Name: methodology for establishing a community-wide life laboratory for capturing unobtrusive and continuous remote activity and health data
Uploaded: 2018-07-27T12:30:00.000+00:00
Duration: 11 min 21 s
Description: 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

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 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.</li>
	<li>Place a QR code tag on each piece of equipment, enabling specific assignments of home location for the sensors and devices when deployed in home.</li>
	<li>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.</li>
	<li>Install the hub computer with a SD card which contains the hub configuration management tool.</li>
	<li>Pack all the now inventoried sensors and equipment, hub computer with installed SD card into an installation kit (box) for home deployment.</li>
	<li>Verify that the participant&#39;s home has an Internet service provider.</li>
</ol>
2. Home Deployment
<ol>
	<li>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. Lastly, plug the hub computer&#39;s power cord into a centrally located room&#39;s power socket and connect the Ethernet cable to the home&#39;s Internet connection. 
	Note: The configuration management tool will ensure that it is using the most current version of the software.</li>
	<li>Connect an Internet enabled device (laptop, tablet, cell phone) to the hub computer&#39;swireless network to access the local control panel website. The control panel website will show the status of the hub computer, as well as any sensors installed in the home (Figure 3).</li>
	<li>Run the software configuration tool, ensuring the appropriate software is installed. Do this by navigating to the control panel and running Update.</li>
	<li>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.</li>
	<li>Add the sensors to the home, beginning with the motion sensors. Start by opening the sensor placement website from the control panel. 
	NOTE: If the home requires more than 16 motion sensors, connect a router dongle into the hub computer and add it to the home or personal area network, also called the &#34;PAN.&#34; Once the extension dongles (if needed) are added to the PAN, remove them from the hub computer and plug them into outlets spread throughout the home, creating a network around the home that will send the motion sensor data to the hub computer.</li>
	<li>In the sensor placement website, create a virtual floor plan of the home, including all the rooms and egress doors. Make sure to select the Sensor Line as one of the areas that is added to the floor plan. Add virtual representations of sensors to the floor plan. Lastly, link the virtual representations of home areas to other each other - in a way that reflects the physical layout of the home - and to the virtual representations of the sensors.</li>
	<li>Add each successive sensor to the PAN - known as the Personal Area Network - by using the sensor placement tool and physically pressing a button near the sensor&#39;s battery. Then, start attaching each sensor to the room or area in the home that is represented in the virtual floor plan.</li>
	<li>Continue attaching the physical sensors to the walls of the home. Place each wall sensor at head height in each room (kitchen, bedrooms, bathrooms, living rooms) ensuring that the sensor only captures the activity in that room and does not pick up the activity from another area (for example, avoid someone walking down a hallway being picked up by the sensor in a room next to the hallway). 
	NOTE: The sensor placement tool allows you to identify and create pathways between rooms.</li>
	<li>Install a row of four restricted field (ceiling) sensors in a straight walkway (hallways or other areas where the participants must walk past each of the four sensors without a change in pace) on the ceiling to capture walking speed.
	<ol>
		<li>Space these walking speed sensors 61 cm (2 feet) apart.</li>
		<li>Record the exact distance between the restricted field sensors in the sensor placement website.</li>
	</ol>
	</li>
	<li>Install door sensors on each egress door, again using the floor plan on the sensor placement website to indicate their physical location.</li>
	<li>Add the pillbox to the PAN, confirming that the device has been assigned to the home inventory. Then activate the device by opening one of its lids. Because the pillbox communicates with the hub computer, ensure it is close enough to the hub computer for its signal to be detected. 
	Note: The pillbox is frequently kept in the kitchen or bathroom based on participant preference.</li>
	<li>To setup the scale, navigate to the scale page, located within the Devices tab in the control panel.
	<ol>
		<li>On the scale, press the side power button for 10 seconds. The scale should display a confirmation message.</li>
		<li>Once the scale shows up in the list of devices, click the Setup button on the right side of the control panel to start the setup process.</li>
		<li>Enter the participant&#39;s height and weight when prompted in the control panel.</li>
