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Tasfiya025/
HAR_MultiModal_Classifier

Name: HAR_MultiModal_Classifier
Rating: 2.5 (5 reviews)

time_series_classification

time-series-classification

human-activity-recognition

multimodal

cnn-lstm

sensor-data

MultiModal_HumanActivity_SensorStream

apache-2.0

model-index

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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:

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

⚠️ Limitations

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.
Activity Overlap: The model may confuse activities with similar movement signatures (e.g., fast walking vs. slow jogging), despite multimodal input.
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"
}

Tasfiya025/HAR_MultiModal_Classifier