Recent advances in wireless systems have demonstrated the possibility of tracking a person's respiration using the RF signals that bounce off her body. The resulting breathing signal can be used to infer the person's sleep quality and stages; it also allows for monitoring sleep apnea and other sleep disordered breathing; all without any body contact. Unfortunately however past work fails when people are close to each other, e.g., a couple sharing the same bed. In this case, the breathing signals of nearby individuals interfere with each other and super-impose in the received signal.This thesis presents DeepSleep, the first RF-based respiration monitoring system that can recover the breathing signals of multiple individuals even when they are separated by zero distance. To design DeepSleep, we model interference due to multiple reflected RF signals and demonstrate that the original breathing can be recovered via independent component analysis. We design a full system that eliminates interference and recovers the original breathing signals. We empirically evaluate DeepSleep using 21 nights of sleep and over 150 hours of data from 13 couples who share the bed. Our results show that DeepSleep is very accurate. Specifically, the differences between the breathing signals it recovers and the ground truth are on par with the difference between the same breathing signal measured at the person's chest and belly. Then, I would like to thank my collaborator Hao He. Hao and I have an enjoyable collaboration during this study. We have discussed every corner of our system and spent countless hours together improving its performance.
Insomnia is the most prevalent sleep disorder in the US. In-home insomnia monitoring is important for both diagnosis and treatment. Existing solutions, however, require the user to either maintain a sleep diary or wear a sensor while sleeping. Both can be quite cumbersome. This paper introduces EZ-Sleep, a new approach for monitoring insomnia and sleep. EZ-Sleep has three properties. First, it is zero effort, i.e., it neither requires the user to wear a sensor nor to record any data. It monitors the user remotely by analyzing the radio signals that bounce off her body. Second, it delivers new features unavailable with other devices such as automatically detecting where the user sleeps and her exact bed schedule, while simultaneously monitoring multiple users in different beds. Third, it is highly accurate. Its average error in measuring sleep latency and total sleep time is 4.9 min and 10.3 min, respectively.
Monitoring sleep posture is important for avoiding bedsores after surgery, reducing apnea events, tracking the progression of Parkinson's disease, and even alerting epilepsy patients to potentially fatal sleep postures. Today, there is no easy way to track sleep postures. Past work has proposed installing cameras in the bedroom, mounting accelerometers on the subject's chest, or embedding pressure sensors in their bedsheets. Unfortunately, such solutions jeopardize either the privacy of the user or their sleep comfort.
In this paper, we introduce BodyCompass, the first RF-based system that provides accurate sleep posture monitoring overnight in the user's own home. BodyCompass works by studying the RF reflections in the environment. It disentangles RF signals that bounced off the subject's body from other multipath signals. It then analyzes those signals via a custom machine learning algorithm to infer the subject's sleep posture. BodyCompass is easily transferable and can apply to new homes and users with minimal effort. We empirically evaluate BodyCompass using over 200 nights of sleep data from 26 subjects in their own homes. Our results show that, given one week, one night, or 16 minutes of labeled data from the subject, BodyCompass's corresponding accuracy is 94%, 87%, and 84%, respectively.
Study Objective: To assess the feasibility of a noncontact radio sensor as an objective measurement tool to study postoperative recovery from endometriosis surgery. Design: Prospective cohort pilot study. Setting: Center for minimally invasive gynecologic surgery at an academically affiliated community hospital in conjunction with in-home monitoring. Patients: Patients aged above 18 years who sleep independently and were scheduled to have laparoscopy for the diagnosis and treatment of suspected endometriosis. Interventions: A wireless, noncontact sensor, Emerald, was installed in the subjects' home and used to capture physiologic signals without body contact. The device captured objective data about the patients' movement and sleep in their home for 5 weeks before surgery and approximately 5 weeks postoperatively. The subjects were concurrently asked to complete a daily pain assessment using a numeric rating scale and a free text survey about their daily symptoms. Measurements and Main Results: Three women aged 23 years to 39 years and with mild to moderate endometriosis participated in the study. Emerald-derived sleep and wake times were contextualized and corroborated by select participant comments from retrospective surveys. In addition, self-reported pain levels and 1 sleep variable, sleep onset to deep sleep time, showed a significant (p <.01), positive correlation with next-day−pain scores in all 3 subjects: r = 0.45, 0.50, and 0.55. In other words, the longer it took the subject to go from sleep onset to deep sleep, the higher their pain score the following day. Conclusion: A patient's experience with pain is challenging to meaningfully quantify. This study highlights Emerald's unique ability to capture objective data in both preoperative functioning and postoperative recovery in an endometriosis population. The utility of this uniquely objective data for the clinician-patient relationship is just beginning to be explored.
A six-axis sensor array has been developed to quantify the 3D force and moment loads applied in scoliosis correction surgery. Initially this device was developed to be applied during scoliosis correction surgery and augmented onto existing surgical instrumentation, however, use as a general load sensor is also feasible. The development has included the design, microfabrication, deployment and calibration of a sensor array. The sensor array consists of four membrane devices, each containing piezoresistive sensing elements, generating a total of 16 differential voltage outputs. The calibration procedure has made use of a custom built load application frame, which allows quantified forces and moments to be applied and compared to the outputs from the sensor array. Linear or non-linear calibration equations are generated to convert the voltage outputs from the sensor array back into 3D force and moment information for display or analysis.
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