Techniques such as ballistocardiography (BCG) that can provide noninvasive long-term physiological monitoring have gained interest due to a growing recognition of adverse effects from poor sleep and sleep disorders. The noninvasive analysis of physiological signals (NAPS) system is a BCG-based monitoring system developed to measure heart rate, breathing rate, and musculoskeletal movement that shows promise as a general sleep analysis tool. Overnight sleep studies were conducted on 40 healthy subjects during a clinical trial at the University of Virginia. The NAPS system's measures of heart rate and breathing rate were compared to ECG, pulse oximetry, and respiratory inductance plethysmography (RIP). The subjects were split into a training dataset and a validation dataset, maintaining similar demographics in each set. The NAPS system accurately detected heart rate, averaged over the prescribed 30-s epochs, to within less than 2.72 beats per minute of ECG, and accurately detected breathing rate, averaged over the same epochs, to within 2.10 breaths per minute of RIP bands used in polysomnography.

The objective of this study was to assess the impact of passive health status monitoring on the cost of care, as well as the efficiencies of professional caregivers in assisted living. We performed a case-controlled study to assess economic impact of passive health status monitoring technology in an assisted-living facility. Passive monitoring systems were installed in the assisted-living units of 21 residents to track physiological parameters (heart rate and breathing rate), the activities of daily living (ADLs), and key alert conditions. Professional caregivers were provided with access to the wellness status of the monitored residents they serve. The monitored individuals' cost of medical care was compared to that of an age, gender, and health status matched cohort. Similarly, efficiency and workloads of professional caregivers providing care to the monitored individuals were compared to those of caregivers providing care to the control cohort in the control site. Over the 3-month period of the study, a comparison between the monitored and control cohorts showed reductions in billable interventions (47 vs. 73, p = 0.040), hospital days (7 vs. 33, p = 0.004), and estimated cost of care (21,187.02 dollars vs. 67,753.88 dollars with monitoring cost included, p = 0.034). A comparison between efficiency normalized workloads of monitoring and control sites' caregivers revealed significant differences both at the beginning (0.6 vs. 1.38, p = 0.041) and the end (0.84 vs. 1.94, p = 0.002) of the study. The results demonstrate that monitoring technologies have significantly reduced billable interventions, hospital days, and cost of care to payers, and had a positive impact on professional caregivers' efficiency.

This paper describes a study designed to assess the acceptance and some psychosocial impacts of monitoring technology in assisted living. Monitoring systems were installed in 22 assisted living units to track the activities of daily living (ADLs) and key alert conditions of residents (15 of whom were nonmemory care residents). Activity reports and alert notifications were sent to professional caregivers who provided care to residents participating in the study. Diagnostic use of the monitoring data was assessed. Nonmemory care residents were surveyed and assessed using the Satisfaction With Life Scale (SWLS) instrument. Pre- and post-installation SWLS scores were compared. Older adult participants accepted monitoring. The results suggest that monitoring technologies could provide care coordination tools that are accepted by residents and may have a positive impact on their quality of life

The National Institutes of Health (NIH) Sleep Disorders Research Plan expresses a need for methods that can non-invasively monitor sleep characteristics. Forty subjects were tested using a novel, passive ballistocardiography-based system during an overnight study. We examined our system's ability to measure heart rate as compared to EKG while we also investigated our system's apnea and arousal detection capabilities as compared to conventional polysomnography. We found a strong correlation (r=0.972, p<0.0001) in average heart rate computed over 480 thirty-second epochs when our method was compared to EKG. Additionally, we achieved a sensitivity of 89.2% and specificity of 94.6% in the automated detection of apneas. Similarly we attained a sensitivity of 77.3% and a specificity of 96.2% in the detection of arousals. These preliminary results demonstrate the effectiveness of our portable ballistocardiography-based system as compared to polysomnography and show promise that high quality sleep assessment can be performed in a home environment

Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is an important component of the living setting for older adult to expedite and improve the medical care provided to this population. Though there are several kinds of fall detectors currently available, they suffer from various drawbacks. Some of them are intrusive while others require the user to wear and activate the devices, and hence may fail in the event of user non-compliance. This paper describes the working principle and the design of a floor vibration-based fall detector that is completely passive and unobtrusive to the resident. The detector was designed to overcome some of the common drawbacks of the earlier fall detectors. The performance of the detector is evaluated by conducting controlled laboratory tests using anthropomorphic dummies. The results showed 100% fall detection rate with minimum potential for false alarms

This paper describes a study designed to assess the impacts of passive health status monitoring technology in home health. Monitoring systems were installed in the homes of 13 home health clients to track physiological parameters (heart rate, breathing rate, and gait), the activities of daily living (ADLs) and key alert conditions of residents, such as falls. Activity reports and alert notifications were sent to professional caregivers in order to refine and target the care administered to clients participating in the study. Informal caregivers of participants were provided with access to the ongoing wellness status of their loved ones. The potential diagnostic utility of the monitoring data, the subjects' quality of life and health related quality of life, as well as the quality of life, strain and burden levels of the informal caregivers were assessed. Pre- and post-installation scores were compared. The results suggest that monitoring technologies could provide care coordination tools that have a positive impact on the perceived quality of life of monitored individuals, as well as a reduction in the strain levels of their informal caregivers, and may have a positive impact on the participants' health related quality of life

