Create README.md

README.md

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+---
+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"
+}