The research described here was supported by grants from the National Institutes of Health, National Institute on Aging (U2CAG054397, P30 AG024978, P30 AG008017, R01 AG042191, R01 AG024059), Intel, the Foundation for the National Institutes of Health and the Robert Wood Johnson Foundation.

All participants provided written informed consent. Life Laboratory participants are asked to live their lives as they normally would to allow longitudinal observational study of their life activities and patterns for the rest of their lives. They may withdraw at any time if they wish. The study protocol was approved by the Oregon Health &#38;amp; Science University (OHSU) Institutional Review Board (Life Laboratory OHSU IRB #2765).
1. Preparation
<ol>
	<li>Prior to deployment, add all equip...

<ol>
	<li>Peetoom, K. K., Lexis, M. A., Joore, M., Dirksen, C. D., De Witte, L. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Literature+review+on+monitoring+technologies+and+their+outcomes+in+independently+living+elderly+people.">Literature review on monitoring technologies and their outcomes in independently living elderly people.</a> Disabil Rehabil Assist Technol. 10 (4), 271-294 (2015).</li><li>Liu, L., Stroulia, E., Nikolaidis, I., Miguel-Cruz, A., Rios Rincon, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smart+homes+and+home+health+monitoring+technologies+for+older+adults:+A+systematic+review.">Smart homes and home health monitoring technologies for older adults: A systematic review.</a> Int J Med Inform. 91, 44-59 (2016).</li><li>Kim, K. I., Gollamudi, S. S., Steinhubl, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Digital+technology+to+enable+aging+in+place.">Digital technology to enable aging in place.</a> Exp Gerontol. 88, 25-31 (2017).</li><li>Kaye, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Making+pervasive+computing+technology+pervasive+for+health+&amp;+wellness+in+aging.">Making pervasive computing technology pervasive for health &amp; wellness in aging.</a> Public Policy &amp; Aging Report. 27 (2), 53-61 (2017).</li><li>Wild, K., Boise, L., Lundell, J., Foucek, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unobtrusive+in-home+monitoring+of+cognitive+and+physical+health:+Reactions+and+perceptions+of+older+adults.">Unobtrusive in-home monitoring of cognitive and physical health: Reactions and perceptions of older adults.</a> Journal of Applied Gerontology. 27 (2), 181-200 (2008).</li><li>Wild, K., Boise, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> In-Home Monitoring Technologies: Perspectives and Priorities of Older Adults. , (2012).</li><li>Boise, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Willingness+of+older+adults+to+share+data+and+privacy+concerns+after+exposure+to+unobtrusive+in-home+monitoring.">Willingness of older adults to share data and privacy concerns after exposure to unobtrusive in-home monitoring.</a> Gerontechnology: international journal on the fundamental aspects of technology to serve the ageing society. 11 (3), 428 (2013).</li><li>Hayes, T. L., Hagler, S., Austin, D., Kaye, J., Pavel, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Engineering+in+Medicine+and+Biology+Society,+2009.+EMBC+2009.">Engineering in Medicine and Biology Society, 2009. EMBC 2009.</a> Annual International Conference of the IEEE. , 7248-7251 (2009).</li><li>Kaye, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One+walk+a+year+to+1000+within+a+year:+Continuous+in-home+unobtrusive+gait+assessment+of+older+adults.">One walk a year to 1000 within a year: Continuous in-home unobtrusive gait assessment of older adults.</a> Gait &amp; posture. 35 (2), 197-202 (2012).</li><li>Petersen, J., Austin, D., Mattek, N., Kaye, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time+out-of-home+and+cognitive,+physical,+and+emotional+wellbeing+of+older+adults:+A+longitudinal+mixed+effects+model.">Time out-of-home and cognitive, physical, and emotional wellbeing of older adults: A longitudinal mixed effects model.</a> PloS one. 10 (10), 0139643 (2015).</li><li>Hayes, T. L., Riley, T., Mattek, N., Pavel, M., Kaye, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sleep+habits+in+mild+cognitive+impairment.">Sleep habits in mild cognitive impairment.</a> Alzheimer disease and associated disorders. 28 (2), 145 (2014).</li><li>Hayes, T. L., Larimer, N., Adami, A., Kaye, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Medication+adherence+in+healthy+elders:+small+cognitive+changes+make+a+big+difference.">Medication adherence in healthy elders: small cognitive changes make a big difference.</a> Journal of aging and health. , (2009).</li><li>Kaye, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Unobtrusive+measurement+of+daily+computer+use+to+detect+mild+cognitive+impairment.">Unobtrusive measurement of daily computer use to detect mild cognitive impairment.</a> Alzheimer's &amp; Dementia. 10 (1), 10-17 (2014).</li><li>Seelye, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer+mouse+movement+patterns:+A+potential+marker+of+mild+cognitive+impairment.">Computer mouse movement patterns: A potential marker of mild cognitive impairment.