Portable low-cost 32-channel ultrasound research system

Fournelle, Marc; Grum, Tobias; Speicher, Daniel; Weber, Steffen; Tretbar, Steffen

doi:10.1109/ius46767.2020.9251327

“…This system was in charge of driving the US transmission and of recording the signals. The system can send customizable sequences of transmissions for each channel and records the US echoes at 8-bit resolution for up to 110 𝜇𝑠 at a 40-MHz sampling rate [17]. The system also allows a multiple receiver interface, which means for each transmission event, the received signals by all the transducers are stored (Fig.…”

Section: B Equipment 1) Ultrasound A-mode Armbandmentioning

confidence: 99%

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

Sgambato¹,

Hasbani²,

Barsakcioglu³

et al. 2023

Preprint

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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. As an alternative HMI approach, ultrasound (US) has been receiving increasing attention as an alternative to EMG. Here, we developed a novel 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 in up to four degrees of freedom (DoFs). A linear regression model was then used to predict the hand kinematics from the US (or sEMG, for comparison) features. The model was also implemented in real-time for a 3-DoF pointer control with minimal training data. Results: In offline analysis, the wearable US system achieved an average of 0.94 in the prediction of four DoFs of the wrist and hand. sEMG reached a performance of = 0.60. Conclusion: The newly proposed A-mode US bracelet setup and processing pipeline can regress hand kinematics for up to four DoFs with higher accuracies than other previously published interfaces based on sEMG or US data. The system allowed real-time control with minimal training data Significance: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The proposed US system here provided robust proportional and simultaneous control over multiple DoFs.

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“…This system was in charge of driving the US transmission and of recording the signals. The system can send customizable sequences of transmissions for each channel and records the US echoes at 8-bit resolution for up to 110 𝜇𝑠 at a 40-MHz sampling rate [17]. The system also allows a multiple receiver interface, which means for each transmission event, the received signals by all the transducers are stored (Fig.…”

Section: B Equipment 1) Ultrasound A-mode Armbandmentioning

confidence: 99%

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

Sgambato¹,

Hasbani²,

Barsakcioglu³

et al. 2023

Preprint

2

0

View full text Add to dashboard Cite

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. As an alternative HMI approach, ultrasound (US) has been receiving increasing attention as an alternative to EMG. Here, we developed a novel 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 in up to four degrees of freedom (DoFs). A linear regression model was then used to predict the hand kinematics from the US (or sEMG, for comparison) features. The model was also implemented in real-time for a 3-DoF pointer control with minimal training data. Results: In offline analysis, the wearable US system achieved an average of 0.94 in the prediction of four DoFs of the wrist and hand. sEMG reached a performance of = 0.60. Conclusion: The newly proposed A-mode US bracelet setup and processing pipeline can regress hand kinematics for up to four DoFs with higher accuracies than other previously published interfaces based on sEMG or US data. The system allowed real-time control with minimal training data Significance: Wearable US technology may provide a new generation of HMIs that use muscular deformation to estimate limb movements. The proposed US system here provided robust proportional and simultaneous control over multiple DoFs.

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Advancing Towards a Wearable Ultrasound Sensor for Real-Time Bladder Volume Monitoring to Reduce Catheterization in Surgical Settings

Imenabadi,

Hasanpour Avanji,

Bhatia

et al. 2024

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Portable low-cost 32-channel ultrasound research system

Cited by 6 publications

References 3 publications

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

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

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

Advancing Towards a Wearable Ultrasound Sensor for Real-Time Bladder Volume Monitoring to Reduce Catheterization in Surgical Settings

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