Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
July 27th, 2018
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.
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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Overview
This methodology can provide real-time, objective, continuous, and ecologically valid data for researchers engaged in longitudinal health studies and clinical trials. It enables the capture of acute data as well as subtle changing events over time, requiring little to no effort from participants.
Key Study Components
Area of Science
- Neuroscience
- Health Technology
- Clinical Research
Background
- Unobtrusive sensors and pervasive computing technology are integrated into the daily lives of older adults.
- This approach allows for the continuous recording of health and activity changes over extended periods.
- It provides high-frequency, multi-domain data suitable for research and clinical use.
- Substantial deployment has been made in chronic conditions like mobility disorders and dementia.
Purpose of Study
- To capture meaningful health and activity data unobtrusively.
- To facilitate longitudinal studies with minimal participant burden.
- To adapt the methodology for various research applications.
Methods Used
- Deployment of unobtrusive sensors in home environments.
- Continuous monitoring of health and activity metrics.
- Integration of data into an online console inventory system.
- Collection of digital, activity, and health data across multiple domains.
Main Results
- Real-time data capture with minimal participant effort.
- Ability to detect both acute and subtle changes over time.
- Extensibility of protocols for various research domains.
- Enhanced understanding of functional changes in chronic conditions.
Conclusions
- This methodology significantly improves data collection in health research.
- It supports the study of important functional changes in older adults.
- Future applications can expand to various health-related research areas.
Frequently Asked Questions
Executive Industry Relevance
This methodology enables continuous, unobtrusive monitoring of functional health metrics in real-world settings, providing ecologically valid data for longitudinal studies in neurodegenerative and mobility-related conditions. By capturing subtle behavioral and physiological changes over extended periods, it supports early detection of prodromal syndromes and evaluation of clinical interventions in naturalistic environments. The system reduces participant burden while enhancing data quality, directly informing risk assessment and go/no-go decisions in preclinical and clinical development pipelines for geriatric therapeutics.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by linking sensor-derived mobility, sleep, and medication adherence metrics to disease progression in preclinical models.
- Operational Value: Supports biological de-risking through continuous, objective monitoring of functional domains in aging cohorts, reducing reliance on episodic clinical assessments.
Screening & Assay Development
- Scientific Value: Prepares validated, disease-relevant systems for downstream screening by establishing baseline activity and physiological profiles in home-based environments.
- Operational Value: Enhances assay standardization and reproducibility through automated, continuous data streams from multi-sensor arrays, minimizing variability in longitudinal readouts.
Translational & Preclinical Research
- Scientific Value: Demonstrates translational continuity by capturing functional endpoints—such as gait speed variability and medication adherence—that correlate with cognitive and mobility outcomes in early disease stages.
- Operational Value: Enables risk-adjusted advancement decisions by detecting subtle, pre-symptomatic changes in health trajectories over months to years, informing dose selection and timing in intervention studies.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from target validation through preclinical validation, offering continuous functional monitoring that bridges in vitro findings and in vivo outcomes in aging-related research.
- Discovery Biology: Supports hypothesis testing and pathway clarification by quantifying real-time changes in mobility, sleep, and social engagement in response to genetic or pharmacological perturbations.
- Screening: Delivers assay readiness via standardized, scalable sensor deployment and real-time data validation, ensuring consistent compound evaluation across cohorts.
- Analytics: Provides quantitative, multi-domain outputs—including step count, weight trends, and nocturnal bathroom trips—that enable statistical comparison of treatment effects over time.
- Translational Research: Connects to preclinical continuity by aligning sensor-derived functional metrics with biomarker trajectories, supporting predictive modeling of disease progression.
- Enterprise Reuse: Functions as a reusable platform for multi-domain health surveillance, allowing future integration of wearables, environmental sensors, and bed activity systems across therapeutic areas.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence by reducing mechanistic ambiguity through longitudinal, ecologically valid functional monitoring in natural habitats.
- Operational Value: Ensures standardization, reproducibility, and scalability through centralized console inventory, QR-coded device tracking, and automated data validation checks.
- Strategic Value: Improves go/no-go decisions by enabling early detection of functional decline, reducing late-stage failure risk in neurodegenerative and mobility-focused portfolios.
- Portfolio Impact: Supports risk-adjusted prioritization by identifying subgroups with distinct trajectories of walking speed variability or medication adherence, informing enrichment strategies in clinical trials.
Implementation Considerations
- Requires expertise in gerontology, sensor networks, and data annotation to interpret multi-modal outputs such as self-reported mood and medication logs.
- Depends on reliable internet connectivity, Zigbee coordinator dongles, and hub computer configuration for seamless data transmission from motion, door, and wearable sensors.
- Necessitates cross-team standardization between field technicians, data scientists, and clinical coordinators to ensure consistent sensor placement, virtual floor plan mapping, and PAN activation protocols.
- Involves adaptation considerations when deploying across diverse home layouts, requiring custom virtual floor plans and sensor linkage mapping via the Control Panel interface.
- Includes practical limitations such as signal interference in multi-story homes and dependency on participant engagement with periodic self-report annotations for contextualizing sensor data.
