Smart homes are proposed as a new location for the delivery of healthcare services. They provide healthcare monitoring and communication services, by using integrated sensor network technologies. We validate a hypothesis regarding older adults' adoption of home monitoring technologies by conducting a literature review of articles studying older adults' attitudes and perceptions of sensor technologies. Using current literature to support the hypothesis, this paper applies the tradeoff model to decisions about sensor acceptance. Older adults are willing to trade privacy (by accepting a monitoring technology), for autonomy. As the information captured by the sensor becomes more intrusive and the infringement on privacy increases, sensors are accepted if the loss in privacy is traded for autonomy. Even video cameras, the most intrusive sensor type were accepted in exchange for the height of autonomy which is to remain in the home.

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Older adults experience increased sleep movement disorders and sleep fragmentation, and these are associated with serious health consequences such as falls. Monitoring sleep fragmentation and restlessness in older adults can reveal information about their daily and long-term health status. Long-term home monitoring is only realistic within the contact of unobtrusive, non-contact sensors. This paper presents exploratory work using the pressure sensor array as an instrument for rollover detection. The sensor output is used to calculate a center of gravity signal, from which five features are extracted. These features are used in a decision tree to classify detected movements in two categories; rollovers and other movements. Rollovers were detected with a sensitivity and specificity of 82% and 100% respectively, and a Mathew's correlation coefficient of 0.86 when data from all sensor positions were included. Intrapositional and interpositional effects of movements on sensors placed throughout the bed are described.

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A pressure sensor array placed below a mattress can be used to estimate the breathing effort signal unobtrusively. When multiple breathing effort sensor outputs are available, there is sometimes a need to choose the sensor with the best approximation of the actual breathing effort. Previous work with pressure sensor arrays placed on top of or under mattresses used for respiration rate and breathing signal estimation have used either the amplitude or the power spectrum to choose the most representative sensor. These methods are both useful when the subject is still; however, pressure sensor signals also contain movement. We propose and test a spectral ratio method for selection in the presence of movement. The spectral ratio method is good at finding strong breathing signals and at discriminating movement signals from strong breathing signals. This method provides a mean correlation to respiration bands that is 4% higher than the next best method during small movements and 14% higher during larger movements.

Variability analysis of respiratory waveforms has been shown to provide key insights into respiratory physiology and has been used successfully to predict clinical outcomes. The current standard for quality assessment of the capnogram signal relies on a visual analysis performed by an expert in order to identify waveform artifacts. Automated processing of capnograms is desirable in order to extract clinically useful features over extended periods of time in a patient monitoring environment. However, the proper interpretation of capnogram derived features depends upon the quality of the underlying waveform. In addition, the comparison of capnogram datasets across studies requires a more practical approach than a visual analysis and selection of high-quality breath data. This paper describes a system that automatically extracts breath-by-breath features from capnograms and estimates the quality of individual breaths derived from them. Segmented capnogram breaths were presented to expert annotators, who labeled the individual physiological breaths into normal and multiple abnormal breath types. All abnormal breath types were aggregated into the abnormal class for the purpose of this manuscript, with respiratory variability analysis as the end-application. A database of 11,526 breaths from over 300 patients was created, comprising around 35% abnormal breaths. Several simple classifiers were trained through a stratified repeated ten-fold cross-validation and tested on an unseen portion of the labeled breath database, using a subset of 15 features derived from each breath curve. Decision Tree, K-Nearest Neighbors (KNN) and Naive Bayes classifiers were close in terms of performance (AUC of 90%, 89% and 88% respectively), while using 7, 4 and 5 breath features, respectively. When compared to airflow derived timings, the 95% confidence interval on the mean difference in interbreath intervals was ± 0.18 s. This breath classification system provides a fast and robust pre-processing of continuous respiratory waveforms, thereby ensuring reliable variability analysis of breath-by-breath parameter time series.