We built two logging components for the device application: one for basic system monitoring, and another with integrated support for the Robot Operating System (ROS) to capture detailed sensor and runtime data.
Our team developed a database and API layer to store data from EM devices, map relationships between data types, and track user actions. This allowed client applications to reliably read, write, and manage all device-related information.
Our engineers used AWS to store device state data and manage the distribution of system packages and application content, ensuring each device receives the correct files at the right time.
We created a backend module that manages content and software packages — handling installation, uninstallation, and updates remotely across devices, with packages grouped by device type and delivered in Debian format.
Our developers used AWS IoT Jobs to set up and trigger remote operations on individual or multiple devices. A dedicated component monitors job topics and runs the appropriate commands based on each task.
Our team built a mobile app that detects emotionally intelligent (EM) devices on local networks and manages their initial setup for cloud communication. This simplified the onboarding process for devices not yet connected to the cloud.
We enabled remote execution of key system tasks — Reboot, Shutdown, Install Package, and Uninstall Package — giving operators control over essential actions directly from the cloud dashboard.
Our specialists have developed a real-time system that captures device errors across four categories — Hardware, External Hardware, OS, and Software — to support faster issue resolution.
We created an initialization procedure that prepares new EM devices for the RDMP (Remote Device Management Platform) by preloading essential software, services, certificates, and runtime dependencies. This ensures each device is fully ready from the first boot.
