| --- |
| 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" |
| } |