</a> Alzheimer's &amp; Dementia: Diagnosis, Assessment &amp; Disease Monitoring. 1 (4), 472-480 (2015).</li><li>Seelye, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Embedded+online+questionnaire+measures+are+sensitive+to+identifying+mild+cognitive+impairment.">Embedded online questionnaire measures are sensitive to identifying mild cognitive impairment.</a> Alzheimer Dis Assoc Disord. 30 (2), 152-159 (2016).</li><li>Dodge, H., Mattek, N., Austin, D., Hayes, T., Kaye, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-home+walking+speeds+and+variability+trajectories+associated+with+mild+cognitive+impairment.">In-home walking speeds and variability trajectories associated with mild cognitive impairment.</a> Neurology. 78 (24), 1946-1952 (2012).</li><li>Austin, J., Klein, K., Mattek, N., Kaye, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Variability+in+medication+taking+is+associated+with+cognitive+performance+in+nondemented+older+adults.">Variability in medication taking is associated with cognitive performance in nondemented older adults.</a> Alzheimer's &amp; Dementia: Diagnosis, Assessment &amp; Disease Monitoring. 6, 210-213 (2017).</li><li>Austin, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+smart-home+system+to+unobtrusively+and+continuously+assess+loneliness+in+older+adults.">A smart-home system to unobtrusively and continuously assess loneliness in older adults.</a> IEEE Journal of Translational Engineering in Health and Medicine. 4, 1-11 (2016).</li><li>Dodge, H. H., Mattek, N. C., Austin, D., Hayes, T. L., Kaye, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In-home+walking+speeds+and+variability+trajectories+associated+with+Mild+Cognitive+Impairment.">In-home walking speeds and variability trajectories associated with Mild Cognitive Impairment.</a> Neurology. 78 (24), 1946-1952 (2012).</li><li>Kaye, J. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intelligent+systems+for+assessing+aging+changes:+home-based,+unobtrusive,+and+continuous+assessment+of+aging.">Intelligent systems for assessing aging changes: home-based, unobtrusive, and continuous assessment of aging.</a> The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 66, 180-190 (2011).</li><li>Wang, Z., Yang, Z., Dong, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Review+of+Wearable+Technologies+for+Elderly+Care+that+Can+Accurately+Track+Indoor+Position,+Recognize+Physical+Activities+and+Monitor+Vital+Signs+in+Real+Time.">A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time.</a> Sensors. 17 (341), (2017).</li><li>Skubic, M., Alexander, G., Popescu, M., Rantz, M., Keller, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+smart+home+application+to+eldercare:+Current+status+and+lessons.">A smart home application to eldercare: Current status and lessons.</a> Technol. Health Care. 17 (3), 183-201 (2009).</li><li>Campbell, I. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Engineering+in+Medicine+and+Biology+Society,+EMBC,+2011.">Engineering in Medicine and Biology Society, EMBC, 2011.</a> Annual International Conference of the IEEE. , 6793-6796 (2011).</li><li>Austin, D., Cross, R. M., Hayes, T., Kaye, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regularity+and+predictability+of+human+mobility+in+personal+space.">Regularity and predictability of human mobility in personal space.</a> PloS one. 9 (2), 90256 (2014).</li><li>Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R., Kupfer, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Pittsburgh+Sleep+Quality+Index:+a+new+instrument+for+psychiatric+practice+and+research.">The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research.</a> Psychiatry research. 28 (2), 193-213 (1989).</li><li>Cummings, J. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Neuropsychiatric+Inventory+Assessing+psychopathology+in+dementia+patients.">The Neuropsychiatric Inventory Assessing psychopathology in dementia patients.</a> Neurology. 48, 10-16 (1997).</li><li>Teng, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Utility+of+the+Functional+Activities+Questionnaire+for+distinguishing+mild+cognitive+impairment+from+very+mild+Alzheimer's+disease.">Utility of the Functional Activities Questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer's disease.</a> Alzheimer disease and associated disorders. 24 (4), 348 (2010).</li><li>Wild, K. V., Mattek, N., Austin, D., Kaye, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term="Are+You+Sure?"+Lapses+in+Self-Reported+Activities+Among+Healthy+Older+Adults+Reporting+Online.">"Are You Sure?" Lapses in Self-Reported Activities Among Healthy Older Adults Reporting Online.</a> Journal of Applied Gerontology. , (2015).</li></ol>

We have described a basic system or platform enabling home- and community-based remote sensing and reporting of salient health and wellbeing measures on a continuous basis. The system is intended to be used primarily in research at this time.
