Wearable Gesture Control for Smart Devices
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Solution Overview
Problem
Conventional voice-controlled devices for home automation have limitations, such as requiring loud voice commands from distant locations and potential security issues due to inability to distinguish users, making it difficult to control smart devices like lights or thermostats effectively.
Innovation Solution
A wearable device that uses sensors and machine learning to detect nearby smart devices and understand user intentions without voice commands, allowing users to control devices through gestures and authentication, supporting multiple wireless protocols and technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of stationary object
If voice commands are used to control smart devices from distant locations, then control range is improved, but security is worsened due to inability to distinguish users
Solution Approach 1:
The patent introduces an accelerometer as an intermediary sensor to detect user gestures and intentions. The accelerometer captures motion data that serves as a mediator between the user and the smart device, enabling authentication and control without relying solely on voice commands. This allows the system to maintain security by detecting unique user movement patterns while still enabling distant control through the wearable device.
Solution Approach 2:
The patent replaces the acoustic field-based voice command system with a mechanical motion detection system using an accelerometer. Instead of relying on sound waves for authentication and control, the system uses mechanical motion data from user gestures to identify and authenticate users, thereby maintaining security while enabling control from various locations.
2Length of stationary object
If loud voice commands are required for distant control, then control range is improved, but ease of operation is worsened
Solution Approach 1:
The patent substitutes the acoustic field system requiring loud voice commands with a mechanical motion detection system using an accelerometer. The accelerometer detects subtle user gestures and movements, allowing quiet, intuitive control of smart devices from distant locations without requiring the user to shout or make loud sounds.
Solution Approach 2:
The accelerometer serves as an intermediary that translates subtle mechanical user gestures into control commands. This mediator enables the system to detect and respond to gentle user intentions without requiring loud voice commands, thereby improving ease of operation while maintaining extended control range.
3Adaptability or versatility
If multiple wireless protocols are supported, then adaptability is improved, but device complexity is worsened
Solution Approach 1:
The wearable device is designed with multi-functionality to support multiple wireless communication protocols including Bluetooth Low Energy (BLE), Zigbee, and others. This universal capability allows the single device to communicate with various smart devices using different protocols, enhancing adaptability without requiring separate dedicated devices for each protocol type.
Solution Approach 2:
The patent combines multiple wireless protocol support capabilities into a single wearable device platform. By merging BLE, Zigbee, and other protocol support within one device, the system achieves broad protocol compatibility while consolidating complexity into a unified hardware and software architecture, rather than requiring separate devices for each protocol.
Data Source
AI summary
Technologies for a wearable device are described. One wearable device includes a radio and a processor, the processor measures sensor data (e.g., a first angle between the wearable device and a first wireless endpoint device and a second angle between the wearable device and a second wireless endpoint device) and motion data indicative of motion of the wearable device over a first duration of time. The wearable device also measure signal strength values for communications with the respective devices. The wearable device predicts, using a first trained model, a position of the wearable device and to which target device the wearable device is directed. The wearable device predicts, using a second trained mode, a gesture made by the wearable device. The wearable device sends a message, corresponding to the gesture, to the target device to control the target device.