Model Card for Asclepius_v1

Asclepius_v1 is a specialized deep learning model designed for real-time medical crisis prediction. It processes multi-modal time-series data from wearable sensors to detect and classify health emergencies.

Model Details

Model Description

Asclepius_v1 utilizes a hybrid architecture combining 1D-CNN, Bidirectional LSTM (BiLSTM), and an Attention Mechanism. This design allows the model to extract local features from high-frequency sensor noise while maintaining a long-term memory of physiological trends.

• Developed by: Koussay Chaanbi
• Model type: Hybrid CNN-BiLSTM-Attention Network
• Language(s): English (Technical Documentation)
• License: MIT
• Model Architecture: - 1D-CNN: For local feature extraction from sensor streams.
  • BiLSTM: To capture temporal dependencies in both forward and backward directions.
  • Attention Layer: To assign weights to critical physiological events within the data window.

Model Sources

• Repository: Asclepius_v1 on Hugging Face
• Hardware Target: Raspberry Pi 4 / Raspberry Pi 5

Uses

Direct Use

The model is intended for integration into wearable health devices (such as smart shirts) to monitor:

1. Heart Rate (HR)
2. Oxygen Saturation (SpO2)
3. Movement (Accelerometer data)

It classifies input data into one of five specific medical crisis categories.

Out-of-Scope Use

This model is not a replacement for professional medical diagnosis or clinical-grade monitoring equipment. It should not be used as the sole basis for medical intervention without human-in-the-loop verification.

Bias, Risks, and Limitations

• Technical Limitations: The model is optimized for edge deployment (float16 quantization), which may lead to a slight drop in precision compared to full-precision desktop models.
• Data Bias: Performance is dependent on the quality and placement of the hardware sensors.

Recommendations

Users should ensure proper sensor calibration and skin contact for reliable inference. The model should be used as a proactive "early warning" system rather than a definitive diagnostic tool.

How to Get Started with the Model

Since the model is provided in .tflite format, use the following code to run inference:

import tensorflow as tf
import numpy as np

# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="model_quantized.tflite")
interpreter.allocate_tensors()

# Get input and output details.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Prepare your sensor data (HR, SpO2, Movement)
# Shape should match: [1, window_size, features]
input_data = np.array(your_sensor_window, dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)

interpreter.invoke()

# Extract prediction
output_data = interpreter.get_tensor(output_details[0]['index'])
print(f"Crisis Classification Result: {np.argmax(output_data)}")