Where possible, the system uses open source tools and sensors or devices taking advantage of available APIs and software development kits (SDK). The system is designed to be technology &#34;agnostic&#34; such that a wide variety of sensors or devices can ...

Prevailing clinical research is fraught with limitations in the reliability and validity of data captured because of inherent shortcomings of assessment methodology. Interviews are constrained by the times when the clinician and patient can coordinate schedules. Time allotted for examinations is limited by what the volunteer can reasonably be asked to do in a single session. These brief, widely spaced sessions - even if augmented by occasional telephone calls or Internet queries - severely limit the potential to detect meaningful change in function or wellbeing over time. Current test sessions are largely composed of requests for information that can be difficult to recall and verify (e.g., &#34;do you remember to take your medications?&#34;) or performance of artificial tasks (e.g., &#34;stand up and sit down as fast as you can&#34;; &#34;remember these ten words&#34;). The assessments are often designed to restrict test-to-test variability when in fact variability in performance itself may be a key diagnostic feature. Further, these brief biopsies of time are conducted under artificial conditions rather than within the normal flow of day-to-day life. Therefore, they are of limited ecological validity. Finally, the current paradigm inherently cannot provide direct linkage of interdependent key events or outcomes (e.g., sleep, socialization, physical activity) because the data are not time-stamped other than as recalled.
An approach to overcoming these shortcomings lies in the development of systems that can be embedded in home or community that take advantage of advances in pervasive computing and sensing technology, wireless communications, and high frequency multi-domain data analytics. The technology and experience in this area are growing and a number of systems have been developed, but have been limited in deployment, features or longitudinal experience1,2,3,4. In this manuscript, we describe a protocol developed as a means to provide real-time, continuous and longitudinal home-based assessment of health-relevant data to improve upon the limitations of the current health assessment paradigm. Oregon Center for Aging &#38;amp; Technology (ORCATECH) has developed a home-based system based on pervasive computing and sensing technology to provide continuous, real-time assessment of health-relevant activity and behavior. Bringing the assessment into home to enable largely unobtrusive and continuous monitoring of real-world activity substantially overcomes current limitations. First, since the core system is embedded in the life-space of the participants as part of their ambient environment, it is inherently convenient. Assessments requiring discrete responses can be collected when a person is most at ease and, in the case of passive collection methods, as frequently as necessary without burdening a participant. Second, being in the person&#39;s normal life-space affords the opportunity to collect data that are immediately ecologically relevant, not simply testing contrived measures of function, but everyday cognition. For example, prospective memory failure, a common complaint difficult to naturalistically test in the clinic, can be assessed at home by automatic daily tracking of medication taking behavior, thus tapping both everyday cognition, as well as a key performance metric known to be sensitive to cognitive change. Third, because the data are digital and time-stamped, measurement of multiple interrelated measures aligned in time is facilitated. For example, time on the telephone and time out of home (measures of social engagement or withdrawal), computer use (measuring initiation, psychomotor activity and cognitive function), and other measures that have been shown to change with functional decline (sleep behavior, weight, walking speed) can add to the sensitivity of the sensor net to distinguish subtle changes that may not be otherwise apparent. Importantly, the effect of health and life events on cognition and function (e.g., weekly reports of pain, medication change, low mood) can also be linked to this data stream as they occur. Finally, conventional testing and queries can be presented via computer or related interfaces (e.g., tablet, smartphone), providing unparalleled opportunities to simultaneously compare legacy test performance to novel digitally derived measures from the same tests, such as response or pause times, learning curves and intra-test variability. This new approach thus transforms current assessment to be more convenient, unobtrusive, continuous, multi-domain and naturalistic. Ultimately, the basic platform of in-home sensor based assessment technology and methodology, provides a system that can be tuned and scaled to address a wide range of specific research questions related to health and wellbeing with noted advantages over the current accepted practice of infrequent clinic or telephone-based assessments.
