LayeredSense: Hierarchical Recognition of Complex Daily Activities Using Wearable Sensors

TL;DR

LayeredSense is a hierarchical framework that decomposes complex daily activities into simpler patterns using wearable sensor data, employing machine learning for real-time recognition with high accuracy.

Contribution

It introduces a novel layered approach combining pattern decomposition and machine learning for recognizing complex activities from wearable sensors.

Findings

High accuracy in recognizing simple and complex activities

Effective decomposition of activities into identifiable units

Scalable real-time activity monitoring

Abstract

Daily activity recognition has gained prominence due to its applications in context-aware computing. Current methods primarily rely on supervised learning for detecting simple, repetitive activities. This paper introduces LayeredSense, a novel framework designed to recognize complex activities by decomposing them into smaller, easily identifiable unit patterns. Utilizing a Myo armband for data collection, our system processes inertial measurement unit (IMU) data to identify basic actions like walking, running, and jumping. These actions are then aggregated to infer more intricate activities such as playing sports or working. LayeredSense employs Gaussian Mixture Models for new pattern detection and machine learning algorithms, including Random Forests, for real-time activity recognition. Our system demonstrates high accuracy in identifying both unit patterns and complex activities, providing a scalable solution for comprehensive daily activity monitoring