Pressure Myography
Meets Machine Learning

Hall-effect sensors measure forearm pressure patterns while machine learning predicts individual finger movements — no EMG, no electrodes, no patents.

How It Works

1

Sense

Hall-effect sensors in a flexible grid detect pressure changes on the forearm as muscles contract. The FlexGrid-60 uses a 15×4 array — 60 sensors capturing the full pressure distribution.

2

Predict

An ExtraTreesRegressor trained on paired sensor/actuator data predicts individual finger positions from raw sensor readings alone. No feature engineering — just 60 raw values in, 4 finger positions out.

3

Move

Predicted finger positions drive a prosthetic hand in real time. The LASK5 linear actuator system provides ground truth during training, then the model runs independently on new sensor data.

Proof of Concept: The results below demonstrate that finger movement data can be extracted from pressure myography sensors using machine learning. These models were trained on very small datasets (under 2 minutes of recording) in controlled conditions. Prediction accuracy is limited — we believe that hardware design improvements and substantially larger datasets (10+ minutes of continuous capture) will yield significantly better results. This is early-stage research, not a finished product.

Real Sensor Data → Finger Prediction

60 pressure sensors wrap around the forearm detecting muscle topology changes. The ML model predicts finger positions in real time from sensor data alone. 683 holdout samples — data the model never saw during training.

Sensor → ML → Finger Prediction

0 / 0

Actual (LASK5)

Predicted (ML)

Radial Polar View

Hexagonal Grid

Low Pressure
High Pressure

Finger Prediction Accuracy

Blue is ground truth from the LASK5 actuators. Orange is what the ML model predicted from sensor data alone. Select a finger to compare.

FlexGrid-60 60 sensors — ExtraTrees — R² = 0.660

FlexGrid-64 — Actual vs Predicted

Actual (LASK5) Predicted (ML)
OM-12 SensorBand 12 sensors — RandomForest — R² = 0.264

OM-12 SensorBand — Actual vs Predicted

Actual (LASK5) Predicted (ML)
Why the difference? The FlexGrid-60 has 5× more sensors (60 vs 12) and captures full hand pressure distribution. The OM-12 uses 3 separate SensorBand banks with only 4 sensors each — less spatial resolution means less signal for the ML model to work with.

Model Selection

Six algorithms evaluated on the same train/test split. ExtraTrees won with the best balance across all four fingers, especially the harder-to-predict pinky (label_3).

Model Comparison (FlexGrid-64)

R² score on 683 holdout samples. Higher is better. ExtraTrees selected as best overall performer.

Devices

FlexGrid-60

4×16 hall-effect pressure sensor grid. The current-generation device with full hand pressure distribution capture.

Sensors60 (15×4)
Best R²0.660
ModelExtraTrees (300)
Training Data1,669 samples

OM-12 SensorBand

3 SensorBand banks with 4 sensors each. Legacy wearable form factor — the original proof-of-concept device.

Sensors12 (2×6)
Best R²0.264
ModelRandomForest (100)
Training Data~6,400 samples

Methodology

1

Data Capture

Sensor packets and LASK5 labels received over UDP, paired by timestamp

2

CSV Export

Paired data saved with sensor columns and label columns (label_0 through label_3)

3

Train/Test Split

Holdout data kept completely separate — never seen during training

4

Model Training

Tree-based regressors trained on sensor-to-label mapping (raw values, no feature engineering)

5

Inference & Export

Trained model predicts labels from holdout sensor data; results exported as JSON for visualization

Key finding: Sensor baselines shift between sessions (mean values drift by ~300), making cross-session prediction unreliable. All training and test data must come from the same session until calibration/normalization is solved.