PrimSeq: a deep learning-based pipeline to quantitate rehabilitation training
This work addresses the lack of practical tools for quantifying rehabilitation training in stroke patients, which is crucial for optimizing recovery, though it appears incremental as it builds on existing sensor and deep learning techniques.
The authors tackled the problem of measuring functional motions in stroke rehabilitation by developing PrimSeq, a pipeline that uses wearable sensors and deep learning to classify and count motions, achieving higher accuracy than existing methods and reducing time and labor costs compared to human experts.
Stroke rehabilitation seeks to increase neuroplasticity through the repeated practice of functional motions, but may have minimal impact on recovery because of insufficient repetitions. The optimal training content and quantity are currently unknown because no practical tools exist to measure them. Here, we present PrimSeq, a pipeline to classify and count functional motions trained in stroke rehabilitation. Our approach integrates wearable sensors to capture upper-body motion, a deep learning model to predict motion sequences, and an algorithm to tally motions. The trained model accurately decomposes rehabilitation activities into component functional motions, outperforming competitive machine learning methods. PrimSeq furthermore quantifies these motions at a fraction of the time and labor costs of human experts. We demonstrate the capabilities of PrimSeq in previously unseen stroke patients with a range of upper extremity motor impairment. We expect that these advances will support the rigorous measurement required for quantitative dosing trials in stroke rehabilitation.