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Design and Analysis for Fall Detection System Simplification

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Hierarchical Span-Based Conditional Random Fields for Labeling and Segmenting Events in Wearable Sensor Data Streams.

Roy J Adams¹, Nazir Saleheen², Edison Thomaz³

¹University of Massachusetts Amherst, 140 Governors Drv, Amherst, MA 01003, RJADAMS@CS.UMASS.EDU.

JMLR Workshop and Conference Proceedings

|January 17, 2017

Summary

This summary is machine-generated.

This study introduces a new model for analyzing wearable sensor data to detect and segment activities like smoking and eating. The advanced model significantly improves activity recognition accuracy compared to previous methods.

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Area of Science:

Computational methods
Machine learning
Mobile health (mHealth)

Background:

Wearable sensors generate continuous data for health and behavior insights.
Analyzing this data requires robust methods for event detection and activity segmentation.
Existing methods may not fully capture complex activity structures.

Purpose of the Study:

To present a novel hierarchical span-based conditional random field model.
To jointly detect discrete events and segment them into high-level activity sessions from sensor data.
To improve the accuracy of activity recognition in mobile health applications.

Main Methods:

Developed a hierarchical span-based conditional random field model.
Incorporated higher-order cardinality and inter-event duration factors.
Utilized exact MAP inference in quadratic time via dynamic programming for learning within a structured support vector machine framework.
Applied the model to smoking and eating detection using four real-world datasets.

Main Results:

The proposed model demonstrated statistically significant improvements in segmentation performance.
Outperformed hierarchical pairwise conditional random field models.
Effectively detected and segmented discrete events into meaningful activity sessions.

Conclusions:

The hierarchical span-based conditional random field model offers superior performance for activity recognition in mHealth.
The model's ability to capture domain-specific structures enhances event detection and segmentation accuracy.
This approach holds significant potential for advancing health and behavior analysis using wearable sensor data.