Gait Velocity Estimation using time interleaved between Consecutive Passive IR Sensor Activations

Gait velocity has been consistently shown to be an important indicator and predictor of health status, especially in older adults. It is often assessed clinically, but the assessments occur infrequently and do not allow optimal detection of key health changes when they occur. In this paper, we show that the time gap between activations of a pair of Passive Infrared (PIR) motion sensors installed in the consecutively visited room pair carry rich latent information about a person's gait velocity. We name this time gap transition time and show that despite a six second refractory period of the PIR sensors, transition time can be used to obtain an accurate representation of gait velocity. Using a Support Vector Regression (SVR) approach to model the relationship between transition time and gait velocity, we show that gait velocity can be estimated with an average error less than 2.5 cm/sec. This is demonstrated with data collected over a 5 year period from 74 older adults monitored in their own homes. This method is simple and cost effective and has advantages over competing approaches such as: obtaining 20 to 100x more gait velocity measurements per day and offering the fusion of location-specific information with time stamped gait estimates. These advantages allow stable estimates of gait parameters (maximum or average speed, variability) at shorter time scales than current approaches. This also provides a pervasive in-home method for context-aware gait velocity sensing that allows for monitoring of gait trajectories in space and time

Three studies in naturalized philosophical psychology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Linguistics and Philosophy, 1990.Includes bibliographical references.by Lawrence Jeffrey Kaye.Ph.D

Dyslipidemia and Blood-Brain Barrier Integrity in Alzheimer's Disease

Background. Blood-brain barrier (BBB) dysfunction may have a significant role in the pathogenesis of Alzheimer's disease (AD). Modifiable factors associated with BBB function may have therapeutic implication. This study tested the hypothesis that dyslipidemia is associated with BBB impairment in mild-to-moderate AD. Methods. Thirty-six subjects with AD were followed for 1 year. Fasting CSF and plasma were collected with clinical assessments at baseline and 12 months. BBB impairment was defined as CSF albumin index ≥9. Independent t-tests and linear regression assessed the relationship between plasma lipoproteins and BBB integrity. Results. Dyslipidemia was prevalent in 47% of the population, and in 75% of those with BBB impairment. Subjects with BBB impairment had significantly higher mean plasma triglyceride and lower HDL cholesterol (TG, P = 0.007; HDL, P = 0.043). Plasma triglycerides explained 22% of the variance in BBB integrity and remained significant after controlling for age, gender, ApoE-4 genotype, blood pressure, and statin use. Conclusion. Dyslipidemia is more prevalent in AD subjects with BBB impairment. Plasma triglyceride and HDL cholesterol may have a role in maintaining BBB integrity in mild-to-moderate Alzheimer's disease

Aging with the Internet of Things

International audienceWith capabilities for sensing, analysis, and actuation, the IoT has immense potential to help aging adults maintain health and independence. The Internet of Things (IoT) offers a host of new functionalities for sensing both environments (e.g., through motion-activity sensors) and individuals (e.g., through wearable smart watches) and for analyzing sensed data to make them actionable to users via notifications (e.g., through a smart speaker) and to applications via actuators (e.g., through a light switch). Wireless capability facilitates the deployment and use of such devices in every aspect of life and across age groups. This article explores the potential of the IoT to compensate for age-related changes by helping older individuals achieve and maintain health and independence. We briefly explain the Internet of Things and its relevance to aging, and then illustrate its applicability with a fictional 80-year-old. Although much of the technology exists to support the fictional scenario, there are challenges to the widespread attainment of the IoT's potential. We review these and then focus on the particular challenge of technology acceptance among the elderly, considering particular aspect

Predictors of the Progression of Dementia Severity in Brazilian Patients with Alzheimer's Disease and Vascular Dementia

Introduction. This study evaluates the progression of dementia and identifies prognostic risk factors for dementia. Methods. A group of 80 Brazilian community residents with dementia (34 with Alzheimer's disease and 46 with vascular dementia) was assessed over the course of 2 years. Data were analyzed with Cox regression survival analysis. Results. The data showed that education predicted cognitive decline (HR = 1.2; P < .05) when analyzed without controlling for vascular risk factors. After the inclusion of vascular risk factors, education (HR = 1.32; P < .05) and hypertension were predictive for cognitive decline (HR = 38; P < .05), and Alzheimer's disease diagnosis was borderline predictive (P = .055). Conclusion. Vascular risk factors interacted with the diagnosis of vascular dementia. Education was a strong predictor of decline

P2–320: Characteristics associated with willingness to participate in technology‐based behavioral clinical trials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152609/1/alzjjalz201305968.pd

Using the Guttman Scale to Define and Estimate Measurement Error in Items over Time: The Case of Cognitive Decline and the Meaning of “Points Lost”

We used a Guttman model to represent responses to test items over time as an approximation of what is often referred to as “points lost” in studies of cognitive decline or interventions. To capture this meaning of “point loss”, over four successive assessments, we assumed that once an item is incorrect, it cannot be correct at a later visit. If the loss of a point represents actual decline, then failure of an item to fit the Guttman model over time can be considered measurement error. This representation and definition of measurement error also permits testing the hypotheses that measurement error is constant for items in a test, and that error is independent of “true score”, which are two key consequences of the definition of “measurement error” –and thereby, reliability- under Classical Test Theory. We tested the hypotheses by fitting our model to, and comparing our results from, four consecutive annual evaluations in three groups of elderly persons: a) cognitively normal (NC, N = 149); b) diagnosed with possible or probable AD (N = 78); and c) cognitively normal initially and a later diagnosis of AD (converters, N = 133). Of 16 items that converged, error-free measurement of “cognitive loss” was observed for 10 items in NC, eight in converters, and two in AD. We found that measurement error, as we defined it, was inconsistent over time and across cognitive functioning levels, violating the theory underlying reliability and other psychometric characteristics, and key regression assumptions

2003-2004 Brass Festival

https://spiral.lynn.edu/conservatory_otherseasonalconcerts/1063/thumbnail.jp