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With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.

Poor quality of sleep increases the risk of many adverse health outcomes. Some measures of sleep, such as sleep efficiency or sleep duration, are calculated from periods of time when a patient is asleep and awake. The current method for assessing sleep and wakefulness is based on polysomnography, an expensive and inconvenient method of measuring sleep in a clinical setting. In this paper, we suggest an alternative method of detecting periods of sleep and wake that can be obtained unobtrusively in a patient's own home by placing load cells under the supports of their bed. Specifically, we use a support vector machine to classify periods of sleep and wake in a cohort of patients admitted to a sleep lab. The inputs to the classifier are subject demographic information, a statistical characterization of the load cell derived signals, and several sleep parameters estimated from the load cell data that are related to movement and respiration. Our proposed classifier achieves an average sensitivity of 0.808 and specificity of 0.812 with 90% confidence intervals of (0.790, 0.821) and (0.798, 0.826), respectively, when compared to the "gold-standard" sleep/wake annotations during polysomnography. As this performance is over 27 sleep patients with a wide variety of diagnosis levels of sleep disordered breathing, age, body mass index, and other demographics, our method is robust and works well in clinical practice.

Quality of sleep is an important attribute of an individual's health state and its assessment is therefore a useful diagnostic feature. Changes in the patterns of mobility in bed during sleep can be a disease marker or can reflect various abnormal physiological and neurological conditions. This paper describes a method for detection of movement in bed that is evaluated on data collected from patients admitted for regular polysomnography. The system is based on load cells installed at the supports of a bed. Since the load cell signal varies the most during movement, the approach uses a weighted combination of the short-term mean-square differences of each load cell signal to capture the variations in the signal caused by movement. We use a single univariate Gaussian model to represent each class: movement versus non-movement. We assess the performance of the method against manual annotation performed by a sleep clinic technician from seventeen patients. The proposed detection method achieved an overall sensitivity of 97.9% and specificity of 98.7%.

We describe a low-cost wearable system that tracks the location of individuals indoors using commonly available inertial navigation sensors fused with radio frequency identification (RFID) tags placed around the smart environment. While conventional pedestrian dead reckoning (PDR) calculated with an inertial measurement unit (IMU) is susceptible to sensor drift inaccuracies, the proposed wearable prototype fuses the drift-sensitive IMU with a RFID tag reader. Passive RFID tags placed throughout the smart-building then act as fiducial markers that update the physical locations of each user, thereby correcting positional errors and sensor inaccuracy. Experimental measurements taken for a 55 m × 20 m 2D floor space indicate an over 1200% improvement in average error rate of the proposed RFID-fused system over dead reckoning alone.

Scratching is a symptom of many dermatological disorders, especially atopic dermatitis. For the development of anti-itch medications, there is a need for objective measures of scratching. Wrist actigraphy (monitoring wrist and hand movements with micro-accelerometers) is a promising method for assessing scratching; however, currently available technology has a limited capacity to discriminate scratching from other similar movements. In this study, we investigated methods to improve the specificity of actigraphy for scratch detection on movement data collected from subjects using the PAM-RL actigraph. A k-means cluster analysis was used to differentiate scratching from walking and restless sleep, which are potential confounds for nighttime scratching. Features used in the analysis include variance, peak frequency, autocorrelation value at one lag, and number of counts above 0.01 g's. The k-means cluster analysis exhibited a high sensitivity (0.90 ± 0.10) and specificity for walking (0.98 ± 0.05) and restless sleep (0.88 ± 0.06), respectively, demonstrating the separability of these activities. This work indicates that the features described here can be used to develop a classifier that discriminates scratch from other activities. The described method of scratch detection shows promise as an objective method for assessing scratching movements in clinical trials and longitudinal studies of scratch.

