In this work, an effort has been made to determine the effect of different shape surgical burr on the thermogenesis during bone osteotomy. The abrasion during bone grinding leads to heat generation and subsequently rise in the temperature which may have adverse effects such as osteonecrosis, blood coagulation in the carotid artery, damage to sciatic nerves, and even loss of vision. So, mitigating the temperature rise during bone grinding is of paramount importance. Especially, in endoscopic endonasal approach (EEA) in which nasal passage is used for the inserting the grinding burr and reaching the target region. The miniature abrasion can significantly increase the temperature and hence leads to the thermal damage to nerves surrounding the temporal and frontal lobe. These parts of the brain controls movement, problem solving ability, behavior, personality mood, hearing, language, memory, speech, breathing, heart rate, consciousness etc. Furthermore, neurosurgeons rely on their personal surgical experience for estimating the temperature rise during grinding. However, this is much difficult for novice surgeons. Therefore, it becomes critically important to preserve the soft neural tissues and nerves amid bone grinding. To overcome these concerns, infrared thermography technique has been exploited to determine the possibility of thermogenesis during bone grinding by measuring the temperature rise and its distribution using infrared camera. All experiments have been carried at a constant set of process variables. The grinding zone is continuously flooded with the irrigating solution to remove the heat and bone debris away from the grinding site. It has been observed that convex tool shape generated lower maximum temperature i.e. 46.03 ℃ among all tools. The temperature produced by the convex tool is 12.06% lower than spherical tool, 33.39% lower than cylindrical tool, and 10.55% lower than tree-shape tool. The results showed that convex shape tool could prevent thermal necrosis in the bone as temperature caused (i.e. 46 ℃) was less than the threshold limit of osteonecrosis. Thermograms revealed that infrared thermography technique could be implemented for the in-vivo surgical operations for the measurement of temperature during bone grinding.

Smart ear-worn devices (called earables) are being equipped with various onboard sensors and algorithms, transforming earphones from simple audio transducers to multi-modal interfaces making rich inferences about human motion and vital signals. However, developing sensory applications using earables is currently quite cumbersome with several barriers in the way. First, time-series data from earable sensors incorporate information about physical phenomena in complex settings, requiring machine-learning (ML) models learned from large-scale labeled data. This is challenging in the context of earables because large-scale open-source datasets are missing. Secondly, the small size and compute constraints of earable devices make on-device integration of many existing algorithms for tasks such as human activity and head-pose estimation difficult. To address these challenges, we introduce Auritus, an extendable and open-source optimization toolkit designed to enhance and replicate earable applications. Auritus serves two primary functions. Firstly, Auritus handles data collection, pre-processing, and labeling tasks for creating customized earable datasets using graphical tools. The system includes an open-source dataset with 2.43 million inertial samples related to head and full-body movements, consisting of 34 head poses and 9 activities from 45 volunteers. Secondly, Auritus provides a tightly-integrated hardware-in-the-loop (HIL) optimizer and TinyML interface to develop lightweight and real-time machine-learning (ML) models for activity detection and filters for head-pose tracking. To validate the utlity of Auritus, we showcase three sample applications, namely fall detection, spatial audio rendering, and augmented reality (AR) interfacing. Auritus recognizes activities with 91% leave 1-out test accuracy (98% test accuracy) using real-time models as small as 6-13 kB. Our models are 98-740x smaller and 3-6% more accurate over the state-of-the-art. We also estimate head pose with absolute errors as low as 5 degrees using 20kB filters, achieving up to 1.6x precision improvement over existing techniques. We make the entire system open-source so that researchers and developers can contribute to any layer of the system or rapidly prototype their applications using our dataset and algorithms.

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