Description
Good Way Technology develops specialized ambient assisted living devices designed for elder care and remote monitoring without compromising domestic privacy. Interviewed at the booth, Jason details the deployment of dedicated on-device Edge AI modules that process human occupancy directly at the sensor node. Operating with integrated CMOS sensors paired with on-chip edge computing engines, the hardware executes local computer vision inference solely to determine human state. Because all visual processing occurs strictly within the local chipset and no raw camera streams or pictures leave the unit, the platform ensures total privacy within sensitive residential spaces. https://www.goodway.com.tw/
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The human presence hardware distinguishes between real human activity and domestic pets, eliminating common false positive triggers caused by household animals. Utilizing mains wall power rather than standard coin batteries to support the continuous processing requirements of on-device neural inference, the wall-mounted sensor transmits concise binary presence and absence states over standard wireless links. The system coordinates via Wi-Fi or Bluetooth connectivity to relay occupant event data directly to a cloud architecture without requiring dedicated gateway base stations in every room.
Multi-room coverage tracks long-term 24-hour routine patterns and activities of daily living to identify concerning behavioral deviations. Caregivers and family members receive automated alerts when the platform detects extended periods of inactivity, such as a lack of movement in a living area or bedroom over a 12-hour or 24-hour window. This continuous room-by-room activity logging enables families to oversee solitary seniors while avoiding intrusive ambient camera feeds.
Beyond basic presence verification, Good Way provides dedicated posture and fall detection nodes that incorporate higher-capacity compute silicon. These specialized edge processors run larger neural network models capable of classifying complex body postures, tracking transitions between sitting, standing, and sudden falls. The edge AI architecture isolates the posture analysis directly on the embedded CPU, providing immediate event recognition without storing or transmitting personal imagery.
To address fire and burn risks in senior living, the sensor family also features an unattended cooking detector fitted with thermal sensing components. Mounted directly above stove ranges and cooking surfaces, the infrared hardware tracks heat signatures to alert caregivers if high cooking temperatures remain unattended. Exhibited at CEATEC 2026, the comprehensive sensor system offers integrators and care providers an edge-computed monitoring framework aimed at safeguarding seniors living alone.