Human activity detection

Summary

This video discusses the importance and implementation of a human activity detection model in the context of Big Data. Highlighting its significance in medical care and elderly support, the script details the dataset from the UCI repository, which includes eight attributes and eleven activity classes. After pre-processing, feature extraction, and model training using a decision tree, the model achieved over 73% accuracy in classifying activities based on attributes like Z coordinates and tag identifiers. The video concludes with suggestions for improving model performance through time series analysis.

Takeaways

• 🧑‍🔬 Activity detection is increasingly important in medical care, supporting elderly independent living and emergency assistance.
• 📈 Companies like Fitbit and Apple rely on activity detection for health monitoring and safety features, respectively.
• 📚 The data set used for the project was sourced from the UCI Machine Learning Repository and includes eight attributes and eleven classes.
• 👥 The data set was created by recording five people performing various activities over five sessions each.
• 📊 The data set attributes include sequence name, tag identifier, timestamp, date, and x, y, z coordinates, along with the activity classification.
• ⚙️ Pre-processing involved checking for missing values and removing highly correlated activities, resulting in three main classes: lying, walking, and sitting.
• 📈 Feature extraction and analysis identified highly correlated attributes like timestamp, tag identifier, and x, y, z coordinates.
• 📉 Box plots were used to visualize variations in attributes across different activities, aiding in attribute selection for the model.
• 🌐 A 70/30 train-test split was found to be optimal for model training, with 5-fold cross-validation repeated three times for parameter tuning.
• 🔑 The decision tree model was chosen for classification, using attributes like x, y, z coordinates, timestamp, and tag identifier.
• 📊 The model achieved over 73% accuracy in classifying activities, with the Z coordinate being particularly effective for distinguishing lying and walking.
• 🤖 Error analysis showed some confusion between lying and sitting, and walking and sitting, suggesting room for improvement in the model.

Q & A

Why is activity detection important in the field of medical care?

Activity detection is important in medical care because it can support the elderly for independent living and can be a life-saving feature, such as in fall detection systems that automatically alert emergency services if a person falls and doesn't get up for a while.

Which companies are mentioned in the script that rely on activity detection?

Fitbit and Apple are mentioned as companies that rely on activity detection. Fitbit for tracking physical activities and Apple for features like fall detection in their Apple Watch products.

What is the source of the data set used for the human activity detection model?

The data set used for the human activity detection model was obtained from the UCI Machine Learning Repository.

How many people were involved in the creation of the data set and what were they asked to do?

Five people were involved in the creation of the data set, and they were asked to perform a sequence of activities over five times.

What are the eight attributes contained in the data set?

The eight attributes in the data set are the sequence name, tag identifier, timestamp, date, and the x, y, z coordinates, followed by the activity classification.

What was the reason for removing certain activities during the pre-processing stage?

Certain activities were removed during pre-processing because they were highly correlated, such as active researches lying and lying down, which had extremely similar variations in terms of their features.

How many class labels were retained after the pre-processing of the data set?

After pre-processing, three class labels were retained: lying, walking, and sitting.

What was the best train-test split ratio found for the model?

The best train-test split ratio found for the model was 70/30.

Which model was used to classify the data based on the given attributes?

A decision tree model was used to classify the data based on the given attributes.

What attributes were selected for training the decision tree model?

The attributes selected for training the decision tree model were the x, y, z coordinates, along with the timestamp and the tag identifier.

What was the overall accuracy achieved by the decision tree model in classifying the activities?

The decision tree model achieved an accuracy of over 73% in classifying the activities.

What was the main issue identified in the error analysis of the decision tree model?

The main issue identified in the error analysis was that the model sometimes confused activities like lying with sitting about 16% of the time, and walking with sitting about 12% of the time.

How can the performance of the decision tree be improved further?

The performance of the decision tree can be improved by conducting a time series analysis of each attribute, which is likely to increase accuracy.