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Applying Multivariate Segmentation Methods to Human Activity Recognition From Wearable Sensors' Data.

Kenan Li¹, Rima Habre¹, Huiyu Deng¹

¹Department of Preventive Medicine, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States.

JMIR Mhealth and Uhealth

|February 8, 2019

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This summary is machine-generated.

Greedy Gaussian segmentation (GGS) offers improved human activity recognition (HAR) by adapting to variable activity durations. This method provides more stable predictions and higher accuracy than fixed-size windows, aiding personalized medicine and research.

Area of Science:

Biomedical engineering
Machine learning
Wearable technology

Background:

Accurate physical activity quantification is crucial for personalized medicine and epidemiological studies, such as managing asthma exacerbations.
Existing human activity recognition (HAR) algorithms often use fixed-size windows, which struggle with real-world variable activity durations.
Short windows yield noisy data, while long windows miss brief, intense activities.

Purpose of the Study:

To develop an HAR framework that accommodates variable activity bout durations.
To detect change points in multivariate time series data to define activity bouts.
To predict physical activity within these dynamically defined, homogeneous windows.

Main Methods:

Applied fixed-width sliding windows and greedy Gaussian segmentation (GGS) to sensor data (accelerometer, gyroscope).

Keywords:

machine learning physical activity smartphone statistical data analysis wearable devices

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Utilized Xgboost models for activity prediction within segmented windows.

Validated methods on the HARuS (adults, smartphone) and BREATHE (children, smartwatch) datasets, including simulated variable activity durations.

Main Results:

GGS produced the least noisy predictions in both datasets.
GGS achieved high accuracy rates: 91.06% in HARuS and 79.4% in BREATHE.
While fixed-size windows showed slightly higher peak accuracy in HARuS, GGS demonstrated superior performance with variable bout durations.

Conclusions:

GGS multivariate segmentation effectively creates adaptive windows for more stable and accurate HAR compared to fixed-size methods.
The approach shows promise for real-world applications, though accuracy varies with data collection context (e.g., device, population).
GGS, initially offline, is adaptable for real-time HAR prediction using streaming data.