Dorien Van Deun

Modeling human-bed interaction: the predictive value of anthropometric models in choosing the correct bed support

The sleep system (i.e. the combination of mattress and bed base) is an important factor of the sleep environment since it allows physical recuperation during sleep by providing proper body support. However, various factors influence the interaction between the human body and the sleep system. Contributing factors include body dimensions, distribution of body weight and stiffness of the sleep system across the mattress surface. During the past decade, the rise of several new bedding technologies has made it increasingly difficult for the consumer to select a proper sleep system. Therefore, this study presents a method to model human-bed interaction in order to objectively predict the ideal sleep system for a particular individual. The proposed method combines a personalized anthropometric model with standardized load-deflection characteristics of mattress and bed base. Results for lateral sleep positions show a root mean square deviation of 11.9 ± 6.1 mm between modeled spine shapes and validation shapes, derived from 3D surface scans of the back surface. The method showed to be a reliable tool to individually identify the sleep system providing superior support from a variety of possible mattress-bed base combinations.

Smart control of spinal alignment through active adjustment of mechanical bed properties during sleep

This study implements an algorithm for the autonomous control of spinal alignment during sleep by the active adjustment of mechanical bed characteristics according to the adopted sleep posture. Bed systems were used that allow active control of the mechanical stiffness in eight comfort zones by means of separately adjustable actuators. Mattress indentation measurements provide the input to detect body movement, recognize sleep posture, and --by combination with a subject specific human model --estimate spine shape. Comparison between the estimated spine shape and the desired shape results in new target values for the actuators. The control loop is repeated until the desired spine shape is reached. Results of overnight experiments revealed a significant improvement of spinal alignment during nights with active control of bed properties compared to a reference night without control. In addition, a significant improvement on subjectively perceived sleep quality was demonstrated after sleeping on the actively controlled systems.

Automatic sleep stage classification based on easy to register signals as a validation tool for ergonomic steering in smart bedding systems

Ergonomic sleep studies benefit from long-term monitoring in the home environment to cope with daily variations and habituation effects. Polysomnography allows to asses sleep accurately, but is costly, time-consuming and possibly disturbing for the sleeper. Actigraphy is cheap and user friendly, but for many studies lacks accuracy and detailed information. This proof-of-concept study investigates Least-Squares Support Vector Machines as a tool for automatic sleep stage classification (Wake-N1-Rem to N2-N3 separation), using automatic trainingset-specific filtered features as derived from three easy to register signals, namely heart rate, breathing rate and movement. The algorithms are trained and validated using 20 nights out of a 600 night database from over 100 different healthy persons. Different training and test set strategies were analyzed leading to different results. The more person-specific the training nights to the test nights, the better the classification accuracy as validated against the hypnograms scored by experts from the full polysomnograms. In the limit of complete person-specific training, the accuracy of the algorithm on the test set reached 94%. This means that this algorithm could serve its use in long-term monitoring sleep studies in the home environment, especially when prior person-specific polysomnographic training is performed.

Parametric Modeling of the Human Body Using Kinect Measurements

Personalized digital human modeling is useful for a wide variety of applications. An obvious interest comes from the entertainment industry, where movies and video games explore the possibilities of this technology. In biomechanics, human modeling assists in the design of person-specific solutions to improve the human well-being and ergonomics. Other applications exist in virtual dressing rooms, human-robot interactions in robotics, etc. Existing technology, although performing well, has the disadvantage of being expensive, immobile, not fully customizable and possibly requiring external body markers. To tackle these issues, this paper presents a modeling technique using the open source software MakeHuman, based on body measurements obtained with Microsoft’s Kinect. The current solution is able to retrieve these measurements when the person is standing in a calibrated scene, this means when the person’s position is known a priori. In order to retrieve the measurement data as a point cloud, and to process this point cloud, the PCL (Point Cloud Library) software is used, leading to a fully open source implementation. With these tools, solutions for person segmentation, measuring and personalized modeling are proposed. It appears that the current Kinect technology on itself is not very accurate for measuring body sizes. However, this work shows that the Kinect information combined with the MakeHuman modeling tool is valuable. The final model incorporates measures like body height, arm span, hip, waist and chest width, completed with information such as age, gender and weight. Evaluation of the resulting human model shows moderate to good results in modeling body height, hip and waist width, whereas chest width modeling is rather poor due to difficulties in chest width extraction from Kinect images..

Characterization of the respiratory and heart beat signal from an air pressure-based ballistocardiographic setup

Off-body detection of respiratory and cardiac activity presents an enormous opportunity for general health, stress and sleep quality monitoring. The presented setup detects the mechanical activity of both heart and lungs by measuring pressure difference fluctuations between two air volumes underneath the chest area of the subject. The registered signals were characterized over four different sleep postures, three different base air pressures within the air volumes and three different mattress top layer materials. Highest signal strength was detected in prone posture for both the respiratory and heart beat signal. Respiratory signal strength was the lowest in supine posture, while heart beat signal strength was lowest for right lateral. Heart beat cycle variability was highest in prone and lowest in supine posture. Increasing the base air pressure caused a reduction in signal amplitude for both the respiratory and the heart beat signal. A visco-elastic poly-urethane foam top layer had significantly higher respiration amplitude compared to high resilient poly-urethane foam and latex foam. For the heart beat signal, differences between the top layers were small. The authors conclude that, while the influence of the mattress top layer material is small, the base air pressure can be tuned for optimal mechanical transmission from heart and lungs towards the registration setup.