Contactless extraction of respiratory rate from depth and thermal sensors

Monitoring of respiration and restless sleep can help detect sleep disturbances that may be indicative of poor health and functional deficits. The current methods of estimating the respiratory rate such as Pneumograph, Capnograph, Photo-plethysmograph (PPG), Respiratory inductance plethysmography (RIP), involve sensors that are in contact with the patient. However, we have a few scenarios such as in hospitals and senior retirement communities where we would like to non-invasively collect the respiration rate and restless body motion where we are not able to place these types of sensors on patients. The initial requirement was to non-invasively monitor vital activity of patients in psychiatric centers. This work investigates a novel approach to estimate the respiratory rate of a person lying on the bed using depth and thermal sensors along with other signal processing algorithms. The initial proof of concept tests were conducted on three subjects. Additional testing on a diverse group of ten participants (ranging in age and body type) was performed to validate the algorithm and the data collection method. The depth and thermal waveforms captured were tested to explore a new approach for detecting individual respiratory rate noninvasively, using various algorithms to detect the region of the bed, common grids where a person is present, best signal selection from grids, and accurately estimate the respiratory rate and amount of body movement during sleep. The performance results at approximately 30 frames per second for the set of 10 participants was a mean error difference of 0.6 breaths per minute for the time domain algorithm and 0.8 breaths per minute for the frequency domain algorithm.Includes bibliographical reference

Body mass index and its effect on plantar pressure in overweight and obese adults

