scholarly journals Engaging older adults in the visualization of sensor data facilitated by an open platform for connected devices

2016 ◽  
Vol 24 (4) ◽  
pp. 541-550 ◽  
Author(s):  
Christian Bock ◽  
George Demiris ◽  
Yong Choi ◽  
Thai Le ◽  
Hilaire J. Thompson ◽  
...  
Keyword(s):  
Older Adults ◽  
Sensor Data ◽  
Open Platform

scholarly journals Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 111012-111029 ◽  
Author(s):  
Flavia D. Casagrande ◽  
Jim Torresen ◽  
Evi Zouganeli
Keyword(s):  
Older Adults ◽  
Sensor Data ◽  
Binary Sensor

scholarly journals OPC Protocol Application for Real-Time Carbon Monitoring System for Industrial Environment

2017 ◽  
Vol 7 (2) ◽  
pp. 1051 ◽  
Author(s):  
Mohd Faiz Rohani ◽  
Noor Azurati Ahmad ◽  
Shamsul Sahibuddin ◽  
Salwani Mohd Daud
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Ghg Emissions ◽  
Measurement Data ◽  
Sensor Data ◽  
Successful Implementation ◽  
Open Platform ◽  
Environmental Monitoring System ◽  
Normal Environment ◽  
Communications Protocol

Global warming is referred to the rise in average surface temperatures on earth primarily due to the Greenhouse Gases (GHG) emissions such as Carbon Dioxide (CO<sub>2</sub>). Monitoring the emissions, either direct or indirect from the industrial processes, is important to control or to minimize their impact on the environment. Most of the existing environmental monitoring system is being designed and developed for normal environment monitoring. Hence, the aim of this project is to develop industrial CO<sub>2 </sub>emission monitoring system which implements industrial Open Platform Communications (OPC) protocol in an embedded microcontroller. The software algorithm based on OPC data format has been designed and programmed into the Arduino microcontroller to interface the sensor data to any existing industrial OPC compliant Supervisory Control and Data Acquisition (SCADA) system<strong>. </strong>The system has been successfully tested in a lab with the suitable environment for real-time CO<sub>2 </sub>emissions measurement. The real-time measurement data has been shown in an industrial SCADA application which indicates successful implementation of the OPC communications protocol.

Early Illness Recognition Using In-home Monitoring Sensors and Multiple Instance Learning

2012 ◽  
Vol 51 (04) ◽  
pp. 359-367 ◽  
Author(s):  
A. Mahnot ◽  
M. Popescu
Keyword(s):  
Older Adults ◽  
Independent Living ◽  
Characteristic Curve ◽  
Home Monitoring ◽  
Receiver Operator Characteristic Curve ◽  
Multiple Instance Learning ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Cognitive Changes ◽  
Peace Of Mind

SummaryBackground: Many older adults in the US prefer to live independently for as long as they are able, despite the onset of conditions such as frailty and dementia. Solutions are needed to enable independent living, while enhancing safety and peace of mind for their families. Elderly patients are particularly at-risk for late assessment of cognitive changes.Objectives: We predict early signs of illness in older adults by using the data generated by a continuous, unobtrusive nursing home monitoring system.Methods: We describe the possibility of employing a multiple instance learning (MIL) framework for early illness detection. The MIL framework is suitable for training classifiers when the available data presents temporal or location uncertainties.Results: We provide experiments on three datasets that prove the utility of the MIL framework. We first tuned our algorithms on a set of 200 normal/abnormal behavior patterns produced by a dedicated simulator. We then conducted two retrospective studies on residents from the Tiger Place aging in place facility, aged over 70, which have been monitored with motion and bed sensors for over two years. The presence or absence of the illness was manually assessed based on the nursing visit reports.Conclusions: The use of simulated sensor data proved to be very useful for algorithm development and testing. The results obtained using MIL for six Tiger Place residents, an average area under the receiver operator characteristic curve (AROC) of 0.7, are promising. However, more sophisticated MIL classifiers are needed to improve the performance.

scholarly journals POOR SLEEP QUALITY IS RELATED TO DECREASED WHITE MATTER INTEGRITY IN BRAIN NOCICEPTIVE PATHWAYS IN OLDER ADULTS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S367-S368
Author(s):  
Anna R Egbert ◽  
Ryan S Falck ◽  
John R Best ◽  
Linda Li ◽  
Lynne Feehan ◽  
...  
Keyword(s):  
Older Adults ◽  
White Matter ◽  
Sleep Quality ◽  
Sleep Efficiency ◽  
Sensor Data ◽  
White Matter Integrity ◽  
Poor Sleep ◽  
Accelerometer Data ◽  
Poor Sleep Quality ◽  
Brain White Matter

Abstract Poor sleep quality, decreased physical activity (PA) and increased sedentary behavior (SB) are common characteristics of older adults. Notably, these factors play an important role in brain health. We examined the relationship between sleep quality, PA, SB and brain white matter integrity (WM) in older adults with osteoarthritis (OA). We retained data on 16 participants (mean age 60, SD=7.7) from a larger Monitor-OA cohort recruited from Metro Vancouver, BC, Canada. Sleep efficiency and duration, amount of time spent on PA and SB daily over a period of one week was acquired with an objective measure – the multi-sensor monitor SenseWear Mini which integrates tri-axial accelerometer data, physiological sensor data and personal demographic information. Brain WM tractography was calculated from fractional anisotropy data obtained with diffusion weighted magnetic resonance imaging. Voxelwise group-level statistics examined the effects of our variables of interest on the integrity of brain WM tracts while controlling for participants age. We found that lower sleep efficiency was related to decreased integrity in WM tracts of frontal, temporal lobes, precuneus and thalamus (Bonferroni corrected p&lt;0.05). Shorter sleep was related to lower WM integrity in frontal regions, posterior cingulate and insula radiations (Bonferroni corrected p&lt;0.05). No significant effects were noted for PA or SB. The identified brain regions are involved in sleep processes but further overlap with the nociceptive brain network. Our findings suggest that neural mechanisms related to sleep disturbance may also involve pain-related processing in older adults.

