The biggest challenge in modern eldercare is an overflow of data with a lack of clarity. Homes today are filled with smart devices generating endless signals: a motion detector blinks, a fridge door opens, a smart plug activates. But what does any of it actually mean? Did someone prepare a meal or just pass through the kitchen? Take a shower, or merely walk by the bathroom?
For care providers and families, these distinctions are critical. Receiving an alert is the bare minimum. But what they really need are validated events that map onto Activities of Daily Living (ADLs): eating, bathing, mobility, personal hygiene, dressing, and moving. These daily routines are considered the benchmarks of safety, dignity, and independence. When performed consistently, they signal stability and well-being. When they start to change — meals skipped, movement slowing, or more frequent bathroom visits — they often reveal the earliest signs of decline. That’s why reliable insight into these shifts can mean the difference between reacting to a crisis and preventing one. But to get there, the industry needs to move beyond the idea that more data always means better care — and focus instead on creating smarter, more meaningful context.
The Need for Context in Care
The next evolution in digital eldercare lies not in adding complicated devices, but in connecting the signals we already have. Motion detectors, vibration sensors, humidity readings, and door openings each capture fragments of daily life. On their own, they create noise; together, they create understanding. This connection is made possible through data fusion — the process of combining multiple, independent signals into one coherent view of what’s truly happening.
In eldercare, data fusion transforms fragments into stories: fewer false alarms, fewer missed events, and more confidence in what the data means. For instance, a fridge door opening doesn’t confirm that someone ate — but when that event is paired with motion data from the kitchen, it can become a reliable indicator of meal preparation. Over time, these patterns can surface subtle behavioral changes, like fewer meals or longer nighttime wandering, that may signal early risk. Wi-Fi plays a uniquely powerful role in this ecosystem. Acting as a continuous, privacy-first presence signal, Wi-Fi Sensing solutions, like Cognitive’s WiFi Motion, can validate and enrich what other devices report. It becomes the connective tissue between disparate systems — translating raw activity into meaningful, human-centered insight.
Connecting the Signals: How WiFi Motion Brings Valuable Insight to Care
As eldercare becomes more connected, context-rich systems offer a foundational layer for understanding what’s truly happening in the home. Built on the idea that Wi-Fi can serve as a common language between devices, Cognitive designed WiFi Motion in order to deliver that continuous, privacy-first view of presence and movement. Rather than replacing other sensors, WiFi Motion enhances them — grounding their readings in the physical reality of human activity. On its own, it continuously detects motion across the home, day and night, recognizing patterns such as room transitions or prolonged inactivity. Over time, it can surface long-term behavioral trends that reveal changes in mobility, sleep, or routine.
From Motion to Meaning: Validating Activities of Daily Living
Building on WiFi Motion’s foundation, Caregiver by Cognitive (Cognitive’s eldercare engine) can therefore use data fusion to translate fragmented events into validated insights about daily living. ADLs remain the most important indicators of whether an older adult can live independently. Yet these are also the hardest to monitor accurately and ethically. Cameras raise privacy concerns; wearables require compliance. Traditional sensors detect moments, but not meaning.
Caregiver bridges that gap. By connecting Wi-Fi activity data with complementary signals, it becomes possible to validate ADLs with far greater confidence and nuance:
- Meal preparation can be inferred when Caregiver detects sustained presence in the kitchen and a vibration or door sensor confirms the fridge or cabinet was opened — a more reliable indicator than either signal alone.
- Bathing can be validated when Wi-Fi data shows a hallway-to-bathroom transition, a brief humidity spike is recorded, and the duration of presence aligns with typical shower patterns.
- Nighttime safety can be supported when motion indicates a trip from bed to bathroom followed by a return — or triggers an alert if that return doesn’t occur.
The true value lies not simply in detecting motion, but in understanding why it happened. By fusing WiFi Motion’s always-on awareness with contextual home data, Caregiver bridges the gap between detection and understanding — turning ordinary activity into actionable, privacy-preserving insight that supports safety, preserves independence, and upholds dignity.