Pre-Clinical Latency Characterization of VRxBioRelax: A Real-Time EMG Biofeedback System for Muscle Relaxation in Virtual Reality

TL;DR

VRxBioRelax, a closed-loop VR biofeedback system that streams sEMG data from Delsys Trigno Avanti sensors via MQTT to a Unity scene, is introduced and mean latency is significantly lower than both the 30 ms VR comfort limit and the 50 ms clinical benchmark.

Abstract

Chronic tension in the upper trapezius (UT), often caused by poor ergonomics, prolonged posture, or psychological stress, contributes to musculoskeletal discomfort, headaches, and impaired interoceptive awareness. Although surface electromyography (sEMG) biofeedback can promote UT relaxation, traditional systems using conventional displays often fail to sustain engagement. Virtual reality (VR) offers a more immersive alternative, provided that latency remains below perceptual thresholds. We introduce VRxBioRelax, a closed-loop VR biofeedback system that streams sEMG data from Delsys Trigno Avanti sensors via MQTT to a Unity scene. Muscle activation drives a dynamic dawn-to-dusk landscape synchronized with a progressive muscle relaxation protocol. To validate system responsiveness, 87,716 EMG samples from the NinaPro DB2 dataset were replayed at $\sim$75 Hz. Timestamps at four key stages-acquisition, Root Mean Square (RMS) processing, network receipt, and rendering-revealed mean latencies of 0.50 ms (processing), 5.62 ms (network), and 19.22 ms (rendering), yielding an average end-to-end delay of 25.34 ms. Notably, 99.3% of frames arrived within 50 ms. One-sided t-tests confirmed mean latency was significantly lower than both the 30 ms VR comfort limit ($t_{87\,715}=-25.2$, $p=5.9{\times}10^{-140}$) and the 50 ms clinical benchmark ($t_{87\,715}=-133.3$, $p<10^{-300}$). These findings support VRxBioRelax for use in remote interoceptive training, stress reduction, and telepresence-enabled rehabilitation.

Figures (5)

• Figure 1: Illustration of the VRxBioRelax biofeedback mechanism: (a) EMG signal processing in real-time; (b) corresponding modulation of the immersive VR environment.
• Figure 2: Raw electromyographic (EMG) signals from the upper trapezius are captured using Delsys Trigno® Avanti sensors and streamed to a custom Python interface that computes real-time root mean square (RMS) amplitude. The processed data are transmitted via MQTT protocol to a Unity-based virtual reality (VR) environment. This environment dynamically maps EMG activity to changes in visual elements and is rendered through a VR headset, enabling closed-loop, immersive biofeedback for muscle tension regulation.
• Figure 3: Empirical cumulative distribution of end-to-end latency ($n=87{,}716$ packets). Shaded bands mark evidence-based limits for closed-loop human–machine interaction: latencies below $50\,\mathrm{ms}$ are virtually imperceptible for sensorimotor control, whereas delays up to $100\,\mathrm{ms}$ remain acceptable but can already attenuate motor accuracy Hadjiosif2025. The navy band shows the bootstrap $95\%$ confidence interval of the sample median (20.45–20.55 ms) and the dotted line indicates the 95th percentile (32 ms). Consequently, $\sim99\%$ of packets fall inside the “ideal” $<50$ ms window, outperforming the $\approx90$ ms loop delay Kantan2022.
• Figure 4: Latency distributions across the three pipeline stages: Processing -- box collapsed at 0.50 ms, showing constant compute time; Network -- median 5.4 ms, IQR (3.0--8.0 ms), full range $-1.7$--15.5 ms; Rendering -- median 15.0 ms, IQR (10.0--20.0 ms), full range 0--35.0 ms. Whiskers span the minimum and maximum observed values.
• Figure 5: Histogram of end-to-end latency (1 ms bins; axis truncated at 45 ms, covering 99.7 % of packets). The dashed red line marks the 30 ms VR-comfort threshold. The sample mean (25.3 ms) lies well to the left and is significantly lower than both the 30 ms and the clinical 50 ms targets (cf. Table \ref{['tab:latency_compact']}, one-sided $t$-tests).