README.md

---
tags:
- time-series-classification
- human-activity-recognition
- multimodal
- cnn-lstm
- sensor-data
datasets:
- MultiModal_HumanActivity_SensorStream
license: apache-2.0
model-index:
- name: HAR_MultiModal_Classifier
  results:
  - task:
      name: Time Series Classification
      type: time-series-classification
    metrics:
    - type: accuracy
      value: 0.931
      name: Sequence Accuracy
    - type: weighted_f1
      value: 0.925
      name: Weighted F1 Score
---

# HAR_MultiModal_Classifier

## 🏃 Overview

The **HAR_MultiModal_Classifier** is a deep learning model designed for **Human Activity Recognition (HAR)**. It classifies complex human activities from raw time-series sensor streams, utilizing data from accelerometers (Acc\_X, Y, Z), gyroscopes (Gyro\_X, Y, Z), and contextual physiological metrics (Heart\_Rate\_BPM, Calories\_Burned\_kJ, Device\_Location) simultaneously.

## 🧠 Model Architecture

The architecture is a specialized **Convolutional Neural Network (CNN) combined with a Long Short-Term Memory (LSTM)** network, optimized for processing sequential, high-frequency sensor data.

* **Input:** Sequences of 50 timesteps, containing 9 features per step (6 sensor, 3 contextual/physiological).
* **CNN Layer:** Extracts spatial features and localized patterns from the sensor data windows.
* **LSTM Layer:** Captures the temporal dependencies and long-range sequential dynamics inherent in human motion (e.g., the cyclical pattern of "Walking").
* **Classification Head:** A dense layer with Softmax activation outputs the probability distribution over the 6 activity classes.
* **Target Classes:** Walking, Sitting, Running, Lifting\_Heavy, Typing, Climbing\_Stairs.

## 🎯 Intended Use

This model is ideal for applications requiring continuous, precise activity monitoring:

1.  **Smart Wearable Devices:** Powering real-time activity tracking and fitness coaching.
2.  **Health Monitoring:** Detecting falls, anomalous activity, or adherence to prescribed exercise routines.
3.  **Contextual Computing:** Providing accurate context for mobile applications and ambient intelligence systems.
4.  **Robotics and Automation:** Training robots to understand human motion and collaboration.

## ⚠️ Limitations

1.  **Device Dependence:** Performance is highly dependent on sensor quality, sampling rate, and device placement (Wrist, Chest, Back, etc.). Deviations from the `Device_Location` in the training set may reduce accuracy.
2.  **Activity Overlap:** The model may confuse activities with similar movement signatures (e.g., fast walking vs. slow jogging), despite multimodal input.
3.  **Subject Variance:** The model's accuracy may vary across new subjects due to differences in gait, body mass, and movement style, necessitating fine-tuning for personalized deployment.

---

### MODEL 2: **AspectScorer_ReviewBERT**

This model is a multi-output regression model based on BERT, trained to predict multiple numerical aspect scores from a single raw text review.

#### config.json

```json
{
  "_name_or_path": "bert-base-uncased",
  "architectures": [
    "BertForMultipleRegression"
  ],
  "hidden_size": 768,
  "model_type": "bert",
  "num_hidden_layers": 12,
  "vocab_size": 30522,
  "problem_type": "multi_output_regression",
  "num_labels": 3,
  "output_labels": ["Aspect_Performance", "Aspect_Price_Value", "Aspect_Aesthetics"],
  "min_rating": 1.0,
  "max_rating": 5.0,
  "transformers_version": "4.35.2"
}