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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Machine Learning Estimation of COVID-19 Social Distance using Smartphone Sensor Data.

Oleksandr Semenov, Emmanuel Agu, Kaveh Pahlavan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Summary

    Machine learning methods using smartphone sensors can detect close proximity, crucial for preventing airborne disease spread like COVID-19. This technology offers a novel approach to public health by identifying individuals within 6 feet.

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    Area of Science:

    • Computer Science
    • Public Health
    • Sensor Technology

    Background:

    • Airborne infectious diseases, including COVID-19, spread through close contact between individuals.
    • Existing methods for proximity detection are limited, necessitating technological solutions.
    • Smartphone sensors offer a potential platform for real-time proximity monitoring.

    Purpose of the Study:

    • To systematically investigate Machine Learning (ML) methods for detecting proximity using smartphone sensors.
    • To classify proximity (less than 6 feet) and estimate distance.
    • To identify the most effective ML algorithms and sensor data combinations.

    Main Methods:

    • Extracted 20 statistical features from smartphone Bluetooth, accelerometer, and gyroscope sensor data.
    • Applied ML classification and regression algorithms to the extracted features.
    • Investigated the impact of elliptical filtering on sensor signal data.

    Main Results:

    • Elliptical filtering improved ML regression performance for accelerometer and gyroscope data.
    • Key predictive features included accelerometer z-axis mean/fourth momentum, gyroscope z-axis mean/y-axis mean, and Bluetooth advertiser time/mean RSSI.
    • Ensemble methods and regression trees performed best using combined sensor data.
    • Proximity (< 6ft) was classified with 100% accuracy using accelerometer data and 62%-97% accuracy using Bluetooth data.

    Conclusions:

    • ML algorithms, particularly ensemble and regression trees, effectively detect proximity using smartphone sensor data.
    • Accelerometer data provides highly accurate proximity classification.
    • This technology-assisted approach can aid in mitigating the spread of airborne infectious diseases.