Automated movement quality assessment
Video-based pose estimation helps translate clinical observations of gait, posture and balance into structured assessment signals.
Privacy-first predictive AI for senior care
Predictive AI for Proactive Senior Care
PreSense AI helps long-term care and senior living organizations identify elevated fall, frailty and cognitive decline risks before they become critical events.
Edge processingNo raw video uploadAnonymized movement analytics
Why it matters
Senior care teams need earlier signals, not more alarms.
Most care systems react after an incident occurs. But fall risk, frailty and functional decline often build up gradually through subtle changes in gait, posture, balance and daily movement.
Falls are often detected too late.
Traditional alerts usually notify staff after an event, leaving limited time for prevention.
Wearables depend on compliance.
Devices can be forgotten, removed or rejected by residents, especially in long-term care environments.
Subtle mobility changes are hard to quantify.
Manual observation cannot continuously measure gait stability, balance trends or movement quality at scale.
Staff need prioritized insight.
Care teams need clear, actionable risk signals that help them focus attention where it matters most.
Platform overview
From passive movement to actionable care insight.
PreSense AI analyzes privacy-protected movement patterns at the edge, converting gait, posture, balance and activity signals into predictive risk scores and digital frailty indicators for care teams.
No resident wearables required.
Raw video stays local.
Anonymized skeletal data powers analytics.
Risk insights support proactive care workflows.
Core capabilities
Built for proactive geriatric care.
Passive Movement Monitoring
Continuous, non-intrusive movement analysis without wearables, designed to preserve resident comfort and dignity.
Predictive Risk Scoring
Identify residents with elevated fall, frailty or functional decline risks using movement-derived signals and predictive analytics.
Digital Frailty Indicators
Track gait, posture, balance and mobility changes over time to support earlier intervention and outcome review.
CogniDrive Empowerment Platform
Digital cognitive and micro-mobility training programs that can be adjusted based on AI-generated health metrics.
Assessment scenarios
Grounded in senior movement assessment workflows.
The platform vision is informed by practical evaluation workflows from the source materials: objective movement quality assessment, activity energy monitoring, care-plan adjustment and community-based early intervention.
Static and dynamic motion analysis
Simple repeated actions can be reviewed through keypoints, motion waveforms and temporal alignment to support objective quality evaluation.
Activity and energy monitoring
Activity signals can support exercise-plan adjustment, fatigue awareness and safer mobility programs for older adults.
Community and primary care support
Objective mobility indicators can help family doctors and community care organizations coordinate earlier intervention.
Care loop
A closed loop for proactive care.
01
Sense
Passive movement capture at the edge.
02
Understand
Convert video into anonymized skeletal and motion-vector data.
03
Predict
Generate risk scores and digital frailty indicators.
04
Intervene
Support care team actions and CogniDrive training programs.
05
Evaluate
Track changes after intervention and refine care plans.
Privacy by design
Privacy is designed into the architecture.
PreSense AI is designed to process visual data locally at the facility level. Raw video does not need to leave the edge unit. Analytics are based on anonymized skeletal and movement-vector data.
Privacy & complianceEdge processing
No raw video upload
Anonymized skeletal tracking
Canada-based analytics data
Technology
Local processing, anonymized signals, care-oriented indicators.
The platform is designed to turn movement into structured indicators through edge processing, skeletal tracking and predictive analytics.
Visual input
Facility environment
Edge unit
Local processing
Skeletal vectors
Anonymized signals
Risk model
Care indicators
Dashboard
Care workflow
Use cases
Designed for senior care environments.
Long-Term Care Facilities
Prioritize resident attention, support fall-risk workflows and monitor frailty trends.
Senior Living Communities
Support independent living with privacy-preserving risk awareness and wellness engagement.
Community and Primary Care Programs
Provide objective movement indicators for earlier intervention and care coordination.
Research and Clinical Partnerships
Enable structured movement analytics for validation, pilot programs and geriatric care innovation.
Ready to pilot proactive senior care?
Partner with PreSense AI to explore privacy-first predictive analytics for your long-term care or senior living organization.