Your devices don't
know how you feel.
FocuX is the missing layer between your body and your devices. It senses your physiological state in real time and tells you when to rest.
Download on App Store Open Source on GitHub →Technology floods you with data.
But never listens to your body.
Your devices can push information to you at 1 Gbps, yet they know absolutely nothing about your physiological state. This asymmetry is costing us dearly.
Sense. Compute. Suggest.
Wear the wristband sensor. FocuX translates your physiological signals into a Stamina score (0-100) — your body's battery level.
Capture Signals
8-channel sensor continuously reads forearm physiological signals at high sampling rate. Dry electrodes, no gel — wear it like a watch.
Edge Processing
84-dimensional feature extraction + CoreML on-device inference. Everything runs locally on your iPhone. No cloud, no latency, no privacy concerns.
Gentle Reminders
Based on your personal baseline and rhythm, it suggests breaks at the optimal moment. No interruptions, no forced stops — sense, don't control.
Not a crude single number.
Stamina is a fusion of three orthogonal dimensions, each backed by independent biomarkers.
Consistency
Motor unit recruitment stability. The more stable your activation pattern, the more precise your control.
Tension
Involuntary sustained muscle contraction. You might be clenching without even knowing it.
Fatigue
Spectral shift is the gold-standard biomarker for neuromuscular fatigue (MDF, De Luca 1997).
Open science,
real numbers.
Our models, data, and methodology are fully open source. Here are the core metrics and architecture.
Gesture Classifier
RandomForest (n=200 trees)
Trained on Ninapro DB5 public dataset with Leave-One-Group-Out cross-validation.
Weighted F1: 86.12% | 5 classes | 84-dim features
Feature Pipeline
7 features per channel × 8 channels + 28 inter-channel correlations = 84 dimensions
Time domain: MAV, RMS, WL, ZC, SSC
Frequency domain: MNF, MDF
Spatial: Pearson correlation C(8,2)
Window: 250ms, Overlap: 50%
On-Device CoreML
Stamina model: MLP 6→16→8→1
Supports MLUpdateTask for on-device personalized fine-tuning. 1-minute calibration (30s rest + 30s grip), accuracy improves 15-20%.
Training Data
Public dataset: Ninapro DB5 (.mat) — resampled to 1000Hz, labels mapped to 5 gesture classes.
Self-recorded: 42+ segments (8ch × 1000Hz), 5 gesture classes (shoot, left, right, up, down), 6-10 segments each.
Emotion data: WESAD dataset transfer learning — stress/non-stress binary classification, LogisticRegression, 6-dim features.
Deep Learning Backup
1D-CNN (PyTorch)
Conv1d(8→32, k7) → Conv1d(32→64, k5) → Conv1d(64→128, k3) → FC(128→64→N)
Apple Silicon MPS | 50 epochs | Dropout 0.5
Export Formats
Models export in multiple formats covering the full research-to-deployment pipeline:
.pkl sklearn
.onnx cross-platform
.mlpackage iOS CoreML
.pt PyTorch
.joblib lightweight
It learns you.
No two bodies are the same.
Everyone's muscle patterns, fatigue thresholds, and work rhythm are different. FocuX doesn't apply a generic model to everyone — it continuously learns on your device, getting better the more you use it.
Layer 1: Instant Calibration
After each work session, you report how you actually felt (focused / okay / a bit tired / exhausted). The system compares its prediction with your feedback and adjusts the offset using exponential smoothing (α=0.3) in real time.
First feedback takes effect immediately. No waiting, no large datasets needed.
Layer 2: CoreML Fine-tuning
Every 3 feedbacks trigger MLUpdateTask to fine-tune the MLP network via SGD (lr=0.01, 10 epochs). New weights are saved as versioned model files.
Model version increments, never lost. Your personalized model grows with you.
Calm awareness,
not another notification.
Following the Calm Technology principle — information lives at the edge of attention until it truly matters.
Stamina Ring
Real-time stamina ring, 0-100 intuitive display of your body's endurance.
Live Activity
Persistent on Lock Screen + Dynamic Island with system-driven timer.
MVC Calibration
Two-phase calibration: 10s resting baseline + 5s maximum grip. Full muscle range recorded.
Session History
Complete record of stamina curves and dimension changes for every work session. Daily overview + pending feedback tracking.
AI Coach
On-device LLM-powered daily coaching summary — trend insights + actionable body advice.
BLE Resilience
Auto-pause on disconnect, 5-min timeout, wear-off detection, battery monitoring.
Immersive Focus
Full-screen focus mode — breathing animation, long-press to end (anti-mistouch), session summary + particle effects.
Home Widgets
4 home screen widgets — session count, stamina value, weekly trend, dashboard. Everything at a glance.
Zero Cloud
All computation runs locally on iPhone. No servers, no accounts, 100% private.
Building in public.
From the first line of code to the App Store — the full iteration journey. Every version is documented in GitHub Issues.
Built by Jiajun Wu.
FocuX is my graduation project at the College of Future Technology, Shenzhen Technology University — and an independent product that's growing.
I believe technology should sense the human state, not just wait for human input. The current version starts with forearm physiological signals — future iterations will expand to more sensing modalities, so your devices truly understand you.
The vision: an operating system layer between your body and your devices. Not VR goggles, not brain chips — just quiet, continuous awareness that makes your tools work with your body, not against it.