High Performance Wearable Ultrasound as a Human-Machine Interface for wrist and hand kinematic tracking

• Published in: IEEE transactions on biomedical engineering 2024-02, Vol.71 (2), p.1-10
• Main Authors: Sgambato, Bruno Grandi, Hasbani, Milia H, Barsakcioglu, Deren Y, Ibanez, Jaime, Jakob, Anette, Fournelle, Marc, Tang, Meng-Xing, Farina, Dario
• Format: Article
• Language: English
• Subjects: A-mode; Biomechanical Phenomena; Deformation wear; Degrees of freedom; Electromyography; Electromyography - methods; Hand; Hand (anatomy); Hand - diagnostic imaging; Human-Machine Interfaces; Humans; Industrial applications; Interfaces; Kinematics; Man-machine interfaces; Motion capture; Movement; Muscles; Regression models; Robust control; Surface Electromyography; Task analysis; Thumb; Transducers; Ultrasonic imaging; Ultrasound; Wearable Electronic Devices; Wearable technology; Wrist; Wrist - diagnostic imaging; Wrist Joint
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2023.3307952

Objective: Non-invasive human machine interfaces (HMIs) have high potential in medical, entertainment, and industrial applications. Traditionally, surface electromyography (sEMG) has been used to track muscular activity and infer motor intention. Ultrasound (US) has received increasing attention as an alternative to sEMG-based HMIs. Here, we developed a portable US armband system with 24 channels and a multiple receiver approach, and compared it with existing sEMG- and US-based HMIs on movement intention decoding. Methods: US and motion capture data was recorded while participants performed wrist and hand movements of four degrees of freedom (DoFs) and their combinations. A linear regression model was used to offline predict hand kinematics from the US (or sEMG, for comparison) features. The method was further validated in real-time for a 3-DoF target reaching task. Results: In the offline analysis, the wearable US system achieved an average R^2 of 0.94 in the prediction of four DoFs of the wrist and hand while sEMG reached a performance of R^2 = 0.06. In online control, the participants achieved an average 93% completion rate of the targets. Conclusion: When tailored for HMIs, the proposed US A-mode system and processing pipeline can successfully regress hand kinematics both in offline and online settings with performances comparable or superior to previously published interfaces. Significance: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The wearable US system allowed for robust proportional and simultaneous control over multiple DoFs in both offline and online settings.