Automatic sensor-based detection and classification of climbing activities

TL;DR

This paper introduces a method for automatic detection and classification of climbing activities using IMUs on different body parts, employing a learning phase and statistical models for real-time activity recognition.

Contribution

It presents a novel sensor-based approach combining IMU data and statistical modeling to automatically classify climbing activities.

Findings

Effective detection of climbing activities using IMUs

High classification accuracy demonstrated

Real-time activity recognition capability

Abstract

This article presents a method to automatically detect and classify climbing activities using inertial measurement units (IMUs) attached to the wrists, feet and pelvis of the climber. The IMUs record limb acceleration and angular velocity. Detection requires a learning phase with manual annotation to construct the statistical models used in the cusum algorithm. Full-body activity is then classified based on the detection of each IMU.