The following protocol outlines the process of deploying this platform for unobtrusive in-home behavioral and health-related data collection. In developing this platform, a key goal has been to provide a basic suite of assessment functions that can provide the data necessary to infer both general domains of health and well-being (physical, cognitive, social, emotive), as well as more specific behaviors (e.g., medication taking, walking, sleep-related activities, physiologic activity). The development of the platform has been guided by several principles including using the most passive unobtrusive sensing approaches, minimizing direct user engagement with technology, being technology &#39;agnostic&#39; (i.e., employing the best devices or technical solutions rather than requiring a particular approach or product), being durable (for long-term assessment) and scalable, and minimizing hands-on maintenance.
The platform described has evolved over the past twelve years, importantly informed by a range of end-users, from &#34;digitally naive&#34; to early adopters. Periodic surveys and focus groups have been key to informing this development5,6,7. Hundreds of volunteers have allowed the systems to be continuously deployed in their homes for up to eleven years with iterative modifications being introduced based on the advances in technology, new functional capacities requested by the research community, and the key constant input of individuals living in home where the technology has been deployed. Collectively, these volunteers have formed a &#34;living&#34; laboratory in the community which we call the &#34;Life Laboratory&#34; where their homes and the continuous data collected throughout the day provide a unique level of details about health, activity, and the life course.
A basic platform of sensing technology forms the backbone of the overall system for capturing continuous home-based data. The elements of this platform are described subsequently. The core platform is modified (elements may be added or removed) based on the information obtained during the process of gathering user attitudes, and beliefs and outcome measures of interest for the study using the research platform. Because data communication protocols are standardized, the system has been designed to allow any device that follows these protocols to be incorporated into the network.
The basic platform described here is based on the use case of volunteers in the Life Laboratory (LL) who consent to have the platform deployed within their homes to collect naturalistic activity and behavior data of their normal life activities for many years (longest current continuous deployment = 11 years).
The hub computer and Ethernet/WiFi connection allow data collection from system devices and transmission back to secure servers at ORCATECH without participant interference. The hub computer is configured to the specific participant and home set-up upon system installation using a laptop or tablet and a control panel that connects to a centralized digital participant management system. Additional data collection devices (such as sensors, MedTracker, and scale) can be configured by communicating with the hub computer in the same way.
The ORCATECH Console and Remote Technology Management System is a custom digital technology and data management system called &#34;Console&#34; that allows participant home technology configuration and system set-up, as well as ongoing remote technology management of homes including secure data collection and monitoring. In addition, to facilitate deployment of the system in the community where each home may have a unique layout, a graphing tool based on a tablet interface is used to automatically record where various sensors are located and their valid physical adjacencies to other sensors (Figure 2). This is important for reference during remote monitoring of the system at the home level.
Passive infrared (PIR) Motion Sensors are digitally assigned to a given home during system installation, communicating with the hub computer via a wireless USB Dongle. One sensor is placed per room to sense motion within the room and participant transitions from room to room. A straight &#34;Sensor Line&#34; of four sensors is placed on the ceiling of a hallway or other area where the participant walks regularly at a consistent pace. This sensor line allows unobtrusive gathering of walking speed many times per day. Other metrics can be derived from these activity sensors such as dwell time or number of room transitions. Door contact sensors are placed around the home at all external doors to detect participants&#39; coming and going from the home, and on the refrigerator to determine general frequency of food access.
On-Line Weekly Health and Activity Self-Reports are required to make optimal sense of the data from the passive system of collection devices. These data are critical to the analysis of participant report of events in the home relative to the sensor collected data. The online weekly self-report survey can be completed on any computing device (e.g., laptop, tablet, smartphone) with an Internet connection to query participants about trips out of the home, visitors in the home, health changes, space changes within the home, loneliness, depression, and pain level. Weekly data collection relies on a relatively short window of recollection, which provides much higher resolution of data and likelihood of accuracy than, for instance, annual or semi-annual check-ups. Furthermore, this self-report process also allows investigators to examine passive indicators of potential cognitive impairment, such as variation in the time to complete the survey, variation in number of clicks, increased difficulties reporting accurate dates, or impairment markers in free text responses. As part of the basic platform, we install a seven day electronic pill box that records whether or not the designated day&#39;s compartment was opened and the time(s) that it was opened each day. This provides information about medication adherence as well as a potential indication of cognitive decline if consistency of medication-taking decreases.
