ArticleSensors (Basel, Switzerland)2019
Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- Fall detection and pre-impact prediction technologies in older adults: a scoping review of translational maturity and public health integration.Frontiers in public health · 2026Article
- Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms.Biomimetics (Basel, Switzerland) · 2025Article
- A Transferable Lidar-Based Method to Conduct Contactless Assessments of Gait Parameters in Diverse Home-like Environments.Sensors (Basel, Switzerland) · 2024Article
- Fall Recognition Based on an IMU Wearable Device and Fall Verification through a Smart Speaker and the IoT.Sensors (Basel, Switzerland) · 2023Article
- Gait Alteration in Individual with Limb Loss: The Role of Inertial Sensors.Sensors (Basel, Switzerland) · 2023Review
- World guidelines for falls prevention and management for older adults: a global initiative.Age and ageing · 2022Article
- Pathway of Trends and Technologies in Fall Detection: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Contactless Gait Assessment in Home-like Environments.Sensors (Basel, Switzerland) · 2021Article
- A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls.Journal of neuroengineering and rehabilitation · 2021Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor "AIDE-MOI" was developed. "AIDE-MOI" senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as "fall" or "non-fall". The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.
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