Abstract: Computer implemented methods of identifying in-dwelling mobility patterns of a subject are disclosed. The methods involve collecting information from a plurality of sensors in the dwelling of the subject. Contextual variables associated with the dwelling, the subject, and/or the surroundings of the dwelling are determined. A mobility pattern within the dwelling based upon the data and the contextual variables is calculated using a regression model.

Abstract: Systems and methods for determining time a subject spends outside of their home based on signals received from unobtrusive sensors in the home are disclosed. In one example approach, a method for determining time a subject spends outside of their home comprises receiving sensor firing data for a duration from sensors in the home, dividing the duration into epochs, extracting features from the sensor firing data received during the epoch, and classifying each epoch as out-of-home or in-home based on the features extracted from the sensor firing data during the epoch.

Abstract: A system and method for conducting a clinical trial of a medical treatment of human patients. The system uses an array of sensors in the patient's home for substantially continuously monitoring one or more objective functions of the patient. The monitored objective functions of the patient are compared against a baseline set of data for the monitored objective functions for the patient or a set of patients. The comparison includes detecting a trend of deviation between the one or more monitored objective functions of the patient and the baseline set of data. The trend of deviation can be correlated with an application or nonapplication of the medical treatment to the patient.

Abstract: Methods and apparatuses for automatically identifying sleep apnea in a subject based on load cell signal data obtained from load cells coupled with one or more supports of a bed are disclosed. In one example approach, a method comprises continuously collecting load cell signal data from one or more load cells positioned below one or more supports of a bed, processing the signal data to obtain processed signal data, extracting features from the processed signal data, calculating a sleep apnea severity parameter based on the extracted features via a model, and identifying sleep apnea in the subject based on the sleep apnea severity parameter.

Abstract: Embodiments provide systems, methods and apparatuses for monitoring the sleep of a subject in a home environment. In embodiments, load cells placed under bed supports may be coupled to a computing device that may process the load cell data to detect disordered breathing. In some embodiments, a computing device may apply a pattern recognition algorithm to load cell data to distinguish between normal movements and movements associated with a sleep disorder. In an embodiment, apparatuses and methods for monitoring sleep may perform functions associated with detection of sleep disturbances and/or identify a sleep disorder.

Abstract: Embodiments provide systems, methods and apparatuses for monitoring the sleep of a subject in a home environment. In embodiments, load cells placed under bed supports may be coupled to a computing device that may process the load cell data to detect disordered breathing. In some embodiments, a computing device may apply a pattern recognition algorithm to load cell data to distinguish between normal movements and movements associated with a sleep disorder. In an embodiment, apparatuses and methods for monitoring sleep may perform functions associated with detection of sleep disturbances and/or identify a sleep disorder.