Automatic sensor-based detection and classification of climbing activities
This addresses the need for automated monitoring of climbing activities, but it is incremental as it builds on existing sensor-based detection techniques.
The paper tackles the problem of automatically detecting and classifying climbing activities by using IMUs attached to a climber's body, resulting in a method that requires manual annotation for training statistical models and then classifies activities based on sensor data.
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.