A wireless digital bioimpedence scale that also collects pulse, body composition metrics, pulse wave velocity, environmental temperature, and ambient carbon dioxide level is installed in the bathroom, providing data on participants&#39; daily weight. This data can then be correlated with other reported events (e.g., health status, medications), as well as other passive indicators of behavior, such as protocol adherence and frequency of use over time.
In cases where our participants drive, we install a driving sensor in their vehicles. This sensor provides information about driving habits such as frequency, timing, duration, and distance of trips, as well as frequency of hard stops or hard accelerations.
A wrist worn wearable device collects physical activity data both in and out of the home. Several brands and models of wearables have been used in Life Laboratory homes.
Depending on the project, an investigator using the ORCATECH platform may choose to supplement the basic sensor set with additional data-collection components. Examples tested in the past include a phone sensor to monitor socialization through landline phone activity, the development and implementation of a digital balance-board for balance testing, a tablet with periodic cognitive tasks for the participant to complete in their own home, and an automated texting system to evaluate the efficacy of medication reminders via phone.
To handle the diverse data generated by the ORCATECH Life Lab, a tailored information and data system is used for collecting, annotating, maintaining, and analyzing copious activity and health data. ORCATECH has developed a custom system for participant management, self-report data collection and processing, and continuous data collection from all system devices and sensors. The system relies on a distributed NoSQL Cassandra server cluster to store the sensor data and a lambda architecture using Kafka and Spark which allows our data processing capabilities to move closer to real-time processing. Using a REST API, data is transferred into standard data analysis platforms and statistical software programs for data analysis.

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			<td height="42" style="height:42px;width:216px;">Raspberry Pi 3 Model B</td>
			<td style="width:123px;">Raspberry Pi Foundation</td>
			<td style="width:167px;">Raspberry Pi 3 Model B</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3041-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Door/Window Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3011-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Curtain Motion Sensor</td>
			<td style="width:123px;">NYCE Sensors Inc</td>
			<td style="width:167px;">NCZ-3045-HA</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">iSort</td>
			<td style="width:123px;">TimerCap</td>
			<td style="width:167px;">iSort</td>
			<td></td>
		</tr>
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			<td height="42" style="height:42px;width:216px;">Home Stealth USB Phone Recorder</td>
			<td style="width:123px;">Fiho</td>
			<td>Fi3001B</td>
			<td></td>
		</tr>
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			<td height="21" style="height:21px;width:216px;">Automatic Pro</td>
			<td style="width:123px;">Automatic</td>
			<td style="width:167px;">AUT-350C</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Body Cardio Scale</td>
			<td style="width:123px;">Nokia</td>
			<td>WBS04</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Activite/Steel Activity Monitor</td>
			<td style="width:123px;">Nokia</td>
			<td style="width:167px;">HWA01 STEEL</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Alta 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB406</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">Charge 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB407</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Flex 2</td>
			<td style="width:123px;">Fitbit</td>
			<td style="width:167px;">FB403</td>
			<td></td>
		</tr>
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			<td height="21" style="height:21px;width:216px;">Zigbee USB Stick</td>
			<td style="width:123px;">Silicon Labs</td>
			<td style="width:167px;">ETRX3USB</td>
			<td></td>
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			<td height="21" style="height:21px;width:216px;">WorkTime</td>
			<td style="width:123px;">Nestersoft</td>
			<td style="width:167px;">WorkTime Corporate</td>
			<td></td>
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methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the daily life of older adults over extended periods of time. The technology is aggregated into a system that incorporates the principles of being minimally obtrusive, while generating secure, privacy protected, continuous objective data in real-world (home-based) settings for months to years. The system includes passive infrared presence sensors placed throughout the home, door contact sensors installed on exterior doors, connected physiological monitoring devices (such as scales), medication boxes, and wearable actigraphs. Driving sensors are also installed in participants' cars and computer (PC, tablet or smartphone) use is tracked. Data is annotated via frequent online self-report options that provide vital information with regard to the data that is difficult to infer via sensors such as internal states (e.g., pain, mood, loneliness), as well as data referent to activity pattern interpretation (e.g., visitors, rearranged furniture). Algorithms have been developed using the data obtained to identify functional domains key to health or disease activity monitoring, including mobility (e.g., room transitions, steps, gait speed), physiologic function (e.g., weight, body mass index, pulse), sleep behaviors (e.g., sleep time, trips to the bathroom at night), medication adherence (e.g., missed doses), social engagement (e.g., time spent out of home, time couples spend together), and cognitive function (e.g., time on computer, mouse movements, characteristics of online form completion, driving ability). Change detection of these functions provides a sensitive marker for the application in health surveillance of acute illnesses (e.g., viral epidemic) to the early detection of prodromal dementia syndromes. The system is particularly suitable for monitoring the efficacy of clinical interventions in natural history studies of geriatric syndromes and in clinical trials.

