Multi-Modal Gesture Control for IoT Device Ambiguity
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Solution Overview
Problem
Conventional IoT devices fail to differentiate between various gesture commands seamlessly, require the user to be in line of sight, and cannot switch between voice and gesture commands effectively, leading to ambiguity in controlling multiple devices.
Innovation Solution
A method and system that utilize multi-modal gesture commands, including personalized gesture and voice commands, to identify and control IoT devices by detecting commands using gesture and voice grammar databases, determining control parameters, and considering user requirements and line of sight information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-defined gestures are configured for multiple IoT devices, then device control capability is improved, but device differentiation capability deteriorates leading to confusion
Solution Approach 1:
The patent segments the gesture recognition process by analyzing gestures in the context of specific device groups and user preferences. Instead of treating all gestures universally, the system divides gesture interpretation based on which devices are nearby and what the user has previously indicated they prefer, allowing the same gesture to mean different things for different devices.
Solution Approach 2:
The system applies local quality by making gesture recognition context-dependent rather than uniform. The meaning and target of a gesture is determined by local factors such as which devices are in proximity, the user's historical preferences for that device, and the current operational state, allowing precise differentiation even with identical gestures.
2Measurement precision
If the user must be in line of sight of the IoT device to control it, then device control accuracy is improved, but user accessibility deteriorates
Solution Approach 1:
The patent makes the gesture recognition system universal by enabling it to function both when the user is in line of sight and when they are not. The system adapts its behavior based on context - using line of sight information when available for precise control, but falling back to other methods like device grouping and preference analysis when the user is out of view, thus serving multiple operational scenarios.
Solution Approach 2:
The system introduces intermediary elements such as device proximity detection, grouping information, and preference databases that mediate between the user's gesture and the target device. These intermediaries allow the system to resolve which device to control even without direct line of sight, by using indirect information about device locations and user preferences.
3Device complexity
If conventional IoT devices use single-modal commands, then system simplicity is improved, but command versatility deteriorates
Solution Approach 1:
The patent merges multiple command modalities - gesture recognition, voice commands, and contextual information - into a unified control system. Rather than requiring users to choose between different separate systems, the patent combines these modalities so they work together, with each modality contributing to the overall command interpretation and device selection process.
Solution Approach 2:
The system dynamically adjusts which modalities are used based on the situation. For example, if voice is clearly audible, voice commands take precedence; if the user is far from devices, gesture recognition with contextual analysis is used; if multiple devices are nearby, preference databases help resolve ambiguity. This dynamic adaptation maintains simplicity when possible while enabling versatility when needed.
4Loss of time
If no query back mechanism is implemented, then system response time is improved, but device identification accuracy deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-analyzing contextual information such as device proximity, grouping relationships, and user preferences before the gesture is even executed. This pre-computation of likely targets based on historical data and current state allows the system to quickly identify the intended device without needing to query the user, thus maintaining fast response times while improving accuracy.
Solution Approach 2:
The system uses implicit feedback mechanisms where user behavior patterns, device interaction history, and contextual cues continuously inform the system about user preferences. This feedback loop allows the system to learn and adapt to user habits, improving device identification accuracy over time without requiring explicit user confirmation for each gesture.
Data Source
AI summary
A method and system are described for controlling an Internet of Things (IoT) device using multi-modal gesture commands. The method includes receiving one or more multi-modal gesture commands comprising at least one of one or more personalized gesture commands and one or more personalized voice commands of a user. The method includes detecting one or more multi-modal gesture commands using at least one of a gesture grammar database and a voice grammar database. The method includes determining one or more control parameters and IoT device status information associated with a plurality of IoT devices in response to the detection. The method includes identifying IoT device that user intends to control from plurality of IoT devices based on user requirement, IoT device status information, and line of sight information associated with user. The method includes controlling identified IoT device based on one or more control parameters and IoT device status information.