Surface Electromyography (sEMG) Based Predictive Motion Management for Radiation Therapy
Abstract
Purpose
Current motion management systems are reactive: they detect patient displacement but require 200-400 ms to respond. This limitation prevents anticipatory intervention during breath-holds, forcing patients to their physiological limits before the system can respond, and fundamentally prevents ultra-fast delivery techniques like FLASH-RT where total treatment time is shorter than system response latency. We investigated whether non-invasive surface electromyography (sEMG) can overcome this limitation by predicting movement 200-500 ms before physical displacement, exploiting the electromechanical delay between muscle depolarization and force generation.
Methods
We developed a machine learning framework to predict motion onset using two publicly available sEMG datasets: voluntary upper-limb gestures (10 subjects) and involuntary respiratory cycles (8 subjects). Ground truth was obtained from kinematic sensors and spirometry. We extracted signal envelopes and computed features at lead times from 50 to 1000 ms before motion onset. Linear Discriminant Analysis classified whether movement would occur within each prediction window. Performance was evaluated using leave-one-subject-out cross-validation and quantified by area under the Receiver Operating Characteristic curve (AUC). Computational latency was measured to assess real-time feasibility.
Results
sEMG successfully predicted motion before physical onset; For voluntary upper-limb movement, we achieved AUC of 0.86 ± 0.03 at 50 ms lead time, maintaining robust performance (AUC > 0.77) up to 500 ms. For respiratory motion, optimal prediction occurred at 300 ms lead time (AUC = 0.78 ± 0.18) and robust performance (AUC> 0.70) was maintained up to 800 ms. Total inference latency remained under 6 ms, enabling real-time beam control. These prediction windows provide sufficient temporal margin for system response, enabling the beam to stop before motion begins.
Conclusion
This proof-of-concept demonstrates that sEMG-based prediction can anticipate motion with sufficient lead time to overcome hardware response latencies. Future work will validate this approach using radiation therapy-specific training data and hardware-integrated closed-loop systems.