Design of all-accelerometer inertial measurement unit for tremor sensing in hand-held microsurgical instrument

Ang, Wei Tech; Khosla, Pradeep K.; Riviere, Cameron N.

doi:10.1109/robot.2003.1241852

Cited by 22 publications

(2 citation statements)

References 10 publications

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“…In medical XR applications such as clinical training and patient rehabilitation, the orientation (yaw, pitch, roll) and position of surgical instruments or a patient's limbs are measured and used as the input. To achieve this, an electromagnetic sensor known as the inertial measurement unit (IMU) [G] 53 can be embedded in hand-held instruments and HMDs thanks to its small size and weight 39,54 . However, the metal objects in the operating room may cause artifacts such as distortion errors on electromagnetic sensors like IMUs 55 ; optical sensors such as cameras are commonly used instead 56,57 .…”

Section: Hardware Components Of Xrmentioning

confidence: 99%

Extended reality for biomedicine

Yuan

Hassan

et al. 2023

Nat Rev Methods Primers

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Extended reality (XR) refers to an umbrella of methods that allows users to be immersed in a three-dimensional (3D) or a 4D (spatial + temporal) virtual environment to different extents, including virtual reality (VR), augmented reality (AR), and mixed reality (MR). While VR allows a user to be fully immersed in a virtual environment, AR and MR overlay virtual objects over the real physical world. The immersion and interaction of XR provide unparalleled opportunities to extend our world beyond conventional lifestyles. While XR has extensive applications in fields such as entertainment and education, its numerous applications in biomedicine create transformative opportunities in both fundamental research and healthcare. This Primer outlines XR technology from instrumentation to software computation methods, delineating the biomedical applications that have been advanced by state-of-the-art techniques. We further describe the technical advances overcoming current limitations in XR and its applications, providing an entry point for professionals and trainees to thrive in this emerging field.

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Section: Hardware Components Of Xrmentioning

confidence: 99%

Extended reality for biomedicine

Yuan

Hassan

et al. 2023

Nat Rev Methods Primers

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“…While prior research has applied BASN application signal processing and classification to tremor assessment [Findley and Koller 1994;Spyers-Ashby et al 1998;Tech Ang et al 2003] and activity recognition [Parkka et al 2006], these techniques and approaches are not created for wearable, embedded, on-node processing environments. Research addressing scalable compression algorithms for data reduction [Hwang et al 2003;Tan and Zakhor 1999] are not considered viable within the context of modern resource-constrained BASN hardware.…”

Section: Introductionmentioning

confidence: 99%

Application-Focused Energy-Fidelity Scalability for Wireless Motion-Based Health Assessment

Hanson

Powell

Barth

et al. 2012

ACM Trans. Embed. Comput. Syst.

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Energy-fidelity trade-offs are central to the performance of many technologies, but they are essential in wireless body area sensor networks (BASNs) due to severe energy and processing constraints and the critical nature of certain healthcare applications. On-node signal processing and compression techniques can save energy by greatly reducing the amount of data transmitted over the wireless channel, but lossy techniques, capable of high compression ratios, can incur a reduction in application fidelity. In order to maximize system performance, these trade-offs must be considered at runtime due to the dynamic nature of BASN applications, including sensed data, operating environments, user actuation, etc. BASNs therefore require energy-fidelity scalability, so automated and user-initiated trade-offs can be made dynamically. This article presents a data rate scalability framework within a motion-based health application context which demonstrates the design of efficient and efficacious wireless health systems. ACM Reference Format:Hanson, M. A., Powell Jr., H. C., Barth, A. T., and Lach, J. 2012. Application-focused energy-fidelity scalability for wireless motion-based health assessment.

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