Anomaly Detection

Build a passive elderly health monitoring system requiring no wearables — using 60GHz mmWave radar for vital signs, toilet visit frequency tracking, Withings Body+ BIA smart scale, IoT pill dispenser, and ML anomaly detection with LINE family alerts.

An adaptive IoT system learns elderly residents’ behavioral patterns over 30–90 days, then detects deviations in sleep, mobility, and eating routines that signal early health decline—delivering LINE OA alerts to family members before conditions become critical.

IoT systems with Machine Learning-based behavioral learning deliver an additional 15–25% energy savings beyond fixed-schedule systems by automatically adapting to the actual living patterns of every household — no manual reprogramming required.

Smart Longevity Real-Time Dashboard displays per-circuit energy KPIs, automatically detects anomalies, predicts monthly costs, and delivers LINE notifications — giving homeowners continuous, actionable control over electricity spending.

Next-generation Smart Longevity systems use Machine Learning to profile electricity behaviour within 4–6 weeks, automatically detect anomalies and Vampire Loads, and adapt energy strategies to Thailand’s seasonal patterns.

The energy-efficient homes of the future will be driven by intelligent energy control systems that learn from experience, predict problems before they occur, and connect to Smart Grids to benefit from Dynamic Pricing electricity tariffs in the Energy 4.0 era.

HappySmart AI SmartHome automatically analyzes household energy consumption patterns, identifies high-draw devices, builds a personalized energy savings plan, and adapts as behavior changes — delivering measurable electricity bill reductions each month.