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This study created a digital twin of a home to track joint motion during daily activities. The digital twin framework can aid in home rehabilitation and ergonomic design.

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Area of Science:

  • Biomechanics
  • Human-Computer Interaction
  • Digital Health

Background:

  • Assessing human mobility in natural home environments is challenging.
  • Laboratory settings lack ecological validity for real-world joint motion analysis.
  • Previous research has not adequately measured joint motion and classified tasks within a home setting.

Purpose of the Study:

  • To develop a digital twin of a home environment for measuring and visualizing joint motion.
  • To classify activities of daily living (ADLs) using sensor data within the digital twin.
  • To investigate the influence of the home environment on joint motion during ADLs.

Main Methods:

  • A single-bedroom apartment was digitally reconstructed using 3D photogrammetry to create a digital twin.
  • Ten healthy adults performed 19 ADLs while wearing inertial measurement units and ultra-wideband sensors.
  • Supervised machine learning was employed for task classification, with joint motion data registered to the digital home model.

Main Results:

  • Supervised machine learning achieved 82.3% accuracy in classifying daily living tasks.
  • Specific ADLs showed distinct joint motion patterns, e.g., highest shoulder elevation during hair combing and maximal hip/knee flexion during sit-to-stand.
  • Joint motion varied significantly across different rooms, impacting walking speed and ankle dorsiflexion.

Conclusions:

  • Joint motion during ADLs is significantly influenced by the internal home environment.
  • The developed digital twin framework offers a viable method for analyzing human mobility in home settings.
  • This approach has potential applications in home-based rehabilitation, remote patient monitoring, and ergonomic interior design.