This paper explores the validity of a rule-based inference method of selected independent activities of daily living (ADLs). An inexpensive ADL monitoring system was installed in the community for 37 days to monitor a middle-aged, healthy individual living alone. The subject was given a personal digital assistant (PDA), running custom activity diary software, and asked to record activities in real-time. Rule-based activity inference algorithms were refined on data from 17 days, and data from the remaining 20 days were used for validation. The chisquare statistic was computed for 2 x 2 contingency tables comparing activities detected by the algorithms to user-logged activities. The phi (r()) and Cohen's kappa (kappa) coefficients were computed as measures of correlation. After correcting for subject noncompliance in logging activities, the kappa correlation between the meal detection algorithm and the PDA record was 0.84, with 91% sensitivity, and 100% specificity. Similarly, the kappa correlation between the shower detection algorithm and the PDA record is 0.69, with 67% sensitivity and 100% specificity. The detection algorithms and the sensory data did not miss any main meals or showering activities recorded on the PDA. The results suggest that rule-based algorithms can successfully detect meal preparation and showering activities using simple low-cost detectors. The sensors and detection algorithms reported events not recorded by the occupant on the PDA attributed to reporting noncompliance. Overall, the PDA activity journal was a compromise between paper diaries, which are more time consuming to keep, and may result in higher noncompliance errors, and video recording, which is considered intrusive.

INTRODUCTION To date, there are few systems that provide a low-cost, passive way of acquiring important sleep monitoring data that requires no additional action from the subject outside of their normal daily routine [1]. There is, however, a great need for research in this area because of the large number of people affected by sleep related conditions who could benefit from knowing more about their sleep habits. About 40% of all American adults suffer from some kind of sleep disorder while about 70 million Americans are chronically sleep deprived [2]. Many feel that little substantial improvement can be made to correct their problems since 70% of sleep sufferers don't discuss the problem with their physician [2]. Objective sleep research has existed since 1922 when Szymansky ran the first such study [3]. The current gold standard for sleep research is polysomnography (PSG), which involves at least the recording of an electroencephalogram (EEG), a measurement of brain waves, an electrooculogram (EOG), a measurement of muscle activity in the eye area, and an electromyogram (EMG), a measurement of muscle activity in specific areas such as the arm or leg [4]. These electrode hookups prove valuable to assess sleep quality, but their attachment to the patient's body affects sleep. In an effort to provide a less obtrusive way to study sleep on a longer-term basis, actigraphs have been developed. These devices can be attached to any of the limbs to provide movement data based on the same principles behind accelerometers. They are also used in activity studies [5] and can provide 24 hour monitoring of the subject. This type of sensor, however, has its limitations in acquiring data that can be interpreted definitively to provide a good assessment of sleep quality. Researchers are dependent on patient journals to help correlate the data recorded on the actigraph and it is hard to distinguish different events that can occur throughout the night [3]. In addition, problems researchers have interpreting results from actigraphs are a direct result of the one-dimensional nature of the data recorded [4]. It is believed that valid sleep assessments can be made through the analysis of physiological characteristics such as body temperature, sleeping position and movement, breathing rate and heart rate. This

On average, we sleep about one-third of our lives. Sleep is something we do everyday; yet, it appears from the 2005 Sleep in America Poll conducted by the National Sleep Foundation (NSF) that while about 75% of Americans indicated that they have at least one symptom of a sleep disorder, the same survey indicates that 76% of Americans do not think they have a sleep problem and only 45% would report it to their doctor if they felt they had issues [1]. Although the people who voluntarily lose sleep can resolve their problem without treatment, 10–13% of Americans (30–40 million) are suffering from clinical sleep disorders that cause them to have disrupted sleep and, hence, affect their health [2]. Untreated sleep disorders lead to losses in productivity totaling around $46 billion annually in the USA, health problems including a higher risk for stroke, or even fatality, with an estimated 38,000 deaths annually attributed to complications from sleep apnea [3]. The condition of sleep apnea, or more broadly sleep-disordered breathing (SDB), is quite prevalent in the US population with at least 12 million Americans suffering from it [4] (Figure 5.1). In 2003, the National Institutes of Health (NIH) released its latest Sleep Disorders Research Plan that outlines research to date in specific areas, but also lays out needs for additional work to be done to advance our understanding of sleep in those areas. As mentioned earlier, SDB, the most prevalent of which is obstructive sleep apnea (OSA), affects millions of Americans, and many of them do not undergo treatment because the condition remains formally undiagnosed [2]. OSA is quantified by counting events termed apneas (stoppages of breathing during sleep for at least 10 s) and hypopneas (an abnormal respiratory event with a decrease in respiratory effort