In physiotherapy, rehabilitation outcome is majorly dependent on the patient continuing exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, Sony PlayStation or Microsoft Xbox360 that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications. We present a concept to employ the Microsoft Kinect system as means to support patients during physiotherapy exercises at home. The system is intended to allow a physiotherapist to compose an individual set of exercises and to control the correct execution of those exercises through tracking the patient’s motions.

Background and objective: Lung mechanics measurements provide clinically useful information about disease progression and lung health. Currently, there are no commonly practiced methods to non-invasively measure both resistive and elastic lung mechanics during tidal breathing, preventing the important information provided by lung mechanics from being utilised. This study presents a novel method to easily assess lung mechanics of spontaneously breathing subjects using a dynamic elastance, single-compartment lung model. Methods: A spirometer with a built-in shutter was used to occlude expiration during tidal breathing, creating exponentially decaying flow when the shutter reopened. The lung mechanics measured were respiratory system elastance and resistance, separated from the exponentially decaying flow, and interrupter resistance calculated at shutter closure. Progressively increasing resistance was added to the spirometer mouthpiece to simulate upper airway obstruction. The lung mechanics of 17 healthy subjects were successfully measured through spirometry. Results: N = 17 (8 female, 9 male) healthy subjects were recruited. Measured decay rates ranged from 5 to 42/s, subjects with large variation of decay rates showed higher muscular breathing effort. Lung elastance measurements ranged from 3.9 to 21.2 cmH 2 O/L, with no clear trend between change in elastance and added resistance. Resistance calculated from decay rate and elastance ranged from 0.15 to 1.95 cmH 2 Os/L. These very small resistance values are due to the airflow measured originating from lowresistance areas in the centre of airways. Occlusion resistance measurements were as expected for healthy subjects, and increased as expected as resistance was added. Conclusions: This test was able to identify reasonable dynamic lung elastance and occlusion resistance values, providing new insight into expiratory breathing effort. Clinically, this lung function test could impact current practice. It does not require high levels of cooperation from the subject, allowing a wider cohort of patients to be assessed more easily. Additionally, this test can be simply implemented in a small standalone device, or with standard lung function testing equipment.

In physiotherapy, rehabilitation outcome is majorly dependent on the patient to continue exercises at home. To support a continuous and correct execution of exercises composed by the physiotherapist it is important that the patient stays motivated. With the emergence of game consoles such as Nintendo Wii, PlayStation Eye or Microsoft Kinect that employ special controllers or camera based motion recognition as means of user input those technologies have also been found to be interesting for other real-life applications such as providing individual physiotherapy exercises and an encouraging rehabilitation routine. Due to the intended use of those motion tracking systems in a computer-game environment it remains questionable if the accuracy of the skeleton joint tracking hardware and algorithms is suflicient for physiotherapy applications. We present a basic evaluation of the joint tracking accuracy where angles between various body extremities calculated by a Kinect system were compared with a high resolution motion capture system. Results show promising results with tracking deviations between 2.7° and 14.2° with a mean of the absolute deviations of 8.7°.

Wound area is a primary outcome measure in wound healing studies. This method comparison study evaluates differences of wound area measurements of a newly developed image analysis method based on wound edge contour to an existing method based on contrast tolerance. Digital images of 64 wounds were taken immediately after wounding matured in vitro 3D organotypic tissues with a biopsy punch. Wound area measurements were calculated using each image analysis method and then normalized. The method comparison study evaluates the difference of each paired measurements for all 64 wound areas. Measurement differences are demonstrated and evaluated in normalized data boxplots, scatter plots with a line of equality, data histogram and Normal probability plots, and a Bland-Altman plot of paired measure difference against mean. The measured wound areas using the tolerance method have large variability in comparison to the contour method measures. The tolerance method measures often underestimate and overestimate what is assumed to be an approximately repeatable initial wound size. Skewness in comparison plots are due to the ‘fat tails’ introduced by the variability of measurements of the tolerance method. In contrast, the contour method results in larger wound area measurements on average at a statistically significant level of difference. The relatively less variable range of contour method measurements suggest this method has more potential to agree with the ‘true’ wound area. Future work to improve the method are proposed for application of image analysis methods to distinguish true wound area and measurement error in time for wound healing treatment-control experiments.

IntroductionCoronavirus disease-2019 (COVID-19) pneumonia has different phenotypes. Selecting the patient individualized and optimal respirator settings for the ventilated patient is a challenging process. Electric impedance tomography (EIT) is a real-time, radiation-free functional imaging technique that can aid clinicians in differentiating the “low” (L-) and “high” (H-) phenotypes of COVID-19 pneumonia described previously.MethodsTwo patients (“A” and “B”) underwent a stepwise positive end-expiratory pressure (PEEP) recruitment by 3 cmH2O of steps from PEEP 10 to 25 and back to 10 cmH2O during a pressure control ventilation of 15 cmH2O. Recruitment maneuvers were performed under continuous EIT recording on a daily basis until patients required controlled ventilation mode.ResultsPatients “A” and “B” had a 7- and 12-day long trial, respectively. At the daily baseline, patient “A” had significantly higher compliance: mean ± SD = 53 ± 7 vs. 38 ± 5 ml/cmH2O (p < 0.001) and a significantly higher physiological dead space according to the Bohr–Enghoff equation than patient “B”: mean ± SD = 52 ± 4 vs. 45 ± 6% (p = 0.018). Following recruitment maneuvers, patient “A” had a significantly higher cumulative collapse ratio detected by EIT than patient “B”: mean ± SD = 0.40 ± 0.08 vs. 0.29 ± 0.08 (p = 0.007). In patient “A,” there was a significant linear regression between the cumulative collapse ratios at the end of the recruitment maneuvers (R2 = 0.824, p = 0.005) by moving forward in days, while not for patient “B” (R2 = 0.329, p = 0.5).ConclusionPatient “B” was recognized as H-phenotype with high elastance, low compliance, higher recruitability, and low ventilation-to-perfusion ratio; meanwhile patient “A” was identified as the L-phenotype with low elastance, high compliance, and lower recruitability. Observation by EIT was not just able to differentiate the two phenotypes, but it also could follow the transition from L- to H-type within patient “A.”Clinical Trial Registrationwww.ClinicalTrials.gov, identifier: NCT04360837.