# Controlling Industrial Robotic Arms Using Gyroscopic and Gesture Inputs from a Smartwatch
## 4 System Architecture  
The control architecture comprises the smartwatch (gesture acquisition), a local Python script (data filtering and mapping), a cloud interface (ThingWorx) for monitoring, and the industrial robot (execution). The smartwatch gathers real-time data, which are analyzed and sent to ThingWorx. Figure 3 below illustrates the system diagram. The diagram illustrates the complete architecture of the smartwatch-based robotic control system, highlighting each processing stage and data flow:  
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2. Xiaomi Smartwatch 2 Pro * Equipped with an IMU (Inertial Measurement Unit): accelerometer, gyroscope, and magnetometer. * Runs the Wow Mouse application (developed by Doublepoint), which enables gesture and orientation capture and Bluetooth communication. 3. Python Script (Local) * Uses TouchSDK to access raw sensor data (received by Doublepoint). * Applies sensor fusion algorithms (complementary/Kalman filters) to compute quaternion orientation, linear acceleration, and gravity vector. * Performs gesture recognition (tap, pinch, and rotate) and motion analysis. * Translates filtered signals into commands for robotic movement. 4. xArm Robot (UFactory) * Executes real-time directives (via xArm Python API) to carry out joint and end-effector motions. * Functions as the tangible implementer of user-intended actions recorded by the smartwatch. 5. ThingWorx Platform (Cloud) * Interfaced over REST API. * Continuously acquires real-time data from the robot and smartwatch: locations, gestures, and IMU measurements. Figure 3: System architecture for smartwatch-based robotic control. The Xiaomi Smartwatch streams inertial data through the Wow Mouse interface and is interpreted via a Python SDK. Commands are sent to the UFactory xArm robot, while data monitoring and visualization are handled through the ThingWorx platform. * Exhibits a digital counterpart of the robot together with control metrics for oversight and troubleshooting. * Records data for analysis and performance evaluation. * Feedback Loop
* Facilitates alignment among operator input, robotic reaction, and cloud-based visualization. The ThingWorx platform plays a central role not only in enabling communication between the smartwatch and robotic system but also in supporting real-time monitoring, diagnostics (machine learning), and decision-making (with simple scripted conditions or even using AI widgets). Through its IoT analytics capabilities, ThingWorx can collect and visualize sensor data such as orientation, velocity, and gesture classification from wearable devices, as well as status feedback from robotic actuators. This bidirectional data stream allows for real-time dashboards that track performance metrics, detect anomalies, and trigger alerts or corrective actions when predefined thresholds are exceeded. For example, if the system detects inconsistent gesture input or latency spikes, ThingWorx can log the event, notify the user, or temporarily suspend control to avoid unintended robot movement. Additionally, ThingWorx enables scalable system integration, allowing multiple smartwatches and robots to operate within a coordinated framework, where high-level decisions--such as task prioritization or resource allocation--can be informed by real-time analytics and historical performance trends. The use of ThingWorx enables the remote monitoring and logging of robotic states and wearable inputs. In addition to real-time visualization, the platform supports REST APIs and orchestration tools, allowing for potential multi-robot coordination via cloud-based commands. User authentication is handled through session-based access control and role-specific permissions, ensuring secure operation. However, as the number of connected devices increases, bandwidth limitations may arise. To mitigate this, future work will explore edge-level filtering and event-driven data synchronization to reduce network load while preserving responsiveness.