		<li>If the participant does not have a pacemaker, toggle the Pacemaker button in the control panel notifying the scale that it can collect bioimpedence data.</li>
		<li>Place the scale in a location that has a flat, solid surface easily accessible to the participant (typically in a bathroom).</li>
		<li>Have the participant weigh themselves, confirming the scale is recording their initial weight which is entered into the control panel.</li>
	</ol>
	</li>
	<li>Set up the wrist worn wearable device by opening the wearable control panel setup page and pressing the reset button located on the back side of the device ten times.
	<ol>
		<li>After the device shows up in the list of devices in the control panel and click the Setup button on the right side of the control panel to start the setup process.</li>
		<li>Once the account is set up, calibrate the time by using the wheel tool on the setup page.</li>
		<li>Finalize the setup by synchronizing the wearable with the hub computer. Click the Sync button in the control panel to confirm that the device is connecting properly and the time is set to the same time as the hub computer.</li>
		<li>Indicate in Console on which wrist the wearable is intended to be worn by the participant. 
		NOTE: Different devices may require different procedures depending on the manufacturer. Additional sensors and devices may also be deployed and integrated into the data stream such as computer use software and driving sensors. Procedures for adding these are given next.</li>
	</ol>
	</li>
	<li>Install the commercial computer use monitoring software on the participant&#39;s computer and record their e-mail address. These e-mail addresses are used to send and receive weekly online health and activity surveys.
	<ol>
		<li>Verify that the participant&#39;s computer operating system is compatible with the commercial computer use monitoring software.</li>
		<li>Install the software on the participant&#39;s computer using the installation program hosted on a USB flash drive.</li>
		<li>Verify that the software is operational on the computer by opening the Task Manager and checking that the software is in the list of Applications.</li>
		<li>In the Console inventory system, associate the software program with the participant&#39;s profile. 
		NOTE: See the Table of Materials for the specific computer use software used (other commercially available monitoring software can be substituted).</li>
	</ol>
	</li>
	<li>Setup a driving sensor for participants
	<ol>
		<li>Verify that the participant&#39;s car was made after 1996 and that the car is supported by the driving sensor device software.</li>
		<li>Install the driving monitoring device&#39;s app on a mobile device and use the app to setup the adapter.</li>
		<li>With the car turned off, plug the adapter into the car&#39;s on-board diagnostic (ODB) port.</li>
		<li>Wait for the app to recognize and connect to the adapter. This should take 2 - 4 min.</li>
		<li>Insert the car key into the ignition. (If the car has keyless ignition, press the car&#39;s start button). Turn the key to the position where it switches on the electric power without starting the engine.</li>
		<li>Wait for the app to finish setting up the adapter.</li>
		<li>In the Console inventory system, add the participant&#39;s account information from the app in order to allow the adapter&#39;s data to be transferred to the ORCATECH servers using the commercial software&#39;s application programming interface (API). 
		NOTE: See the Table of Materials for the specific driving monitoring device used.</li>
	</ol>
	</li>
</ol>
3. System Confirmation
<ol>
	<li>Once all devices are in their final places in the home, confirm that the hub computer is working properly by navigating to the control panel. Check that the hub computer can communicate with the main servers to transfer data and the services to collect the data for each device type are running.</li>
	<li>Review if data is streaming from each device by navigating to the data collection page on the control panel.</li>
	<li>Walk near the motion sensors installed in each room in the home in order to confirm each sensor is collecting data about recent movements. Check the motion sensors by viewing the live graph of the motion sensor data activated by walking through the home.</li>
	<li>Check the pillbox by opening and closing each of the compartment doors of the pillbox a few times. Review the data collection page on the control panel to see if this recent activity was measured and collected.</li>
	<li>Check the scale by weighing yourself or the participant. Confirm this data is properly synched and transmitted by navigating to the Synched column within the scale Devices page in the control panel.</li>
	<li>Check if the wearable device is properly synched and transmitting data by navigating to the Synched column within the wearable Devices page in the control panel.</li>
</ol>

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The authors have nothing to disclose.

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.

