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Related Experiment Video

Updated: Dec 10, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Indirect Recognition of Predefined Human Activities.

Ojan Majidzadeh Gorjani1, Antonino Proto1, Jan Vanus1

  • 1Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 70833 Ostrava-Poruba, Czech Republic.

Sensors (Basel, Switzerland)
|August 30, 2020
PubMed
Summary
This summary is machine-generated.

This study uses artificial neural networks and logistic regression to recognize room occupant activities. The system, integrating smart home sensors and wearable devices, accurately classifies actions for enhanced wellbeing and environmental control.

Keywords:
activity recognitionartificial neural networkclassificationdeep learningintelligent buildingslogistic regressionpredictionsmart homes

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Area of Science:

  • Artificial Intelligence
  • Smart Home Technology
  • Human Activity Recognition

Background:

  • Smart home automation systems collect environmental data (humidity, CO2, temperature) using KNX standard devices.
  • Wearable gadgets provide additional data streams for activity monitoring.
  • Accurate occupant activity recognition is crucial for personalized smart home services.

Purpose of the Study:

  • To apply artificial neural networks and logistic regression for classifying room occupant activities.
  • To leverage integrated smart home and wearable sensor data for enhanced activity recognition.
  • To enable proactive wellbeing monitoring and optimized environmental control (HVAC) for occupants.

Main Methods:

  • Utilized KNX standard-based devices for smart home data acquisition (humidity, CO2, temperature).
  • Integrated data from two wearable gadgets with environmental sensor data.
  • Employed artificial neural networks and logistic regression algorithms for activity classification.

Main Results:

  • Achieved accurate classification of specific activities performed by room occupants.
  • Demonstrated the effectiveness of combining environmental and wearable sensor data.
  • Validated the performance of both artificial neural networks and logistic regression models.

Conclusions:

  • The developed system accurately recognizes occupant activities, paving the way for intelligent smart home applications.
  • This technology can significantly benefit elderly residents by monitoring wellbeing and ensuring optimal living conditions.
  • The findings support the use of machine learning for personalized environmental control and health monitoring in smart homes.