Sparse Wearable Sonomyography Sensor-based Proprioceptive Proportional Control Across Multiple Gestures
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910358555328512 |
|---|---|
| author | Kamatham, Anne Tryphosa Sharma, Kavita Venkataraman, Srikumar Mukherjee, Biswarup |
| author_facet | Kamatham, Anne Tryphosa Sharma, Kavita Venkataraman, Srikumar Mukherjee, Biswarup |
| contents | Sonomyography (SMG) is a non-invasive technique that uses ultrasound imaging to detect the dynamic activity of muscles. Wearable SMG systems have recently gained popularity due to their potential as human-computer interfaces for their superior performance compared to conventional methods. This paper demonstrates real-time positional proportional control of multiple gestures using a multiplexed 8-channel wearable SMG system. The amplitude-mode ultrasound signals from the SMG system were utilized to detect muscle activity from the forearm of 8 healthy individuals. The derived signals were used to control the on-screen movement of the cursor. A target achievement task was performed to analyze the performance of our SMG-based human-machine interface. Our wearable SMG system provided accurate, stable, and intuitive control in real-time by achieving an average success rate greater than 80% with all gestures. Furthermore, the wearable SMG system's abilities to detect volitional movement and decode movement kinematic information from SMG trajectories using standard performance metrics were evaluated. Our results provide insights to validate SMG as an intuitive human-machine interface. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_05308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sparse Wearable Sonomyography Sensor-based Proprioceptive Proportional Control Across Multiple Gestures Kamatham, Anne Tryphosa Sharma, Kavita Venkataraman, Srikumar Mukherjee, Biswarup Human-Computer Interaction Robotics Signal Processing Sonomyography (SMG) is a non-invasive technique that uses ultrasound imaging to detect the dynamic activity of muscles. Wearable SMG systems have recently gained popularity due to their potential as human-computer interfaces for their superior performance compared to conventional methods. This paper demonstrates real-time positional proportional control of multiple gestures using a multiplexed 8-channel wearable SMG system. The amplitude-mode ultrasound signals from the SMG system were utilized to detect muscle activity from the forearm of 8 healthy individuals. The derived signals were used to control the on-screen movement of the cursor. A target achievement task was performed to analyze the performance of our SMG-based human-machine interface. Our wearable SMG system provided accurate, stable, and intuitive control in real-time by achieving an average success rate greater than 80% with all gestures. Furthermore, the wearable SMG system's abilities to detect volitional movement and decode movement kinematic information from SMG trajectories using standard performance metrics were evaluated. Our results provide insights to validate SMG as an intuitive human-machine interface. |
| title | Sparse Wearable Sonomyography Sensor-based Proprioceptive Proportional Control Across Multiple Gestures |
| topic | Human-Computer Interaction Robotics Signal Processing |
| url | https://arxiv.org/abs/2403.05308 |