<table><tbody><tr><td>Raspberry Pi 3 Model B</td><td>Raspberry Pi Foundation</td><td>Raspberry Pi 3 Model B</td><td></td></tr><tr><td>Motion Sensor</td><td>NYCE Sensors Inc</td><td>NCZ-3041-HA</td><td></td></tr><tr><td>Door/Window Sensor</td><td>NYCE Sensors Inc</td><td>NCZ-3011-HA</td><td></td></tr><tr><td>Curtain Motion Sensor</td><td>NYCE Sensors Inc</td><td>NCZ-3045-HA</td><td></td></tr><tr><td>iSort</td><td>TimerCap</td><td>iSort</td><td></td></tr><tr><td>Home Stealth USB Phone Recorder</td><td>Fiho</td><td>Fi3001B</td><td></td></tr><tr><td>Automatic Pro</td><td>Automatic</td><td>AUT-350C</td><td></td></tr><tr><td>Body Cardio Scale</td><td>Nokia</td><td>WBS04</td><td></td></tr><tr><td>Activite/Steel Activity Monitor</td><td>Nokia</td><td>HWA01 STEEL</td><td></td></tr><tr><td>Alta 2</td><td>Fitbit</td><td>FB406</td><td></td></tr><tr><td>Charge 2</td><td>Fitbit</td><td>FB407</td><td></td></tr><tr><td>Flex 2</td><td>Fitbit</td><td>FB403</td><td></td></tr><tr><td>Zigbee USB Stick</td><td>Silicon Labs</td><td>ETRX3USB</td><td></td></tr><tr><td>WorkTime</td><td>Nestersoft</td><td>WorkTime Corporate</td><td></td></tr></tbody></table>

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 ...

methodology-for-establishing-community-wide-life-laboratory-for

Universidad de Cartagena UDEC

Research

JoVE Journal

Behavior

8.1K Views.  Oregon Health & Science University. 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. 

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

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that attempt to recognize user activities. Participants performed a pre-determined set of daily living actions within a continuous test circuit that included mobility activities (walking, standing, sitting, lying, ascending/descending stairs), daily living tasks (combing hair, brushing teeth, preparing food, eating, washing dishes), and subtle environment changes (opening doors, using an elevator, walking on inclines, traversing staircase landings, walking outdoors). 
To evaluate WMMS performance on this circuit, fifteen able-bodied participants completed the tasks while wearing a smartphone at their right front pelvis. The WMMS application used smartphone accelerometer and gyroscope signals to classify activity states. A gold standard comparison data set was created by video-recording each trial and manually logging activity onset times. Gold standard and WMMS data were analyzed offline. Three classification sets were calculated for each circuit: (i) mobility or immobility, ii) sit, stand, lie, or walking, and (iii) sit, stand, lie, walking, climbing stairs, or small standing movement. Sensitivities, specificities, and F-Scores for activity categorization and changes-of-state were calculated.
The mobile versus immobile classification set had a sensitivity of 86.30% &#177; 7.2% and specificity of 98.96% &#177; 0.6%, while the second prediction set had a sensitivity of 88.35% &#177; 7.80% and specificity of 98.51% &#177; 0.62%. For the third classification set, sensitivity was 84.92% &#177; 6.38% and specificity was 98.17 &#177; 0.62. F1 scores for the first, second and third classification sets were 86.17 &#177; 6.3, 80.19 &#177; 6.36, and 78.42 &#177; 5.96, respectively. This demonstrates that WMMS performance depends on the evaluation protocol in addition to the algorithms. The demonstrated protocol can be used and tailored for evaluating human activity recognition systems in rehabilitation medicine where mobility monitoring may be beneficial in clinical decision-making.

A standardized evaluation method was developed for Wearable Mobility Monitoring Systems (WMMS) that includes continuous activities in a realistic daily living environment. Testing with a series of daily living activities can decrease activity recognition sensitivity; therefore, realistic testing circuits are encouraged for valid evaluation of WMMS performance.

An evaluation method that includes continuous activities in a daily-living environment was developed for Wearable Mobility Monitoring Systems (WMMS) that ...

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches ...

