Tracking of Scalpel Motions With an Inertial Measurement Unit System

Kabuye, Ernest; Hellebrekers, Tess; Bobo, Justin; Keeys, Nolen; Majidi, Carmel; Cagan, Jonathan; Duc, Philip Le

doi:10.1109/jsen.2022.3145312

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…Soft and flexible IMU sensors might be another solution [ 49 , 50 , 51 ]. Placing some soft and flexible IMU sensors on body during the fast and complex motor skills may not affect athletes’ performance too much.…”

Section: Discussionmentioning

confidence: 99%

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

Wang

Shan

et al. 2022

Sensors

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Developing real-time biomechanical feedback systems for in-field applications will transfer human motor skills’ learning/training from subjective (experience-based) to objective (science-based). The translation will greatly improve the efficiency of human motor skills’ learning and training. Such a translation is especially indispensable for the hammer-throw training which still relies on coaches’ experience/observation and has not seen a new world record since 1986. Therefore, we developed a wearable wireless sensor system combining with artificial intelligence for real-time biomechanical feedback training in hammer throw. A framework was devised for developing such practical wearable systems. A printed circuit board was designed to miniaturize the size of the wearable device, where an Arduino microcontroller, an XBee wireless communication module, an embedded load cell and two micro inertial measurement units (IMUs) could be inserted/connected onto the board. The load cell was for measuring the wire tension, while the two IMUs were for determining the vertical displacements of the wrists and the hip. After calibration, the device returned a mean relative error of 0.87% for the load cell and the accuracy of 6% for the IMUs. Further, two deep neural network models were built to estimate selected joint angles of upper and lower limbs related to limb coordination based on the IMUs’ measurements. The estimation errors for both models were within an acceptable range, i.e., approximately ±12° and ±4°, respectively, demonstrating strong correlation existed between the limb coordination and the IMUs’ measurements. The results of the current study suggest a remarkable novelty: the difficulty-to-measure human motor skills, especially in those sports involving high speed and complex motor skills, can be tracked by wearable sensors with neglect movement constraints to the athletes. Therefore, the application of artificial intelligence in a wearable system has shown great potential of establishing real-time biomechanical feedback training in various sports. To our best knowledge, this is the first practical research of combing wearables and machine learning to provide biomechanical feedback in hammer throw. Hopefully, more wearable biomechanical feedback systems integrating artificial intelligence would be developed in the future.

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Section: Discussionmentioning

confidence: 99%

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

Wang

Shan

et al. 2022

Sensors

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show abstract

“…The second sub-component is a 9DOF IMU (InvenSense ICM20948) in the form of a flexible IMU (Fig. 7) that can be attached to the pivot of any user tool to track motions (Kabuye et al 2022). In Fig.…”

Section: Inertial Measurement Unit Sensormentioning

confidence: 99%

“…However, optical trackers still require a direct line of sight between the user and the target object, which can negatively impact surgical technique. IMUs can track object motion without the need to physically see the device, which can be a significant advantage (Kabuye et al 2022).…”

Section: Introductionmentioning

confidence: 99%

A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

Kabuye

LeDuc²,

Cagan³

2023

Virtual Reality

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Successful surgical operations are characterized by preplanning routines to be executed during actual surgical operations. To achieve this, surgeons rely on the experience acquired from the use of cadavers, enabling technologies like virtual reality (VR) and clinical years of practice. However, cadavers, having no dynamism and realism as they lack blood, can exhibit limited tissue degradation and shrinkage, while current VR systems do not provide amplified haptic feedback. This can impact surgical training increasing the likelihood of medical errors. This work proposes a novel Mixed Reality Combination System (MRCS) that pairs Augmented Reality (AR) technology and an inertial measurement unit (IMU) sensor with 3D printed, collagen-based specimens that can enhance task performance like planning and execution. To achieve this, the MRCS charts out a path prior to a user task execution based on a visual, physical, and dynamic environment on the state of a target object by utilizing surgeon-created virtual imagery that, when projected onto a 3D printed biospecimen as AR, reacts visually to user input on its actual physical state. This allows a real-time user reaction of the MRCS by displaying new multi-sensory virtual states of an object prior to performing on the actual physical state of that same object enabling effective task planning. Tracked user actions using an integrated 9-Degree of Freedom IMU demonstrate task execution This demonstrates that a user, with limited knowledge of specific anatomy, can, under guidance, execute a preplanned task. In addition, to surgical planning, this system can be generally applied in areas such as construction, maintenance, and education.

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Visual servo control of endoscope-holding robot based on multi-objective optimization: System modeling and instrument tracking

et al. 2023

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Tracking of Scalpel Motions With an Inertial Measurement Unit System

Cited by 8 publications

References 39 publications

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw

A mixed reality system combining augmented reality, 3D bio-printed physical environments and inertial measurement unit sensors for task planning

Visual servo control of endoscope-holding robot based on multi-objective optimization: System modeling and instrument tracking

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