Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves

Tashakori, Arvin; Jiang, Zenan; Servati, Amir; Soltanian, Saeid; Narayana, Harishkumar; Le, Katherine; Nakayama, Caroline; Yang, Chieh-ling; Wang, Z. Jane; Eng, Janice J.; Servati, Peyman

doi:10.1038/s42256-023-00780-9

Cited by 11 publications

(2 citation statements)

References 49 publications

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“…Through multi-stage machine learning, the system achieves low joint-angle estimation errors of 1.21 • and 1.45 • for intra-and inter-participant validation, matching costly motion-capture cameras' accuracy. A data augmentation technique enhances robustness to noise, enabling accurate tracking during object interactions and diverse applications, including typing on a simulated keyboard, recognizing dynamic and static gestures from American Sign Language, and object identification [46].…”

Section: Electro-mechanical Wearable Devicesmentioning

confidence: 99%

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Olyanasab,

Annabestani

2024

JPM

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This review investigates the convergence of artificial intelligence (AI) and personalized health monitoring through wearable devices, classifying them into three distinct categories: bio-electrical, bio-impedance and electro-chemical, and electro-mechanical. Wearable devices have emerged as promising tools for personalized health monitoring, utilizing machine learning to distill meaningful insights from the expansive datasets they capture. Within the bio-electrical category, these devices employ biosignal data, such as electrocardiograms (ECGs), electromyograms (EMGs), electroencephalograms (EEGs), etc., to monitor and assess health. The bio-impedance and electro-chemical category focuses on devices measuring physiological signals, including glucose levels and electrolytes, offering a holistic understanding of the wearer’s physiological state. Lastly, the electro-mechanical category encompasses devices designed to capture motion and physical activity data, providing valuable insights into an individual’s physical activity and behavior. This review critically evaluates the integration of machine learning algorithms within these wearable devices, illuminating their potential to revolutionize healthcare. Emphasizing early detection, timely intervention, and the provision of personalized lifestyle recommendations, the paper outlines how the amalgamation of advanced machine learning techniques with wearable devices can pave the way for more effective and individualized healthcare solutions. The exploration of this intersection promises a paradigm shift, heralding a new era in healthcare innovation and personalized well-being.

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Section: Electro-mechanical Wearable Devicesmentioning

confidence: 99%

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Olyanasab,

Annabestani

2024

JPM

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“…Stretchable electronics is an emerging field revolutionizing the ways that robots interact with the environment and humans monitor their health. − One of the key problems along this direction is to accurately measure the mechanical properties (such as modulus and strength) of soft and thin materials in the devicesthis facilitates the design and fabrication of flexible devices, as well as their integration on biotissues , and soft robots , because a mechanical mismatch often leads to an unstable interface and thus limited lifetime. − In bioelectronics, a material laminated on the skin or implanted in the body should have a stiffness close to that of the biotissue for conformal and long-term integration. ,, Metals and semiconductors, on the other hand, are often made porous or mesh-like to achieve high stretchability. − However, determination of the mechanical properties of such materials remains challenging.…”

mentioning

confidence: 99%

Mechanical Tester Driven by Surface Tension

Wang,

Shao,

Song

et al. 2024

Nano Lett.

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The measurement of in-plane mechanical properties, such as Young's modulus and strength, of thin and stretchable materials has long been a challenge. Existing measurements, including wrinkle instability and nano indentation, are either indirect or destructive, and are inapplicable to meshes or porous materials, while the conventional tension test fails to measure the mechanical properties of nanoscale films. Here, we report a technique to test thin and stretchable films by loading a thin film afloat via differential surface tension and recording its deformation. We have demonstrated the method by measuring the Young's moduli of homogeneous films of soft materials including polydimethylsiloxane and Ecoflex and verified the results with known values. We further measured the strain distributions of meshes, both isotropic and anisotropic, which were otherwise nearly impossible to measure. The method proposed herein is expected to be generally applicable to many material systems that are thin, stretchable, and water-insoluble.

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Innovations in Tactile Sensing: Microstructural Designs for Superior Flexible Sensor Performance

Wu,

Li,

Bao

et al. 2024

Adv Funct Materials

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Tactile sensors have garnered considerable interest for their capacity to detect and quantify tactile information. The incorporation of microstructural designs into flexible tactile sensors has emerged as a potent strategy to augment their sensitivity to pressure variations, thereby enhancing their linearity, response spectrum, and mechanical robustness. This review underscores the imperative for progress in microstructured flexible tactile sensors. Subsequently, the discourse transitions to the prevalent materials employed in the fabrication of sensor electrodes, encapsulation layers, and active sensing mediums, elucidating their merits and limitations. In‐depth discussions are devoted to tactile sensors adorned with microstructures, including but not limited to, micropyramids, microhemispheres, micropillars, microporous configurations, microcracks, topological interconnections, multilevel constructs, random roughness, biomimetic microstructures inspired by flora and fauna, accompanied by exemplar studies from each category. Moreover, the utility of flexible tactile sensors within the realm of intelligent environments is explicated, highlighting their application in the monitoring of physiological signals, the detection of sliding motions, and the discernment of surface textures. The review culminates in a critical examination of the paramount challenges and predicaments that must be surmounted to further the development and enhance the functional performance of tactile sensors, paving the way for their integration into advanced sensory systems.

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Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves

Cited by 11 publications

References 49 publications

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review

Mechanical Tester Driven by Surface Tension

Innovations in Tactile Sensing: Microstructural Designs for Superior Flexible Sensor Performance

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