Sensor-Placement-Agnostic Sonomyography: Toward Continuous High-Dimensional Control by Users with Tetraplegia
- URL: http://arxiv.org/abs/2607.26401v1
- Date: Wed, 29 Jul 2026 02:26:53 GMT
- Title: Sensor-Placement-Agnostic Sonomyography: Toward Continuous High-Dimensional Control by Users with Tetraplegia
- Abstract summary: Sonomyography (SMG) enables continuous device control via ultrasound-measured muscle deformation signals.<n>We present a real-time, sensor-placement-agnostic SMG control system based on sparse optical flow tracking.<n>We evaluate both 1- and 2-DOF systems' performance for a preliminary cohort of 3 cervical spinal cord injury survivors and 6 uninjured individuals.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sonomyography (SMG) enables continuous device control via ultrasound-measured muscle deformation signals, but existing SMG interfaces generally require substantial user- and sensor-location-specific training data and provide only one proportional signal or task-specific classification. We present a real-time, sensor-placement-agnostic SMG control system based on sparse optical flow tracking that enables continuous 1-DOF control after minimal calibration (3 pose definitions). We also present a preliminary expansion of this method that augments this algorithm with a short computer-aided calibration to enable 2-DOF control. We evaluate both 1- and 2-DOF systems' performance for a preliminary cohort of 3 cervical spinal cord injury survivors and 6 uninjured individuals across 6 sensor placements spanning the arm, neck, and upper torso. As assessed by a cursor trajectory tracking task, all participants achieved continuous 1-DOF control at all tested sensor locations (even those that relied on passive tissue motions), with all participants achieving <5.5% tracking error using at least one placement (and many <4% across many). All participants were also able to modulate 2D cursor position via the 2-DOF system, with varying levels of control authority, and several were able to complete a 2D drawing task, constituting the first (to our knowledge) demonstration of location-agnostic multi-DOF continuous SMG-based control. These results highlight the promise of SMG to enable rapidly calibratable, high-dimensional, sensor-placement-agnostic device control by users with tetraplegia, and also illuminate key challenges in both signal processing and practical system deployment. To enable further development by scientific and user communities, developed algorithms have been open-sourced as part of the OpenMyoControl project on SimTK (simtk.org/projects/openmyocontrol).
Related papers
- Freezing of Gait Prediction Under Spatial Occlusion: An IMU-Supervised Cross-Modal Distillation Approach [0.0]
Parkinson's disease is a progressive neurodegenerative disorder characterised by gradual deterioration of movement control.<n> automated freezing-of-gait (FOG) detection supports the objective assessment of gait-related motor impairment.<n>Two common approaches are used for FOG prediction: (i) analysing video recordings of the patient's movements and (ii) analysing data collected using inertial measurement unit (IMU) wearable sensors attached to the patient's lower limbs.<n>Video-based approaches may suffer detection errors during continuous turning-in-place tasks because the lower limbs undergo substantial geometric self-occlusion, degrading pose-estimation accuracy.
arXiv Detail & Related papers (2026-09-09T07:29:13Z) - Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration [50.18488308004802]
Sen-Cap is a 3D motion capture framework that integrates multi-modal data from LiDAR and camera.<n>Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion.<n>This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios.
arXiv Detail & Related papers (2026-08-03T14:16:18Z) - Real-Time sEMG-Based Telecontrol of an Assistive Robotic Arm Using a 1D Convolutional Neural Network [0.30786914102688595]
This work proposes a real-time sEMG-based interface for the teleoperation of an assistive robotic arm.<n>The system relies on four-channel sEMG acquisition, signal preprocessing, segmentation into sliding windows, and classification using a one-dimensional convolutional neural network (CNN)<n>The system exhibits stable real-time behavior, with an average latency of approximately 0.32 s consistent with the chosen windowing strategy.
arXiv Detail & Related papers (2026-07-14T15:34:28Z) - FLORO: A Multimodal Geospatial Foundation Model for Ecological Remote Sensing Across Sensors and Scales [65.4821703903285]
FLORO is a multimodal foundation model designed to learn transferable representations from a small but highly diverse remote sensing corpus.<n> FLORO is pretrained using masked autoencoding on a heterogeneous combination of Sentinel-1, Sentinel-2, SkySAT imagery, elevation, and UAV-derived data.<n>We evaluated FLORO on the PANGAEA benchmark under a frozen-encoder protocol across scene classification, segmentation, and regression tasks.
arXiv Detail & Related papers (2026-05-27T08:55:54Z) - Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control [51.92884966472683]
MSCoT is a multi-scale, coarse-to-fine model for test-time human motion synthesis and control.<n> MSCoT discretizes motion into a multi-scale hierarchical representation and predicts the entire token sequence at each temporal scale.
arXiv Detail & Related papers (2026-05-14T15:09:49Z) - EasyREG: Easy Depth-Based Markerless Registration and Tracking using Augmented Reality Device for Surgical Guidance [10.156036566483888]
We present a markerless framework that relies only on the depth sensor of AR devices.<n>The registration module integrates depth sensor error correction, a human-in-the-loop region filtering technique, and a robust global alignment.<n>The tracking module employs a fast and robust registration algorithm that uses the initial pose from the registration module to estimate the target pose in real-time.
arXiv Detail & Related papers (2025-04-13T09:48:33Z) - Automated Detection of Epileptic Spikes and Seizures Incorporating a Novel Spatial Clustering Prior [4.432163893362497]
We introduce a paradigm that first clusters MEG channels based on their sensor's spatial position.<n>Next, a novel convolutional input module is designed to integrate the spatial clustering and temporal changes of the signals.<n>Our method achieves an F1 score of 94.73% on a large real-world MEG dataset Sanbo-CMR collected from two centers, outperforming state-of-the-art approaches by 1.85%.
arXiv Detail & Related papers (2025-01-05T02:06:13Z) - Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors [6.369424231505372]
Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease.<n>We present FOGSense, a real-world deployable FOG detection system using only a single sensor.
arXiv Detail & Related papers (2024-11-18T17:43:43Z) - Agile gesture recognition for capacitive sensing devices: adapting
on-the-job [55.40855017016652]
We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller.
The controller generates real-time signals from each of the wearer five fingers.
We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
arXiv Detail & Related papers (2023-05-12T17:24:02Z) - Next-generation Surgical Navigation: Marker-less Multi-view 6DoF Pose Estimation of Surgical Instruments [64.59698930334012]
We present a multi-camera capture setup consisting of static and head-mounted cameras.<n>Second, we publish a multi-view RGB-D video dataset of ex-vivo spine surgeries, captured in a surgical wet lab and a real operating theatre.<n>Third, we evaluate three state-of-the-art single-view and multi-view methods for the task of 6DoF pose estimation of surgical instruments.
arXiv Detail & Related papers (2023-05-05T13:42:19Z) - Visual-tactile sensing for Real-time liquid Volume Estimation in
Grasping [58.50342759993186]
We propose a visuo-tactile model for realtime estimation of the liquid inside a deformable container.
We fuse two sensory modalities, i.e., the raw visual inputs from the RGB camera and the tactile cues from our specific tactile sensor.
The robotic system is well controlled and adjusted based on the estimation model in real time.
arXiv Detail & Related papers (2022-02-23T13:38:31Z) - Automatic Extrinsic Calibration Method for LiDAR and Camera Sensor
Setups [68.8204255655161]
We present a method to calibrate the parameters of any pair of sensors involving LiDARs, monocular or stereo cameras.
The proposed approach can handle devices with very different resolutions and poses, as usually found in vehicle setups.
arXiv Detail & Related papers (2021-01-12T12:02:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.