Move beyond the constraints of the laboratory. Care Active equips researchers with IoT devices and AI-powered analytics to capture authentic human behavior — at home, outdoors, and everywhere in between.
Traditional research environments introduce observer bias, limit study duration, and fail to capture the complexity of daily living. We close the gap between controlled experiments and real-world validity.
IoT sensors capture behavior passively 24/7, eliminating recall bias and the limitations of self-reporting.
On-device data preprocessing ensures participant privacy is preserved. Fully IRB-compliant with no raw location exposure.
Simple device setup designed for non-technical participants. Remote monitoring and management for research teams.
AI-powered pattern detection, longitudinal trend analysis, and exportable data structures ready for statistical tools.
Participants install compact sensors at home or carry wearables. Guided setup requires no technical background.
Devices passively collect location, motion, and behavioral signals continuously across all environments.
On-device and cloud AI anonymizes, structures, and enriches raw sensor data into research-ready variables.
Access dashboards and export tools to integrate data into your analysis pipeline.
Every feature is designed with research workflows in mind — from IRB documentation to publication-ready exports.
Monitor enrollment, device status, and data completeness across all participants from one interface.
Location abstraction, on-device processing, and configurable data minimization aligned with IRB requirements.
Pre-computed variables — activity levels, sleep parameters, mobility metrics — ready to merge with clinical data.
Detect behavioral change over weeks or months with automated anomaly flagging and cohort comparison tools.
Three distinct behavioral sensing methodologies, each designed to unlock new dimensions of ecological research.
Room-level presence tracking combined with continuous activity monitoring reveals spatial routines, social synchrony, and long-term behavioral change — all within the home environment.
Understand community mobility, life-space, and social engagement without ever exposing participants' precise whereabouts. On-device processing preserves privacy while yielding rich behavioral indices.
A single compact sensor applied to beds, refrigerators, or medication bottles transforms everyday objects into behavioral observation instruments — capturing sleep schedules, eating habits, and medication adherence passively.
Talk to our research team about study design, IRB documentation support, and pilot program availability.
Contact the Research Team →Using wrist-worn devices and room-level positioning infrastructure to capture spatial routines, social synchrony, and longitudinal activity trends in natural home settings.
Understanding how people actually use their living spaces — and how those patterns shift over time or in response to interventions — is nearly impossible to study in a laboratory. Self-report diaries are unreliable; direct observation is intrusive and impractical at scale.
This case study demonstrates how Care Active's indoor sensing infrastructure enables continuous, passive behavioral monitoring across multi-room environments with no researcher presence required.
Participants wear the Care Watch, a lightweight wrist-worn device that communicates with small Bluetooth Stations installed in each room of the home. Signal strength triangulation provides room-level location estimates continuously throughout the day.
Combined with the watch's built-in accelerometer, the system produces a longitudinal record of both where participants spend time and how active they are in each space — without cameras or microphones.
Social Synchrony Analysis. In household studies, overlapping presence data from multiple participants reveals co-location patterns — a proxy for social interaction and relationship dynamics that is otherwise difficult to measure continuously.
Longitudinal Activity Trends. Tracking room-specific activity levels over months enables researchers to detect gradual behavioral change — relevant to aging research, rehabilitation outcomes, and mental health studies.
Spatial Routine Disruption. Deviations from established spatial patterns (e.g., spending significantly less time in the kitchen, or increased nighttime movement) can serve as sensitive behavioral markers in clinical research.
Explore how we capture outdoor mobility without exposing participant location data.
View Case Study 02 →Capturing real-world mobility and community engagement through on-device AI processing — rich behavioral indices without ever transmitting identifiable location data.
Location data is among the most sensitive personal information a study can collect. Traditional GPS-tracking approaches require storing detailed movement logs on central servers — a significant IRB and participant consent barrier that has historically limited the scope of community-based research.
Care Active solves this with an on-device processing architecture: raw GPS coordinates are processed locally on the participant's smartphone and never transmitted. Only derived, abstract behavioral variables reach the research platform.
Despite never exposing actual places visited, the system generates a comprehensive mobility profile for each participant:
The system's architecture makes IRB approval substantially easier to obtain. Because precise location data is never collected or stored by the research platform, many of the most common ethical concerns around location tracking are addressed at the infrastructure level — not through policy alone.
Care Active provides documentation templates and technical architecture diagrams specifically designed to support IRB submission for location-based behavioral studies.
See how a single sensor transforms everyday objects into behavioral instruments.
View Case Study 03 →One compact sensor. Multiple behavioral windows. From sleep architecture to medication adherence, Care Motion turns everyday objects into longitudinal behavioral observation instruments.
Care Motion is a compact vibration and accelerometry sensor designed to detect motion events from objects it is attached to — without any camera, microphone, or participant interaction. Hanging from a mattress edge or adhered to a cabinet, it becomes a silent, continuous observer.
The same hardware, deployed differently, captures fundamentally distinct behavioral data streams — making it an exceptionally versatile tool across research domains.
Sleep as a behavioral marker. Objective sleep data — collected at home over weeks — is a far richer dataset than actigraphy alone or self-reported sleep diaries. Nighttime exit frequency is particularly relevant to dementia and fall-risk research.
Medication adherence at scale. Non-adherence is one of the most significant confounds in longitudinal clinical trials. Care Motion provides a scalable, non-burdensome way to track whether participants are actually taking medication as prescribed.
Appetite and routine changes. Refrigerator interaction data captures daily rhythm and appetite disruption — variables of high relevance in psychiatric, geriatric, and nutritional research — without any dietary reporting burden on participants.
Our research team can help you select the right sensor configuration and data pipeline for your specific research questions.
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