CHARM: A Hierarchical Deep Learning Model for Classification of Complex Human Activities Using Motion Sensors

TL;DR

This paper introduces CHARM, a hierarchical deep learning model that accurately classifies complex human activities from motion sensors, automatically learning low-level activities without explicit labels, enhancing human-machine interaction.

Contribution

The novel CHARM model advances HAR by recognizing high-level activities and automatically learning low-level activities without explicit labels, outperforming existing methods.

Findings

Outperforms state-of-the-art in accuracy and F1 scores.

Automatically learns low-level activities without explicit labels.

Enables semi-supervised learning for complex HAR tasks.

Abstract

In this paper, we report a hierarchical deep learning model for classification of complex human activities using motion sensors. In contrast to traditional Human Activity Recognition (HAR) models used for event-based activity recognition, such as step counting, fall detection, and gesture identification, this new deep learning model, which we refer to as CHARM (Complex Human Activity Recognition Model), is aimed for recognition of high-level human activities that are composed of multiple different low-level activities in a non-deterministic sequence, such as meal preparation, house chores, and daily routines. CHARM not only quantitatively outperforms state-of-the-art supervised learning approaches for high-level activity recognition in terms of average accuracy and F1 scores, but also automatically learns to recognize low-level activities, such as manipulation gestures and locomotion modes, without any explicit labels for such activities. This opens new avenues for Human-Machine Interaction (HMI) modalities using wearable sensors, where the user can choose to associate an automated task with a high-level activity, such as controlling home automation (e.g., robotic vacuum cleaners, lights, and thermostats) or presenting contextually relevant information at the right time (e.g., reminders, status updates, and weather/news reports). In addition, the ability to learn low-level user activities when trained using only high-level activity labels may pave the way to semi-supervised learning of HAR tasks that are inherently difficult to label.