CorNET (PPG heart rate, convolution + LSTM)

helia_edge.models.cornet.build rebuilds the CorNET heart-rate regressor from Biswas et al., IEEE TBioCAS 2019: two convolution stages followed by stacked LSTMs. CorNetParams holds the backend-free config.

from helia_edge.models import CorNetParams, ModelSpec, build

model = build(ModelSpec(params=CorNetParams(unroll=True), input_shape=(1000, 1)))

Input: 8 s of wrist PPG at 125 Hz (1000 samples, one channel). The paper band-passes the signal from 0.1 to 18 Hz and z-scores each window. This preprocessing is the caller’s responsibility.

Default architecture (paper Sec. III, Fig. 6, Table III):

• Two convolution stages. Each is Conv1D (32 filters, kernel 40, valid padding, stride 1), then BatchNorm, ReLU, MaxPooling 4 and Dropout 0.1. The window goes from 1000 samples to 961, 240, 201 and finally 50 timesteps.
• LSTM(128), returning its sequence.
• LSTM(128), returning the last step.
• A single linear output, hr.

Trainable parameters match Table III: 1,312 and 40,992 for the convolutions, and 82,432 and 131,584 for the LSTMs. Table III’s dense row is the two-class identification head (258); the one-neuron HR head has 129.

Some details are not stated in the paper and are reconstructed here:

• Stride and padding are inferred from Table III’s MAC counts.
• Fig. 6 places BatchNorm before ReLU, while the text places it after. The constructor follows Fig. 6.
• The dropout position is not stated.
• The LSTM gate activation is not stated. The paper used Keras 2.0.4, whose LSTM default was hard_sigmoid. The constructor defaults to sigmoid, which current Keras uses and fused LSTM kernels require; recurrent_activation="hard_sigmoid" reproduces the older default.

The model processes one window per call. It is not a streaming model, and the LSTM state starts at zero for every window.

LSTM lowering

CorNetParams.unroll changes only how the LSTMs are built, not their weights:

Form	How to build it	LiteRT lowering	Notes
Rolled	unroll=False (default)	WHILE loop	Float only. With TensorFlow 2.21, INT8 or 16x8 conversion of the loop aborts the converter process. Some engines do not parse WHILE.
Unrolled	unroll=True	Per-timestep FULLY_CONNECTED, LOGISTIC, TANH, MUL and ADD operations	These can be quantized to INT8 and 16x8.

Keras 3 does not emit the fused UNIDIRECTIONAL_SEQUENCE_LSTM operator. A fused export needs a tf-keras rebuild of the same architecture with the weights copied over; that is export tooling, not part of this constructor.

Weights and terms

The paper publishes no code or weights. These constructors produce untrained models, suited to performance measurement only; no heart-rate accuracy is implied.