ncdisrup-ai / MotionCaptureHandPostures

Classify the motion capture from hand postures through supervised learning models

Motion Capture Hand Postures

Classify the motion capture from hand postures through supervised learning models

🤔 What is this?

Description: Provide the best possible classification models Testing exclusively the following models

• Linear models
• Tree Based models
• Naive Bayes
• K-Nearest Neighbours

For Motion Capture from Hand Postures.

Notes for processing the data:

• The Variable to Classify is Class.
• Pay attention to the variable User as it is numeric and probably should be considered categorical
• Eliminate the first row of the data set as there are only Zeros
• There are plenty of missing values identified by '?' These should be handled accordingly

Models should examine different hyperparameters and select the best one [Remember: everything else being similar, the simplest models should be prefered] You should do Simple Cross Validation (testing=25% of all data) for evaluating the models, but the same data partition must be used for all models - DO NOT USE KFOLD CV

This involves Data Understanding, research and design of Artificial Intelligence supervised learning algorithms for classification problem in time series

A typical data science project has several phases (Cross-Industry Standard Process for Data Mining (CRISP-DM)). Phases adapted to this project

1. Business Understanding: Understand the business problem and define the objectives of the project.

2. Data Understanding: Understand the data and its structure. We look at the data types, missing values, and other characteristics of the data. Discover and visualize the data to gain insights.

• Load Data
• PCA for reducing features (Need for?)
• Exploratory Data Analysis (EDA): Data Analysis Process with estatistical analysis and data visualization for knowing data and obtain findings a) Data Exploration: Examining summary statistics, visualizing data distributions, and identifying patterns or relationships. b) Data Visualization: Presenting insights through plots, charts, and graphs to communicate findings effectively

3. Data Preparation: Prepare the data for modeling by cleaning it up and transforming it into a format that can be used by machine learning algorithms.

• Data Cleaning: Handling missing values and categorical values, removing outliers and duplicates, and ensuring data quality.
• Data Transforming
• Feature Engineering: Transforming variables, creating new features, or selecting relevant variables for analysis. -- Feature selection: selecting the most useful features to train on among existing features. -- Feature extraction: combining existing features to produce a more useful one (e.g. handling missing data, encoding variables, dealing with categorical variables, dimensionality reduction algorithms ...). -- Creating new features by gathering new data.
• PCA for reducing features (or in the begining after loading data) (need for?)
• Data Visualization after Data Preparation: Presenting insights through plots, charts, and graphs to communicate findings effectively

4. Modeling / Select and Train Models: We create a model that can be used to make predictions or classify new data.

• Retrieve Class (y) from the dataset (X)
• Split Data
• Normal Test
• OverSampling (SMOTE) and undersampling (need for?)
• Calculate the weights for each sample based on the feature (transaction amount) and class weight (need for?)
• Intermediate Results (need for?)
• Train model (w/cross-validate) (based on OverSampling (SMOTE) and undersampling) (need for?)

5. Evaluation: We evaluate the model’s performance and determine whether it meets the business objectives.

• Evaluation using cross-validate
• Fine-tune Models
• Model Evaluation on Test Set

6. Deployment Deploy the model into production and monitor its performance over time.

📚 Data

Data with the projects (to train the model and to apply the model) are in data dir.

Task Dataset: Motion Capture Hand Postures https://archive.ics.uci.edu/dataset/405/motion+capture+hand+postures

The files 'Postures.csv' is provided

5 types of hand postures from 12 users were recorded using unlabeled markers on fingers of a glove in a motion capture environment. Due to resolution and occlusion, missing values are common.

Due to the manner in which data was captured, it is likely that for a given record and user there exists a near duplicate record originating from the same user. We recommend therefore to evaluate classification algorithms on a leave-one-user-out basis wherein each user is iteratively left out from training and used as a test set. One then tests the generalization of the algorithm to new users. A 'User' attribute is provided to accomodate this strategy.

instances: 78095 Dataset Characteristics Multivariate

Data is provided as a CSV file. A header provides the name of each attribute. An initial dummy record composed entirely of 0s should be ignored. A question mark '?' is used to indicate a missing value. A record corresponds to a single instant or frame as recorded by the camera system.

'Class' - Integer. The class ID of the given record. Ranges from 1 to 5 with 1=Fist(with thumb out), 2=Stop(hand flat), 3=Point1(point with pointer finger), 4=Point2(point with pointer and middle fingers), 5=Grab(fingers curled as if to grab). 'User' - Integer. The ID of the user that contributed the record. No meaning other than as an identifier. 'Xi' - Real. The x-coordinate of the i-th unlabeled marker position. 'i' ranges from 0 to 11. 'Yi' - Real. The y-coordinate of the i-th unlabeled marker position. 'i' ranges from 0 to 11. 'Zi' - Real. The z-coordinate of the i-th unlabeled marker position. 'i' ranges from 0 to 11.

Each record is a set. The i-th marker of a given record does not necessarily correspond to the i-th marker of a different record. One may randomly permute the visible (i.e. not missing) markers of a given record without changing the set that the record represents. For the sake of convenience, all visible markers of a given record are given a lower index than any missing marker. A class is not guaranteed to have even a single record with all markers visible.

🚀 Quick Install

jupiter notebook (.ipynb) file in model dir

📖 Documentation

Please see the description in .ipynb about this project.

🚀 Results

Executing the list of scalers, imputers,models in a total of 126 models we can verify that RandomForest, with standard scaler and simple imputer has the better f1 (0.9901). In terms of models ther macro order is RandomForest, DecisionTree , KNN (some time can surpass DecisionTree depending on the hyperparameters), GaussianNB