Sleep is characterized by episodes of immobility interrupted by periods of voluntary and involuntary movement. Increased mobility in bed can be a sign of disrupted sleep that may reduce sleep quality. This paper describes a method for classification of the type of movement in bed using load cells installed at the corners of a bed. The approach is based on Gaussian Mixture Models using a time-domain feature representation. The movement classification system is evaluated on data collected in the laboratory, and it classified correctly 84.6% of movements. The unobtrusive aspect of this approach is particularly valuable for longer-term home monitoring against a standard clinical setting.

Ubiquitous and unobtrusive in-home monitoring has the potential to detect physical and mental decline earlier and with higher precision than current clinical methods. However, given that this field is in its infancy, the specific metrics through which these changes are detected are not well defined. The work presented here offers room-transitions, the act of physically moving from one area of a home to another, as a quantifiable measure for total daily activity that can be inferred from a network of passive infrared sensors. We describe a method to calculate this value from raw sensor data and validate this method on an acute health event: an 18-day quarantine at a retirement community that was initiated in the midst of a norovirus outbreak. The results from this case study show that room-transition values increased significantly as subjects remained in their homes during the quarantine, demonstrating a mean increase of 12 transitions per day. Furthermore, a time-adjusted measure of room-transitions is examined that did not significantly change across the group. Finally, the healthy subjects and those that fell ill were analyzed separately, and significant differences were found between them for both the raw and time-adjusted metrics. As detection algorithms improve, these types of measures may be useful in the early detection of a change in health status.

Gait velocity has repeatedly been shown to be an important indicator and predictor of both cognitive and physical function, especially in elderly. However, clinical gait assessments are conducted infrequently and cannot distinguish between abrupt changes in function and changes that occur more slowly over time. Collecting gait measurements continuously in-home has recently been proposed and validated to overcome these clinical limitations. In this paper, we describe the longitudinal analysis of in-home gait velocity collected unobtrusively from passive infrared motion sensors. We first describe a model for the probability density function of the in-home gait velocities. We then describe estimation of the evolution of the density function over time and report empirically determined algorithm parameters that have performed well over a wide variety of different gait velocity data. Finally, we demonstrate how this approach allows detection of significant events (abrupt changes in function) and slower changes over time in gait velocity data collected from a sample of two elderly subjects in the Intelligent Systems for Assessing Aging Changes (ISAAC) study.

Individuals who suffer from acid reflux at night, who snore chronically, or who have sleep apnea are frequently encouraged to sleep in a particular lying position. Side sleeping decreases the frequency and severity of obstructive respiratory events (e.g. apnea and hypopnea) in patients with positional sleep apnea. It has been suggested that individuals with Gastroesophageal Reflux Disease sleep on their left sides in order to help minimize symptoms. In this paper, we present a method of predicting the position of an individual lying on the bed using load cells placed under each of the bed supports. Our results suggest that load cells utilized in this manner could be successfully implemented into a system that tracks or helps train individuals to sleep in a particular lying position.

Disrupted sleep patterns are a significant problem in the elderly, leading to increased cognitive dysfunction and risk of nursing home placement. A cost-effective and unobtrusive way to remotely monitor changing sleep patterns over time would enable improved management of this important health problem. We have developed an algorithm to derive sleep parameters such as bed time, rise time, sleep latency, and nap time from passive infrared sensors distributed around the home. We evaluated this algorithm using 404 days of data collected in the homes of 8 elderly community-dwelling elders. Data from this algorithm were highly correlated to ground truth measures (bed mats) and were surprisingly robust to variability in sensor layout and sleep habits.

In this paper we describe a preliminary modeling and analysis of a unique data set comprising unobtrusive and continuous measurements of gait velocity in the elder participants' residences. The data have been collected as a part of a longitudinal study aimed at early detection of cognitive decline. We motivate these analyses by first presenting evidence that suggests significant relationship between gait parameters and cognitive functions. We then describe a simple, model-based approach to the analysis of gait velocity using a weighted correlation function estimates. One of the main challenges is due to the fact that the daily estimates of the gait parameters vary with the number of walks. We illustrate the importance of using weighted as opposed to unweighted estimates on a sample of different houses. The correlation functions appear to capture behavioral differences that can be related to the cognitive functioning of the participants.

