Yasha Iravantchi

All Publications

• HandSAW: Wearable Hand-based Event Recognition via On-Body Surface Acoustic Waves PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT Yaxuan, K., Iravantchi, Y., Zhu, Y., Park, H., Sample, A. P. 2025; 9 (1)

  View details for DOI 10.1145/3712276

  View details for Web of Science ID 001440789600001

• MagDesk: Interactive Tabletop Workspace Based on Passive Magnetic Tracking PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT Huang, K., Iravantchi, Y., Chen, D., Sample, A. 2024; 8 (4)

  View details for DOI 10.1145/3699756

  View details for Web of Science ID 001412078800001

• T4Train: Rapid Prototyping of ML-Driven Interactive Applications Iravantchi, Y., Sample, A. P., ASSOC COMPUTING MACHINERY ASSOC COMPUTING MACHINERY. 2024

  View details for DOI 10.1145/3613905.3636269

  View details for Web of Science ID 001227587700004

• Privacy-Preserving Automatic Collection of Acoustic Voiding Events Arjona, L., Iravantchi, Y., Sample, A., Alvarez, M. L., Bahillo, A., Canalon, E., IEEE IEEE. 2023: 1-4

  Abstract

  Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention. This platform operates outside of human-audible frequencies, providing privacy-preserving, automatic uroflowmetries that can be conducted at home as part of daily routines. After evaluating several machine learning algorithms, we found that the Multi-layer Perceptron classifier performed exceptionally well, with a classification accuracy of 97.8% and a low false negative rate of 1.2%. Furthermore, even on lightweight SVM models, performance remains robust. Our results also showed that the voiding flow envelope, helpful for diagnosing underlying pathologies, remains intact even when using only inaudible frequencies.Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous embedded privacy-preserving audio capture devices with Edge AI capabilities.

  View details for DOI 10.1109/EMBC40787.2023.10341012

  View details for Web of Science ID 001133788304126

  View details for PubMedID 38082651

• SAWSense: Using Surface AcousticWaves for Surface-bound Event Recognition Iravantchi, Y., Zhao, Y., Kin, K., Sample, A. P., ACM ASSOC COMPUTING MACHINERY. 2023

  View details for DOI 10.1145/3544548.3580991

  View details for Web of Science ID 001037809505028

• METRO: Magnetic Road Markings for All-weather, Smart Roads Wang, J., Wang, S., Iravantchi, Y., Wang, M., Sample, A., Shin, K. G., Wang, X., Zhou, C., Chen, D., Assoc Computing Machinery ASSOC COMPUTING MACHINERY. 2023: 280-293

  View details for DOI 10.1145/3625687.3625809

  View details for Web of Science ID 001249418600021

• BrushLens: Hardware Interaction Proxies for Accessible Touchscreen Interface Actuation Iravantchi, Y., Krolikowski, T., Liang, C., Geng, R., Sample, A., Guo, A., ACM ASSOC COMPUTING MACHINERY. 2023

  View details for DOI 10.1145/3586183.3606730

  View details for Web of Science ID 001360355700036