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Simple fall criteria for MEMS sensors: data analysis and sensor concept.

Alwathiqbellah Ibrahim1, Mohammad I Younis2

  • 1Department of Mechanical Engineering, State University of New York at Binghamton, Binghamton, NY 13902, USA. aibrahi4@binghamton.edu.

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Summary

This study introduces a simple fall detection concept using detailed human fall and daily activity data. The new method uses multiple MEMS sensors to accurately detect falls while preventing false alarms during everyday activities.

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Area of Science:

  • Biomechanics
  • Sensor Technology
  • Human Motion Analysis

Background:

  • Accurate fall detection is crucial for elderly care and patient monitoring.
  • Existing systems often struggle with false positives due to the complexity of human motion.
  • Micro-Electro-Mechanical Systems (MEMS) sensors offer potential for wearable fall detection but require detailed kinematic data for algorithm development.

Purpose of the Study:

  • To present a novel and simple fall detection concept.
  • To provide detailed experimental data on human falls and activities of daily living (ADLs) for various sensor locations (hip, head, chest).
  • To develop a fall detection algorithm compatible with MEMS sensors and propose a new sensing concept.

Main Methods:

  • Collected detailed experimental data on human falls and ADLs, measuring acceleration components at multiple body locations.
  • Utilized a two-degree-of-freedom model to interpret experimental data.
  • Developed a fall detection algorithm based on MEMS switches and proposed a sensing concept using series-connected inertia sensors.

Main Results:

  • Presented comprehensive data on acceleration components during falls and ADLs at various body locations.
  • Established a fall detection algorithm leveraging MEMS switches.
  • Proposed a novel sensing concept where simultaneous triggering of multiple sensors indicates a true fall, preventing false positives during ADLs.

Conclusions:

  • The proposed fall detection concept, based on detailed kinematic data and a novel MEMS switch array, effectively distinguishes falls from ADLs.
  • This approach offers a promising solution for reliable and accurate fall detection systems.
  • The study highlights the importance of location-specific kinematic data for MEMS-based fall detection algorithm development.