MM-Fit: Multimodal Deep Learning for Automatic Exercise Logging across Sensing Devices

Fitness tracking devices have risen in popularity in recent years, but limitations in terms of their accuracy and failure to track many common exercises presents a need for improved fitness tracking solutions. This work proposes a multimodal deep learning approach to leverage multiple data sources for robust and accurate activity segmentation, exercise recognition and repetition counting. For this, we introduce the MM-Fit dataset; a substantial collection of inertial sensor data from smartphones, smartwatches and earbuds worn by participants while performing full-body workouts, and time-synchronised multi-viewpoint RGB-D video, with 2D and 3D pose estimates. We establish a strong baseline for activity segmentation and exercise recognition on the MM-Fit dataset, and demonstrate the effectiveness of our CNN-based architecture at extracting modality-specific spatial temporal features from inertial sensor and skeleton sequence data. We compare the performance of unimodal and multimodal models for activity recognition across a number of sensing devices and modalities. Furthermore, we demonstrate the effectiveness of multimodal deep learning at learning cross-modal representations for activity recognition, which achieves 96% accuracy across all sensing modalities on unseen subjects in the MM-Fit dataset; 94% using data from the smartwatch only; 85% from the smartphone only; and 82% on data from the earbud device. We strengthen single-device performance by using the zeroing-out training strategy, which phases out the other sensing modalities. Finally, we implement and evaluate a strong repetition counting baseline on our MM-Fit dataset. Collectively, these tasks contribute to recognising, segmenting and timing exercise and non-exercise activities for automatic exercise logging.