The proportion of overweight or obese adults is creating a growing problem throughout the world. Overweight and obesity have a significant influence on gait, and often cause difficulty. There is evidence to suggest that being overweight or obese places adults at a greater risk of developing foot complications such as osteoarthritis, tendonitis, plantar fasciitis, and foot ulcers. Increasingly, pressure ulcers have become a serious health problem. The purpose of this research is to investigate the effect of body weight on the feet, and to investigate the use of simulated body mass to study the effect of variable body mass on the foot plantar in adults aged 24 to 50 years of age while walking at a self-selected pace. A series of studies were undertaken to achieve the above purpose. The research involved: 1) assessing dynamic foot plantar pressure characteristics in adults who are normal weight, overweight or obese; 2) studying the gait impact of increased simulated body weight (SBW); and 3) evaluating the spatial relationship between the trace of the centroid of the area of contact with heel strike, midstance, and toe-off phases for the SBW groups. F-Scan in-shoe systems were utilised to gather the foot pressure data. The first study sought to investigate the effect of different body mass index (BMI) levels on plantar pressure distribution during walking, collection in fifteen voluntary participants were recruited. The BMI participants were divided into three groups (healthy, overweight and obese). The foot was divided into ten regions: heel (H), midfoot (MF), first metatarsal head (1MH), second metatarsal head (2MH), third metatarsal head (3MH), fourth metatarsal head (4MH), fifth metatarsal head (5MH), hallux (1stT), second toe (2ndT), and third to fifth toes (3rd-5thT). For each region, the following parameters were calculated: force (F), contact area (CA), contact pressure (CP), pressure time integral (PTI) and peak pressure (PP). The mean of the three repetitions of each subject was computed, and statistical procedures were performed with these mean ± standard deviation (SD) values. This study showed that the obese group had higher plantar pressure parameter values compared to the other two groups (overweight and healthy) for the ten different foot regions. The study observed significant changes in the parameters in the H and MHs (e.g. 2MH and 3MH) foot regions. The forefoot appears to be more sensitive to weight-related pressure under the foot than the rearfoot. Findings from this study indicate that being overweight or obese increases foot pressure measures, even for individuals with similar body features. Higher BMI values correlate with a higher load on the foot during walking in males. These findings have implications for pain and discomfort in the lower extremity in the obese while participating in activities of daily living such as walking. The second study investigated the effect of the research methodology involving the simulation of body weight (SBW) with additional weight, adding 10, 20, 30 kg to each participant’s body weight on plantar pressures. The sample comprised 31 adult males; each subject walked four times. The first walk was without any external weight (NBW, 0 kg), the second walk was with a weight of 10 kg, the third walk was with a weight of 20 kg and the last walk with a weight of 30 kg in the vest. The foot was divided into ten regions and for each region, the parameters were calculated the same way as the first study. At the end of this study it should be noted that SBW groups subjected to load have shown changes in foot plantar measure values compared to the NBW group. Most of the differences were found under H, MHs, 1stT and MF regions in the most clinically relevant parameters in SBW groups compared to the control group; the SBW groups showed higher values of plantar pressure. The results of the ICC showed a generally good to an excellent level of reliability, the quality of which was dependent on the regions of the foot and the variables investigated with SBW loads. This experiment pointed out that an insole pressure system is a reliable tool for evaluating foot plantar forces and pressures throughout the walk. The plantar pressure measures can be used in relative assessments, as the measures of repeatability are favourable for the measures and foot zones generally utilised in the study of people with clinical problems like neuropathic diabetics. In the final study, associations were investigated of the centroid (coordinates x-axis and y-axis) of the area of contact captured between normal (NBW) and simulated body weight (SBW) changes. The same 31 adult males who enrolled with the SBW tests were used to collect the centroid of the area of contact with the surface. This was located by calculating the geometric centre of a set of cloud points having the lowest z coordinate value. In this part, a foot pressure sensing insole was used to calculate the moment of heel strike, midstance and toe-off phases. Data were analysed descriptively (mean ± SD only). The outcome of this study, relating to specific individual characteristics of the centroid trace of the plantar contact area was compared with the heel strike, midstance, and toe-off phases for the SBW group with the NSBW group. X-axis and y-axis coordinates in the heel strike, midstance and toe-off phases under SBW with 30, 20, 10 kg had higher mean values compared to NSW. The x-axis and y-axis coordinates had mean values of 11.76, 9.68, and 7.76 mm; while the y-axis coordinates had mean values of 11.96, 9.89, and 8.18 mm. Moreover, x-axis and y-axis coordinates were assessed in the midstance phase under SBW with 30, 20, 10 kg with means of 6.59, 5.48, and 4.50 mm; while the y-axis coordinates had mean values of 6.38, 5.41, and 4.41 mm. In addition, x-axis and y-axis coordinates were assessed in the toe-off phase under SBW (30, 20, 10 kg) with mean values of 11.56, 9.67, and 7.97 mm; while the y-axis coordinates had mean values of 11.51, 9.39, 8.02 mm, respectively. X-axis and y-axis coordinates had mean values in relation to NBW in three phases: heel strike of 5.47 and 6.15; midstance of 2.99 and 3.05; and toe-off of 6.04 and 5.82, respectively. The x-locate and y-locate change can be calculate the change in rotation of the ankle joint. As the data was normalised according to the total time taken for the loading phase of the gait, the y-locational change was due partly to the extra weight, which could increase the time of lifting the foot. Therefore, the results showed that the x-locate and y-locate change can help to calculate the change in the rotation of the ankle joint. The project has shown that it is possible to demonstrate that obese people will, throughout their lives, adopt ways to effectively execute a particular activity. This finding provides a foundation for future clinical trials which could assist in preventing foot complications and could assist in the design of appropriate interventions to promote healthy outcomes for these adults. The simulated body weight resulted in a variation in plantar pressure distribution. Because the human foot adapts itself to any simulated condition, knowledge of the variation of pressure distributions of both feet can provide input for suitable guidelines for biomedical engineers. To promote the prevention of likely injury to the feet of overweight and obese people, the results of this study demonstrate the need to develop strategies which could include the building of an insole (orthosis) that absorbs foot plantar pressure

Modeling Humans at Rest with Applications to Robot Assistance

Humans spend a large part of their lives resting. Machine perception of this class of body poses would be beneficial to numerous applications, but it is complicated by line-of-sight occlusion from bedding. Pressure sensing mats are a promising alternative, but data is challenging to collect at scale. To overcome this, we use modern physics engines to simulate bodies resting on a soft bed with a pressure sensing mat. This method can efficiently generate data at scale for training deep neural networks. We present a deep model trained on this data that infers 3D human pose and body shape from a pressure image, and show that it transfers well to real world data. We also present a model that infers pose, shape and contact pressure from a depth image facing the person in bed, and it does so in the presence of blankets. This model similarly benefits from synthetic data, which is created by simulating blankets on the bodies in bed. We evaluate this model on real world data and compare it to an existing method that requires RGB, depth, thermal and pressure imagery in the input. Our model only requires an input depth image, yet it is 12% more accurate. Our methods are relevant to applications in healthcare, including patient acuity monitoring and pressure injury prevention. We demonstrate this work in the context of robotic caregiving assistance, by using it to control a robot to move to locations on a person’s body in bed.Ph.D