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Title of Journal: Med Biol Eng Comput

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Abbravation: Medical & Biological Engineering & Computing

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Springer Berlin Heidelberg

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DOI

10.1007/978-1-4614-4142-7_1

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ISSN

1741-0444

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Chest wall motion analysis in healthy volunteers a

Authors: James M Harte Christopher K Golby Johanna Acosta Edward F Nash Ercihan Kiraci Mark A Williams Theodoros N Arvanitis Babu Naidu
Publish Date: 2016/02/13
Volume: 54, Issue: 11, Pages: 1631-1640
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Abstract

Respiratory disease is the leading cause of death in the UK Methods for assessing pulmonary function and chest wall movement are essential for accurate diagnosis as well as monitoring response to treatment operative procedures and rehabilitation Despite this there is a lack of lowcost devices for rapid assessment Spirometry is used to measure air flow expired but cannot infer or directly measure full chest wall motion This paper presents the development of a lowcost chest wall motion assessment system The prototype was developed using four Microsoft Kinect sensors to create a 3D timevarying representation of a patient’s torso An evaluation of the system in two phases is also presented Initially static volume of a resuscitation mannequin with that of a Nikon laser scanner is performed This showed the system has slight underprediction of 0441  Next a dynamic analysis through the comparison of results from the prototype and a spirometer in nine cystic fibrosis patients and thirteen healthy subjects was performed This showed an agreement with correlation coefficients above 08656 in all participants The system shows promise as a method for assessing respiratory disease in a costeffective and timely manner Further work must now be performed to develop the prototype and provide further evaluationsRespiratory disease including lung cancer is the leading cause of death in the UK accounting for 920000 disabilityadjusted life years lost 10 It is the most frequent cause of disease in primary care in all age groups and the second most common cause of chronic conditionsFor patients who report to AE a quick and lowcost method of assessment is required One process currently used to assess respiratory disease in a lowcost and timeeffective manner is spirometry 14 Spirometry allows for the measurement of expired airflow from the lungs enabling physicians to better characterise the cause of breathlessness and to assess progression of respiratory disease over timeHowever spirometry can have significant limitations Firstly forced spirometric efforts allow assessment of initial diaphragm/muscle strength and can only measure total airflow in and out of the lungs it therefore provides a limited amount of feedback and does not allow physicians to identify motion at the chest and the relative contribution of different areas of each lung to the subjects’ respiratory function 16 This is particularly important in subjects with more focal lung abnormalities such as emphysematous bullae collapsed lung segments and previous surgical lung resection Secondly the effect of chest wall abnormalities such as respiratory muscle weakness and pectus excavatum as well as diaphragm movement cannot be assessed by spirometry 21 Thirdly since it is an effortdependent procedure there is a potential for inaccurate results in subjects unable to reliably perform a forced expiratory manoeuvre 12 eg children the elderly and subjects with hearing impairments learning difficulties or a language barrier Fourthly subjects with facial abnormalities or muscle weakness are often unable to form a tight seal around the mouthpiece preventing spirometry being accurately performed 8As a result of these limitations there has been increasing interest in the development of alternative methods of assessing respiratory function including chest wall motion analysis Systems such as magnetometers 17 respiratory inductance plethysmography 13 optoelectronic plethysmography 1 4 OEP and structured light plethysmography 5 SLP have been demonstrated for this purpose however these technologies have been shown can be cumbersome expensive time consuming difficult to interpret and not suited to the clinical environment A more recent method for evaluating chest wall motion is through the use of the Microsoft Kinect technology a lowcost motion tracking camera This system is portable low cost noninvasive and has been suggested as an alternative for spirometry 3 with positive correlations being shown Ye et al 2012 show correlations of r = 0966 24 However research demonstrating the use of this technology utilises a onecamera version of the system 3 7 23 24 This method follows a process of monitoring the chest wall only and detecting changes in surface The issue with this is that motion may affect the sample particularly large movements during analysis In addition it is difficult to compartmentalise different parts of the torso and also calculate volumes of the whole or parts of the torsoThis paper reports the development of a lowcost and timeeffective novel prototype for capturing dynamic chest wall motion using four Microsoft Kinect sensors An initial evaluation of this prototype is also presented involving healthy volunteers and adults with cystic fibrosis an inherited condition causing progressive respiratory failure 20This research created a system which was capable of assessing respiratory motion using a lowcost and timeeffective motion tracking device The device to be used was the Microsoft Kinect which is a human tracking peripheral used in the gaming industry for the Microsoft Xbox to provide lowcost 3D motion capture capabilities 6 The device is composed of an infrared IR laser projector an IR camera a colour camera and a microphone array The Kinect uses the IR projector to detect distance of an object from the sensor by emitting a single infrared beam which is split to create an invisible pattern of speckles 11 This pattern is captured by the IR camera and compared against a reference pattern stored in the device to calculate distance from external objects This study utilised four of these devices to create threedimensional representations of a subject’s torso over time so difference could be calculated by comparing the difference between frames


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