Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RR depth). This was compared to a reference respiratory rate determined from a capnograph (RR cap). The bias and root mean squared difference (RMSD) accuracy between RR depth and the reference RR cap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RR depth = 0.99 × RR cap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RR depth and RR cap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting.

There is considerable interest in the noncontact monitoring of patients as it allows for reduced restriction of patients, the avoidance of single-use consumables and less patient–clinician contact and hence the reduction of the spread of disease. A technology that has come to the fore for noncontact respiratory monitoring is that based on depth sensing camera systems. This has great potential for the monitoring of a range of respiratory information including the provision of a respiratory waveform, the calculation of respiratory rate and tidal volume (and hence minute volume). Respiratory patterns and apneas can also be observed in the signal. Here we review the ability of this method to provide accurate and clinically useful respiratory information.

Foot clearance is an important measurement variable in understanding trip falls. Current methods for measuring foot clearance are limited by their inability to capture multiple steps and confinement to a laboratory. Given that variation in this parameter is considered a factor in trip falling, it’s measurement in the field over multiple steps would be valuable. The development of an optical proximity sensor (OPS) has created the opportunity to collect this type of data. This study aimed to test the validity of an OPS through comparison with a motion capture system. Twenty subjects aged 33(+/−10) years, with a height of 174(+/−6) cm and a weight of 75(+/−12) kg, walked at three self selected velocities (preferred, slow, and fast). The OPS was mounted on the shoe of each subject. The motion of the shoe was recorded with a motion analysis system which tracked three markers attached to the shoe and outer casing of the OPS. Both systems were sampled at 50 Hz. The lowest point of the foot during the swing phase was recorded from each system and compared using intraclass correlation coefficients (ICCs). There was excellent agreement between the two systems. ICCs of 0.925 (all speeds), 0.931 (preferred), 0.966 (slow), and 0.889 (fast) were recorded. These results represent a strong agreement between the two systems in measuring the lowest point during swing. The OPS could thus be used instead of a camera system to record foot clearance, opening up opportunities for data collection over long periods of time, in natural settings. These results should be interpreted in context of the young healthy sample.

The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RRdepth) and tidal volume (TVdepth) estimates. The bias and root mean squared difference (RMSD) accuracy between RRdepth and the ventilator reference, RRvent, across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RRdepth = 0.96 × RRvent + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TVdepth and the reference TVvent across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TVdepth = 0.79 × TVvent—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RRdepth is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TVdepth may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.

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