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A Trainable Open-Source Machine Learning Accelerometer Activity Recognition Toolbox: Deep Learning Approach.

Fluri Wieland¹, Claudio Nigg¹

¹Department of Health Science, Institute of Sports Science, University of Bern, Bern, Switzerland.

|June 14, 2024

Summary

This summary is machine-generated.

This study introduces HumanActivityRecorder, an open-source smartphone tool for accurate human activity recognition. It uses deep learning to achieve 87% accuracy in classifying behaviors, improving scientific research repeatability.

Keywords:

accelerometry activity classification activity recognition activity recorder deep learning deep learning algorithm digital health application machine learning open source sensor device smartphone app

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

Area of Science:

Human-computer interaction
Biomedical engineering
Machine learning for healthcare

Background:

Current activity trackers lack the accuracy and open-source nature required for scientific research.
Existing movement determination software is insufficient for precise scientific applications.

Purpose of the Study:

To develop an accurate, trainable, and open-source smartphone-based activity-tracking toolbox.
To create a system adaptable to new behaviors for research applications.

Main Methods:

A semisupervised deep learning approach was utilized.
Activity classification was based on accelerometry and gyroscope data.
The model was trained and validated using both proprietary and public datasets.

Main Results:

The HumanActivityRecorder achieved approximately 87% accuracy in classifying 6 distinct behaviors.
The developed algorithm demonstrated superiority over a dimension-adaptive neural architecture model.
Robustness against variations in sampling rate and sensor dimensions was confirmed.

Conclusions:

HumanActivityRecorder offers a versatile, retrainable, and accurate open-source solution for activity tracking.
The toolbox facilitates adaptation to specific research behaviors and enhances scientific study repeatability.
Continuous testing on new data ensures the ongoing utility and accuracy of the system.