Optimal Positioning of Inertial Measurement Units in a Smart Shirt for Determining Respiratory Volume.

Tidal volume can be estimated using the surface motions of the upper body induced by respiration. However, the precision and instrumentation of such estimation must be improved to allow widespread application. In this study, respiration induced changes in parameters that can be recorded with inertial measurement units are examined to determine tidal volumes. Based on the data of an optical motion capture system, the optimal positions of inertial measurement units (IMU) in a smart shirt for sets of 4, 5 or 6 sensors were determined. The errors observed indicate the potential to determine tidal volumes using IMUs in a smart shirt.Clinical Relevance- The measurement of respiratory volumes via a low-cost and unobtrusive smart shirt would be a major advance in clinical diagnostics. In particular, conventional methods are expensive, and uncomfortable for conscious patients if measurement is desired over an extended period. A smart-shirt based on inertial sensors would allow a comfortable measurement and could be used in many clinical scenarios - from sleep apnoea monitoring to homecare and respiratory monitoring of comatose patients.

Improving the Generalisability of Deep CNNs by Combining Multi-stage Features for Surgical Tool Classification.

Dataset characteristics play an important role in training convolutional neural networks (CNNs) to evolve optimal features required to perform a specific task. Due to the high cost of recording and labelling surgical data, available datasets are relatively small in size and have been predominantly acquired at single sites. CNN-based approaches have been widely adapted to analyse surgical workflow using single-site datasets. Therefore, assessing generalised performance on data from different institutions has not been investigated. In this work, a CNN model that combines features from multiple stages to develop more accurate and generalised tool classification was introduced. An extensive evaluation of the proposed approach on three different datasets showed better generalised performance of our approach compared to base CNN models. The proposed approach achieved mAP values of 91.46%, 69.02% and 37.14% on the Cholec80, Cholec20 and Gyna05 datasets, respectively. The generalisation performance was improved on the achieved base CNN models mAP by about 7%. Clinical Relevance- In this research, we proposed a method to improve generalisation capability of CNN models which will have positive impact on developing more robust assistive systems that can support the surgeon and improve patient care.

Effects of Intra-Abdominal Pressure on Lung Mechanics during Laparoscopic Gynaecology.

Investigating the relations between surgical actions and physiological reactions of the patient is essential for developing pre-emptive model-based systems. In this study, the effects of insufflating abdominal cavity with CO2 in laparoscopic gynaecology on the respiration system were analysed. Real-time recordings of anaesthesiology and surgical data of five subjects were acquired and processed, and the correlation between lung mechanics and the intra-abdominal pressure was evaluated. Alterations of ventilation settings undertaken by the anaesthesiologist were also considered. Experimental results demonstrated the high correlation with a mean Pearson coefficient of 0.931.Clinical Relevance- This study demonstrates the effects of intra-abdominal pressure during laparoscopy on lung mechanics and enables developing predictive models to promote a greater awareness in operating rooms.