The ORCATECH suite of technology makes it possible to collect a uniquely rich data set about the life patterns of people as they go about their usual activities. The sensor system allows unobtrusive and continuous monitoring of the volunteers in their own homes. The system has been used in dozens of studies involving hundreds of volunteers in research examining key domains of health and function such as walking speed and mobility, medication-taking behavior, mood, time in or out of home, ...

Watch this Scientific Journal Video about Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data at JoVE.com

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Unobtrusive sensors and pervasive computing technology incorporated into the daily home life of older adults enables meaningful health and activity changes to be recorded continuously for months to years, providing ecologically valid, high frequency, multi-domain data for research or clinical use.

An end-to-end suite of technologies has been established for the unobtrusive and continuous monitoring of health and activity changes occurring in the ...

This methodology can provide real time, objective, continuous and ecologically valid data for researchers engaged in longitudinal health studies and clinical trials. The advantages of this approach are that it while it requires little to no effort on the part of the research participants, it enables the capture of acute data as well as subtle changing events over time. It is adaptable for multiple-use cases.This methodology has been substantially deployed to access outcomes most related to functional changes important for research in high-impact chronic conditions such as disorders of mobility or dementia. The protocols intend to be extensible in application of research to collect digital, activity and health data across many different research domains. Prior to deployment, add all the equipment to the console inventory system online.Assign a name to each device or sensor, along with its serial number and MAC address, allowing it to be stored within the console inventory system. Place a QR or quick response code tag on each piece of equipment enabling specific assignments of home location for the sensors and devices when deployed in the home. Before deployment, all sensors and devices that are to be installed will be assigned to the home by scanning the QR code on the sensor or device.This will bring up a website that allows sensor or device assignment to that specific home. Install the hub computer with an SD card which contains the hub configuration management tool. Pack all of the now inventoried sensors and equipment and hub computer with installed SD card into an installation kit for home deployment.Verify that the participant's home has an Internet service provider. Set up the hub computer by inserting the wireless dongle and the primary zigbee coordinator dongle into the hub computer. Insert the ethernet cable into the hub computer in order to connect to the home's Internet connection.And lastly, plug the hub computer's power cord into a centrally-located room's power socket. Connect and Internet-enabled device to the hub computer's wireless network. Open a web browser in order to access the local Control Panel website.The Control Panel website will show the status of the hub computer and once the installation is complete, any sensors installed in the home. Run the software configuration tool ensuring the appropriate software is installed. Do this by navigating to the Control Panel and running update.Navigate to the Control Panel to check that the hub computer is communicating with the main servers. Make sure that the services that allow data to be collected from each installed device and sensor are up and running. Add the sensors to the home, beginning with the motion sensors.Start by opening the Sensor Placement website from the Control Panel. In the Sensor Placement website create a virtual floor plan of the home by adding the areas that represent rooms in the home. Adding sensors.And creating linkages between areas and sensors. Add each motion sensor to the personal area network or PAN using a toggle switch located on the sensor page within the Control Panel. At the same time, press a button located near the motion sensor's battery 10 times.This two-part process activates the sensor. Using the virtual floor plan as a guide, place the motion sensors in their corresponding rooms in the home. The motion sensors should be placed on the walls at head height using tape.Continuing to use the virtual floor plan as a guide install a row of four restricted field sensors on the ceiling of a straight walkway such as a hallway in order to capture walking speed. Space these walking speed sensors two feet or 61 centimeters apart. Record the exact distance between the restricted field sensors in the Sensor Placement website.Install door sensors on each egress door again using the virtual floor plan to indicate their physical location. Add the pillbox to the pan by navigating to the devices column within the Control Panel. Turn on the device, because the pillbox connects to the hub computer.Ensure it is close enough to the hub computer for the signal to be detected. Set up the scale by navigating to the Control Panel opening the left navigation menu, choosing the expand device tab and clicking on the name of the scale. On the scale hold the side button for 10 seconds.The scale will turn on displaying a start up message. Once the