# Application of Machine Learning Approach to Classify Human Activity Level Based on Lifelog Data

## TL;DR

This paper presents a machine learning model that classifies human activity levels using lifelog data from wearable devices, achieving high accuracy.

## Contribution

The novel contribution is the development of an accurate activity-level classification model using integrated wearable data and multiple machine learning algorithms.

## Key findings

- Eighty percent of the data was used for training, and the model achieved high accuracy in classifying activity levels.
- Heart rate and step count were found to be effective features for activity classification.
- Deep learning models like CNN and RNN performed well alongside traditional machine learning models.

## Abstract

The present paper provides a human activity-level classification model based on the patient’s lifelog collected from wearable devices. During about two months, the heart rate, step count, and calorie consumption for a total of 182 patients were collected from a wearable device. Using the lifelog data, the machine learning models were developed to classify the physical activity status of patients into five levels. Three types of wearable data with heart rate, step count, and calorie consumption were pre-processed as integrated data in time series. A total of 80% of the integrated data was used as the training dataset, and the remaining 20% was used as the test dataset. Sixteen algorithms were evaluated, including 12 traditional machine learning models (SVM, KNN, RF, etc.) and 4 deep learning models (CNN, RNN, etc.), and cross-validation was performed by dividing the training dataset into 5 folds. By changing the parameters required for training, the models with optimal parameters were derived. The performance of the final models with the new patient lifelog data was evaluated, and it was shown that the classification for human activity level based on heart rate and step count can be performed with high accuracy.