Visualization of Intensity Levels to Reduce the Gap Between Self-Reported and Directly Measured Physical Activity

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, and vigorous PA. However, inaccuracies may occur due to individually different understanding of PA intensity levels. Alternatively used direct measures (e.g., accelerometers) are susceptible to reactivity bias and may lack the ability to capture certain activities. Compared to accelerometer measurement, respondents report more time spent in higher-intensity PA. A video that visualizes PA intensity levels might help to overcome this problem. This report describes the design of a randomized controlled trial as a methodology to investigate the effect of a video on the difference between self-reported and directly measured PA. It is hypothesized that the video reduces the mean difference between the two measures. Individuals from the general population are recruited. Hip-worn accelerometers are used to collect directly measured PA data on seven consecutive days. Afterwards, participants are randomly allocated to the experimental and the control group. The experimental group receives a video demonstration on PA intensity levels and subsequent PA assessment via self-administered computer-assisted questionnaire. The control group receives PA assessment only. Thereafter, the data are processed to compare the difference between self-reported and accelerometer-based moderate-to-vigorous physical activity (MVPA) between the study groups using a two-sample t-test. This methodology is appropriate for investigating the effect of any existing or self-produced video on the difference between the two measurement methods. It can be used not only for persons from the general population, but for a variety of other populations and contexts as accurate measures are needed to evaluate PA levels.

This protocol describes a randomized controlled trial as a method to test the effect of a video demonstration on the intra-individual difference between self-reported and accelerometer-based moderate-to-vigorous physical activity.

Physical activity (PA) assessment needs tools that are inexpensive and easy to administer. Common questionnaires inquire time spent in light, moderate, ...

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to join existing programs at cardiac rehabilitation centers. The unique remote patient monitoring system presented here enables healthcare providers to monitor CR patients at home in real-time and at low cost. The system combines mobile technology, artificial intelligence, and supportive services, expanding the delivery of medical care to the patient&#39;s home. The primary aim of the study is to increase the long-term adherence to physical activity in patients who participate in CR via the addition of a home-based digitally monitored CR component to the standard CR program in patients with ischemic heart disease (IHD), with the idea of forming new habitual health behaviors and increasing the long-term motivation for physical exercise (PE) habits at home. Secondary aims are to assess the program&#39;s impact on the physical activity level measured by average steps per day, minutes of exercise per week, blood pressure, metabolic parameters, body mass index, and waist-to-hip ratio, as well as a quality-of-life (QoL) questionnaire.The study has two arms: (1) home-based monitored exercise using a smart digital garment and wristband, in addition to motivation and reinforcement via text messages; (2) standard CR facility-based exercise. The study design is a randomized, controlled trial comparing standard CR to a home-based monitoring and reinforcement program. The study program is designed for 12 weeks.Clinical tests and anthropometric measurements are performed before and after the study, measuring height, weight, waist circumference, visceral fat and body mass index (BMI), blood pressure, and HbA1c and lipid profile. Patients have to complete a baseline survey including socio-demographic characteristics and QoL questionnaire SF-36. At the end of the study, patients complete a survey regarding the use of the smart digital garment&#39;s benefits and usability. The study is currently underway.

The aim is to promote a new approach to cardiac rehabilitation (CR), using a unique remote patient monitoring system that will enable healthcare providers to monitor CR patients at home at low cost, with the intention of making CR services more accessible and improving compliance. The study is currently underway.

Despite the evidence that cardiac rehabilitation (CR) reduces the risk of recurrent cardiac events, only a minority of eligible patients are willing to ...

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the Healthy Landscape and Healthy People Lab, National Taiwan University (HLHP-NTU), on smartphones and smartwatches to collect data on users' real-time psychological and physiological responses and environmental information. A flexible and integrated research method was proposed because it can be difficult to measure multi-dimensional aspects of personal data in on-site studies in landscape and outdoor recreation research. An on-site study conducted in 2020 at the National Taiwan University campus was used as an application example. A dataset of 385 participants was used after excluding invalid samples. During the experiment, participants were asked to walk around campus for 30 min when their heart rate and psychological-scale items were measured, together with several environmental metrics. This work aimed to provide a possible solution to help on-site studies track real-time human responses that match ambient factors. Due to the app's flexibility, its use on wearable devices shows excellent potential for multidisciplinary research studies.