scale appears on the Control Panel, click the setup gear button to start the setup process. Once the setup gear button turns green the scale will continue setting up.Place the scale in a location that has a flat, solid surface and is easily accessible to the participant. Enter the participant's height and weight when prompted in the Control Panel. If the participant does not have a pacemaker, toggle the pacemaker button in the Control Panel notifying the scale that it can collect certain data.Once the scale is set up, have the participant weigh themselves, confirming the scale is recording the initial weight that has been entered into the Control Panel. Set up the wrist-worn wearable device by navigating to the wearables setup page within the Control Panel and pressing the reset button located on the back side of the device 10 times. After the wearable device shows up in the list of wearable devices on the Control Panel, click the setup button in order to start the setup process.Once the wearables account is setup, calibrate the time by using the wheel on the setup page. To finalize the setup, synchronize the wearable by clicking the sync button in the Control Panel. This confirms that device is connecting properly and that the time is set to the same time as the hub computer.Different devices may require different procedures depending on the manufacturer. Additional sensors and devices may be integrated and deployed into the technology platform in the future. Once all the devices and sensors have been installed in the home confirm that the hub computer is working properly.Review if data is streaming from each device by navigating to the data collection page on the Control Panel. Walk near the sensors installed in each room of the home in order to see if each sensor is collecting data about recent movements. Check the motion sensors by viewing the live chart of motion sensor data.Check if the pillbox is transmitting data by opening and closing at least one of the compartment doors a few times. Review the data collection page on the Control Panel to see if this recent activity was measured and collected. In order to check if the scale is transmitting data, weigh yourself.Review if the scale is properly synced by navigating to the synced column within the devices page in the Control Panel. Check if the wearable device is properly synced and is transmitting data by navigating to the synced column within the wearables devices page in the Control Panel. The plot shows the trajectories of change in walking speed variability captured during multiple transits through home sensor lines.Variability was defined as the coefficient of variation in a cohort of older adults followed in their homes for over three years. Latent trajectory models identified four distinct groups of walking speed variability. Distinct patterns of change in walking variability were seen more commonly in early and later phases of mild cognitive impairment.This suggests that walking speed variability can be a marker for MCI progression. This spiral plot demonstrates the display of six months of continuous home activity data for a single volunteer. Data are plotted as a 24-hour clock with midnight at the top and noon at the bottom.Each day forms one circle. The solid blue circles mark one month boundaries. Colored points indicate sensor activity by room location where red is the bathroom, green is the bedroom, pink is the kitchen and blue is the living room.Consistent patterns of behavior are observed. Bedtime is usually around 2200 hours as visualized by sensor activity in the bedroom following sensor activity in the bathroom. The participants are up once most nights between 2 a.m.and 4 a.m. as evidenced by sensor activity in the bedroom and bathroom during that time. Rise time is 0600 hours as observed through sudden sensor activity at that time.Also apparent is a visit from a housekeeper at 5 p.m. every two weeks evidenced by the increased movement in the kitchen, followed by other rooms during that time. Finally, note the two week period during the fourth month when the volunteer did not leave the apartment during a community congregation restriction due to a flu epidemic.Once mastered, this technique can be utilized in less than 90 minutes, depending on the size of the home and the number of devices or senors utilized. The system is designed to allow ongoing or future studies to update or deploy new sensors or devices such as other wearables, bed activity sensing systems or home environmental condition sensors. The system has been deployed to hundreds of homes and continues to evolve as technologies advance.After watching this video, you should have a good understanding about how to deploy and integrate into your research pervasive computing technologies and sensors that will provide a more real time, continuous and ecologically valid set of data for your research.

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8.1K visualizações.  Oregon Health & Science University. This methodology can provide real time, objective, continuous and ecologically valid data for researchers engaged in longitudinal health studies and clinical trials. The advantages of this approach are that it while it requires little to no effort on the part of the research participants, it enables the capture of acute data as well as subtle changing events over time. It is adaptable for multiple-use cases.This methodology has been substantially deployed to access outcomes most relate...