A patient's sleep/wake schedule is an important step underlying clinical evaluation of sleep-related complaints. Aspects related to timing of a person's sleep routine provide important clues regarding diagnosis and treatments. Solutions for sleep complaints may sometimes rely solely on changes in habits and life style, based on what is learned from daily rest-activity patterns. This paper describes an approach for determining two states, in-bed and out-of-bed, using load cells under the bed. These states are important because they can help characterize rest-activity patterns at nighttime or detect bed exits in hospitals or nursing homes. The information derived from the load cells is valuable as an objective and continuous measure of daily patterns, and it is particularly valuable in sleep studies in populations who would not be able to remember specific hours to complete sleep diaries. The approach is evaluated on data collected in a laboratory experiment, in a sleep clinic, and also on data collected from residents of an assisted-living facility.

Walking speed and activity are important measures of functional ability in the elderly. Our earlier studies have suggested that continuous monitoring may allow us to detect changes in walking speed that are also predictive of cognitive changes. We evaluated the use of passive infrared (PIR) sensors for measuring walking speed in the home on an ongoing basis. In comparisons with gait mat estimates (ground truth) and the results of a timed walk test (the clinical gold standard) in 18 subjects, we found that the clinical measure overestimated typical walking speed, and the PIR sensor estimations of walking speed were highly correlated to actual gait speed. Examination of in-home walking patterns from more than 100,000 walking speed samples for these subjects suggested that we can accurately assess walking speed in the home. We discuss the potential of this approach for continuous assessment.

Accurate assessment of mobility in bed presents challenges to clinicians and researchers alike. Mobility is traditionally assessed by either overnight polysomnograph recording or wrist actigraphy. This paper describes an alternative system for unobtrusive and continuous monitoring of sleep movements that uses load sensors installed at the corners of a bed. This work is focused on the detection and classification of clinically relevant types of movement based on the forces sensed by load cells. The accuracy of the system for detecting movement has been evaluated using data collected in a laboratory setting. We also present a comparison of the proposed system with wrist-actigraphy.

Sleep disturbances are prevalent, financially taxing, and have a negative effect on health and quality of life. One of the most common sleep disturbances is obstructive sleep apnea-hypopnea syndrome (OSAHS) which frequently goes undiagnosed. The gold standard for diagnosing OSAHS is polysomnography (PSG)-a procedure that is inconvenient, time-consuming, and interferes with normal sleep patterns. We are investigating an alternative to PSG in which unobtrusive load cells fitted under the bed are used to monitor movement, heart rate, and respiration. In this paper we describe how load cell data can be used to distinguish between clinically relevant disordered breathing (apneas and hypopneas) and normal respiration. The method correctly classified disordered breathing segments with a sensitivity of 0.77 and a specificity of 0.91.

Unobtrusive in-home computer monitoring could one day be used to deliver cost-effective diagnostic information about the cognitive abilities of the elderly. This could allow for early detection of cognitive impairment and would additionally be coupled with the cost advantages that are associated with a semi-automated system. Before using the computer usage data to draw conclusions about the participants, we first needed to investigate the nature of the data that was collected. This paper represents a forensics style analysis of the computer usage data that is being collected as part of a larger study of cognitive decline, and focuses on the isolation and removal of non user-generated activities that were recorded by our computer monitoring software (CMS).

Poor medication adherence is a serious medical problem, particularly in older adults. Various solutions have been developed to remind people to take their medications, but these systems are usually simple time-based alarm systems that are not particularly effective. We describe a system that is context aware, and that utilizes information about past patterns of behavior plus the current context to provide prompts at the appropriate time and place. A case study from our initial deployment of the system to eleven older adults illustrates the possibilities and advantages of context aware prompting systems.