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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

Physical Workload Tracking Using Human Activity Recognition with Wearable Devices.

Jose Manjarres¹, Pedro Narvaez¹, Kelly Gasser¹

¹Department of Electrical and Electronics Engineering, Universidad del Norte, Barranquilla 081001, Colombia.

Sensors (Basel, Switzerland)

|December 22, 2019

Summary

This study introduces a wearable sensor system for accurately computing physical workload by combining human activity recognition and heart rate monitoring. The framework offers scalable health and fitness insights for workplace applications.

Keywords:

human activity recognition machine learning for real-time applications physical workload wearable systems for healthcare

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Published on: December 11, 2015

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Area of Science:

Biomedical Engineering
Ergonomics
Wearable Technology

Background:

Accurate physical workload assessment is crucial for workplace health and fitness applications.
Existing methods may lack scalability or real-time monitoring capabilities.

Purpose of the Study:

To develop a scalable framework for workload computation using wearable sensors.
To integrate human activity recognition and heart rate measurements for comprehensive workload analysis.

Main Methods:

Utilized two wearable sensors for motion and heart rate detection.
Employed machine learning algorithms, specifically a random forest classifier, for activity recognition.
Integrated the Frimat's score from ergonomics to compute physical workload from heart rate data.

Main Results:

Achieved 97.5% accuracy in activity classification during training and validation.
Demonstrated real-time reliability with 92% accuracy in tests with 20 subjects.
Successfully tracked body adaptation to exercise routines in a 20-day case study.

Conclusions:

The proposed system provides a reliable and scalable solution for remote, multi-user workload monitoring.
This framework supports experts in ergonomics and workplace health by offering objective workload data.
The system's ability to detect physiological adaptation highlights its potential in personalized fitness and health management.