength‑training enthusiasts lack objective, muscle‑level feedback during workouts, relying instead on visual cues or generic metrics that do not reveal activation patterns or fatigue progression. This makes it difficult to identify performance plateaus, optimize training volume, and track true muscular adaptations over time.

Solution

ReFlex provides a wearable surface electromyography (sEMG) sensor that attaches to a target muscle and continuously records electrical activity throughout each repetition. The system automatically detects reps, measures time‑under‑tension, and quantifies activation and fatigue in real time. After each set, the data are summarized into performance scores that indicate how the muscle performed and how fatigue accumulated. Users can review these scores to monitor trends across sessions, spot plateaus, and adjust training variables based on concrete muscle‑level insights. The device integrates with existing workout routines without requiring changes to exercise technique.

Target Audience

Primary users are individual strength‑training athletes, personal trainers, and fitness coaches who need precise muscle performance data to refine programming and monitor progress.

Features

  • Surface EMG sensor that captures muscle electrical signals during strength exercises
  • Automatic real‑time rep detection eliminating manual logging
  • Rep‑by‑rep activation tracking to visualize engagement changes within a set
  • Time‑under‑tension measurement for each repetition
  • Fatigue estimation based on signal patterns, presented as post‑set performance scores
  • Trend analytics that display activation and fatigue evolution across multiple workouts
This profile is AI-generated and may contain inaccuracies.