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 →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.
Wear the wristband sensor. FocuX translates your physiological signals into a Stamina score (0-100) — your body's battery level.
8-channel sensor continuously reads forearm physiological signals at high sampling rate. Dry electrodes, no gel — wear it like a watch.
84-dimensional feature extraction + CoreML on-device inference. Everything runs locally on your iPhone. No cloud, no latency, no privacy concerns.
Based on your personal baseline and rhythm, it suggests breaks at the optimal moment. No interruptions, no forced stops — sense, don't control.
Stamina is a fusion of three orthogonal dimensions, each backed by independent biomarkers.
Motor unit recruitment stability. The more stable your activation pattern, the more precise your control.
Involuntary sustained muscle contraction. You might be clenching without even knowing it.
Spectral shift is the gold-standard biomarker for neuromuscular fatigue (MDF, De Luca 1997).
Our models, data, and methodology are fully open source. Here are the core metrics and architecture.
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
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%
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%.
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.
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
Models export in multiple formats covering the full research-to-deployment pipeline:
.pkl sklearn
.onnx cross-platform
.mlpackage iOS CoreML
.pt PyTorch
.joblib lightweight
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.
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.
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.
Following the Calm Technology principle — information lives at the edge of attention until it truly matters.
Real-time stamina ring, 0-100 intuitive display of your body's endurance.
Persistent on Lock Screen + Dynamic Island with system-driven timer.
Two-phase calibration: 10s resting baseline + 5s maximum grip. Full muscle range recorded.
Complete record of stamina curves and dimension changes for every work session. Daily overview + pending feedback tracking.
On-device LLM-powered daily coaching summary — trend insights + actionable body advice.
Auto-pause on disconnect, 5-min timeout, wear-off detection, battery monitoring.
Full-screen focus mode — breathing animation, long-press to end (anti-mistouch), session summary + particle effects.
4 home screen widgets — session count, stamina value, weekly trend, dashboard. Everything at a glance.
All computation runs locally on iPhone. No servers, no accounts, 100% private.
From the first line of code to the App Store — the full iteration journey. Every version is documented in GitHub Issues.
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.