The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.

The current protocol aims to showcase the technology integration, providing a detailed description of adopting the HealthCloud app, developed by the ...

Environment

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling designs aimed at mitigating fatigue are typically evaluated in controlled laboratory environments, but the effectiveness of translating such strategies to operational environments can be challenging to assess. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in a complex operational environment. We studied 44 airline pilots over 34 days while they flew a fixed schedule, which included a baseline data collection with 5 days of mid-morning flights, four early flights, four high-workload mid-day flights, and four late flights that landed after midnight. Each work block was separated by 3&#8211;4 days of rest. To assess sleep, participants wore a wrist-worn research-validated activity monitor continuously and completed daily sleep diaries. To assess the circadian phase, pilots were asked to collect all urine produced in four or eight hourly bins during the 24 h after each duty block for the assessment of 6-sulfatoxymelatonin (aMT6s), which is a biomarker of the circadian rhythm. To assess subjective fatigue and objective performance, participants were provided with a touch-screen device used to complete the Samn-Perelli Fatigue Scale and Psychomotor Vigilance Task (PVT) during and after each flight, and at waketime, mid-day, and bedtime. Using these methods, it was found that sleep duration was reduced during early starts and late finishes relative to baseline. Circadian phase shifted according to duty schedule, but there was a wide range in the aMT6s peak between individuals on each schedule. PVT performance was worse on the early, high-workload, and late schedules relative to baseline. Overall, the combination of these methods was practical and effective for assessing the influence of sleep loss and circadian phase on fatigue and performance in a complex operational environment.

Sleep loss and circadian misalignment contribute to numerous operational accidents and incidents. The effectiveness of countermeasures and work scheduling designs aimed at mitigating fatigue can be challenging to evaluate in operational environments. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in complex operational environments.

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling ...

Measuring Engagement of Spectators of Social Digital Games

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of participants with motion tracking systems. In the context of games that are not confined within a screen, measuring the different dimensions of engagement such as physiological arousal can be challenging. The focus of the study is made on the spectators of the game and the differences in their engagement according to interactivity. Engagement is measured with physiological and self-reported arousal, as well as an engagement questionnaire at the end of the experiment. Physiological arousal is measured with electrodermal activity (EDA) sensors that record the data on a portable device (EDA box). Portability was essential because of the nature of the game, which is akin to a life-size pong and includes many participants that move. To have an overview of the events of the game, three cameras are used to film three angles of the playing field. To synchronize the EDA data with events happening in the game, boxes with digital numbers are used and put in the frames of cameras. Signals are sent from a sync box simultaneously to the EDA boxes and to light boxes. The light boxes show the synchronization numbers to the cameras, and the same numbers are also logged on the EDA data file. That way, it is possible to record EDA of many people that move freely in a large space and synchronize this data with events in the game. In our particular study, we were able to assess the differences in arousal for the different conditions of interactivity. One of the limitations of this method is that the signals cannot be sent farther than 20 meters away. This method is, therefore, appropriate for recording physiological data in games with an unlimited number of players but is restricted to a limited space.

We propose a methodology that enables measuring engagement of spectators in a social digital game combining physiological and self-reported data. As this digital game involves a group of freely moving people, the experience is filmed using a synchronizing technique that links physiological data with events in the game.

The goal of this methodology is to assess explicit and implicit measures of engagement of spectators during social digital games in a group of ...

Home-Based Monitor for Gait and Activity Analysis

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical evaluators. These measures are slightly subjective and are performed during a visit to a clinic or hospital and constitute therefore a point assessment. Point assessments can be influenced by daily patient condition or factors such as fatigue, motivation, and intercurrent illness. To enable home-based monitoring of gait and activity, a wearable magneto-inertial sensor (WMIS) has been developed. This device is a movement monitor composed of two very light watch-like sensors and a docking station. Each sensor contains a tri-axial accelerometer, gyroscope, magnetometer, and a barometer that record linear acceleration, angular velocity, the magnetic field of the movement in all directions, and barometric altitude, respectively. The sensors can be worn on the wrist, ankle, or wheelchair to record the subject&#8217;s movements during the day. The docking station enables data uploading and recharging of sensor batteries during the night. Data are analyzed using proprietary algorithms to compute parameters representative of the type and intensity of the performed movement. This WMIS can record a set of digital biomarkers, including cumulative variables, such as total number of meters walked, and descriptive gait variables, such as the percentage of the most rapid or longest stride that represents the top performance of patient over a predefined period of time.