Clinical gait assessment of older adults using open platform tools

2011 ◽  
Author(s):  
Cliodhna Ni Scanaill ◽  
Barry R. Greene ◽  
Emer P. Doheny ◽  
Karol O'Donovan ◽  
Terrance O'Shea ◽  
...  
Keyword(s):  
Older Adults ◽  
Open Platform ◽  
Gait Assessment ◽  
Clinical Gait Assessment

scholarly journals Comparison of Embedded Sensor Data for Long-Term Care Residents Before and After Onset of the COVID-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 95-96
Author(s):  
Erin Robinson ◽  
Wenlong Wu ◽  
Geunhye Park ◽  
Gashaye M Tefera ◽  
Kari Lane ◽  
...  
Keyword(s):  
Mental Health ◽  
Older Adults ◽  
Long Term Care ◽  
Sensor Data ◽  
Term Care ◽  
Health Concerns ◽  
Front Door ◽  
Depth Sensors ◽  
Mental Health Concerns

Abstract Older adults have experienced greater isolation and mental health concerns during the COVID-19 pandemic. In long-term care (LTC) settings, residents have been particularly impacted due to strict lockdown policies. Little is known about how these policies have impacted older adults. This study leveraged existing research with embedded sensors installed in LTC settings, and analyzed sensor data of residents (N=30) two months pre/post the onset of the U.S. COVID-19 pandemic (1/13/20 to 3/13/20, 03/14/20 to 5/13/20). Data from three sensors (bed sensors, depth sensors, and motion sensors) were analyzed for each resident using paired t-tests, which generated information on the resident’s pulse, respiration, sleep, gait, and motion in entering/exiting their front door, living rooms, bedrooms, and bathrooms. A 14.4% decrease was observed in front door motion in the two months post-onset of the pandemic, as well as a 2.4% increase in average nighttime respiration, and a 7.6% increase in nighttime bed restlessness. Over half of our sample (68%) had significant differences (p&lt;0.05) in restlessness. These results highlight the potential impact of the COVID-19 pandemic and social distancing policies on older adults living in LTC. While it is not surprising that significant differences were found in the front door motion sensor, the bed sensor data can potentially shed light on how sleep was impacted during this time. As older adults experienced additional mental health concerns during this time, their normal sleep patterns could have been affected. Implications could help inform LTC staff, healthcare providers, and self-management of health approaches among older adults.

scholarly journals Automatic Quantification of Tandem Walking Using a Wearable Device: New Insights Into Dynamic Balance and Mobility in Older Adults

2020 ◽  
Vol 76 (1) ◽  
pp. 101-107
Author(s):  
Natalie Ganz ◽  
Eran Gazit ◽  
Nir Giladi ◽  
Robert J Dawe ◽  
Anat Mirelman ◽  
...  
Keyword(s):  
Older Adults ◽  
Postural Control ◽  
Dynamic Balance ◽  
Principal Component ◽  
Adverse Outcomes ◽  
Community Dwelling ◽  
Sensor Data ◽  
Quiet Standing ◽  
Mobility Disability ◽  
Community Dwelling Older Adults

Abstract Background Wearable sensors are increasingly employed to quantify diverse aspects of mobility. We developed novel tandem walking (TW) metrics, validated these measures using data from community-dwelling older adults, and evaluated their association with mobility disability and measures of gait and postural control. Methods Six hundred ninety-three community-dwelling older adults (age: 78.69 ± 7.12 years) wore a 3D accelerometer on their lower back while performing 3 tasks: TW, usual-walking, and quiet standing. Six new measures of TW were extracted from the sensor data along with the clinician’s conventional assessment of TW missteps (ie, trip other loss of balance in which recovery occurred to prevent a fall) and duration. Principal component analysis transformed the 6 new TW measures into 2 summary TW composite factors. Logistic regression models evaluated whether these TW factors were independently associated with mobility disability. Results Both TW factors were moderately related to the TW conventional measures (r &lt; 0.454, p &lt; .001) and were mildly correlated with usual-walking (r &lt; 0.195, p &lt; .001) and standing, postural control (r &lt; 0.119, p &lt; .001). The TW frequency composite factor (p = .008), but not TW complexity composite factor (p = .246), was independently associated with mobility disability in a model controlling for age, sex, body mass index, race, conventional measures of TW, and other measures of gait and postural control. Conclusions Sensor-derived TW metrics expand the characterization of gait and postural control and suggest that they reflect a relatively independent domain of mobility. Further work is needed to determine if these metrics improve risk stratification for other adverse outcomes (eg, falls and incident disability) in older adults.

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