This innovative device uses magneto-inertial sensors to permit gait and activity analysis in uncontrolled environments. Currently in the qualification process as an outcome measure in the European Medical Agency, one of the applications will be to serve as a clinical endpoint in clinical trials in neuromuscular diseases.

Current outcomes in neuromuscular disorder clinical trials include motor function scales, timed tests, and strength measures performed by trained clinical ...

Bioengineering

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments in clinical studies. Nevertheless, the real-life application of these technologies in studies involving a large number of participants for a significant observation period may be hindered by several practical challenges. 
In this study, we present a modified protocol from a previous intervention study for the mitigation of health effects from desert dust storms. The study involved two distinct population groups: asthmatic children aged 6-11 years and elderly patients with atrial fibrillation (AF). Both groups were equipped with a smartwatch for the assessment of physical activity (using a heart rate monitor, pedometer, and accelerometer) and location (using GPS signals to locate individuals in indoor "at home" or outdoor microenvironments). The participants were required to wear the smartwatch equipped with a data collection application on a daily basis, and data were transmitted via a wireless network to a centrally administered data collection platform for the near real-time assessment of compliance. 
Over a period of 26 months, more than 250 children and 50 patients with AF participated in the aforementioned study. The main technical challenges identified included restricting access to standard smartwatch features, such as gaming, internet browser, camera, and audio recording applications, technical issues, such as loss of GPS signal, especially in indoor environments, and the internal smartwatch settings interfering with the data collection application.
The aim of this protocol is to demonstrate how the use of publicly available application lockers and device automation applications made it possible to address most of these challenges in a simple and cost-effective way. In addition, the inclusion of a Wi-Fi received signal strength indicator significantly improved indoor localization and largely minimized GPS signal misclassification. The implementation of these protocols during the roll-out of this intervention study in the spring of 2020 led to significantly improved results in terms of data completeness and data quality.

Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.

Wearable sensors, which are often embedded in commercial smartwatches, allow for continuous and non-invasive health measurements and exposure assessments ...

Mobile Device-assisted Dietary Ecological Momentary Assessments for the Evaluation of the Adherence to the Mediterranean Diet in a Continuous Manner

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, in a real-world setting and in a continuous manner. They have the potential to minimize recall bias, participant, and investigator burden, and decrease economic and time investment while maximizing ecological validity.
We developed a set of EMAs aimed at evaluating continuous adherence to the MedDiet. Four multiple-choice EMAs are sent daily in a randomized manner from a total of eight questions. The EMAs enquire about the consumption of 11 key food groups of the Mediterranean diet in the last 24-48 h in a semi-quantitative way. EMAs capture the daily frequency of consumption of fruits, vegetables, and extra virgin olive oil on different days of the week. Additionally, EMAs capture the weekly frequency of consumption of whole grain products, sugary drinks, nuts, legumes, sweets, fish and seafood, and red and processed meats. A designed scoring system behind the EMAs extracts the percentage of adherence to the MedDiet recommendations and calculates a quality index of the diet every week. Individualized reports are sent periodically to the volunteers highlighting the strengths and weaknesses of their diet. EMAs are also expected to have a behavioral effect, reinforcing the choice of Mediterranean foods.

Presented here is a study protocol aimed at monitoring continuous adherence to the Mediterranean diet (MedDiet) by means of ecological momentary assessments. The method evaluates the intake of key food groups of the MedDiet and calculates an index of adherence.

Mobile device-assisted dietary ecological momentary assessments (EMAs) have emerged as a new tool allowing the evaluation of dietary intake in real time, ...