Cooperative Wireless Gesture Detection Using Peer Devices
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Solution Overview
Problem
Current wireless gesture detection systems face limitations in accuracy and range, particularly in detecting fine motor gestures and small-scale movements, as they rely on proximity to a wireless access point and are prone to signal decay with distance, requiring specialized hardware and being restricted to direct signal paths.
Innovation Solution
The system enhances gesture detection by forming peer-to-peer connections between devices in close proximity to the user, utilizing channel state information (CSI) and artificial intelligence/machine learning models to improve accuracy, and compensates for signal decay through multiple connections and sensor fusion, allowing detection of intricate gestures without the need for specialized hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If wireless gesture detection is performed using a single access point, then device complexity is reduced, but measurement precision deteriorates due to limited detection accuracy and range
Solution Approach 1:
The patent combines multiple wireless devices (access points, peer devices) into a collaborative gesture detection system. Multiple devices work together to detect gestures, sharing detection responsibilities and combining their observations to improve overall measurement precision while distributing system complexity across multiple components rather than concentrating it in a single access point
2Length of stationary object
If detection range is increased, then user mobility is improved, but measurement precision deteriorates due to signal decay with distance
Solution Approach 1:
The patent segments the detection space by deploying multiple wireless devices at different locations. Each device covers a specific spatial zone, and together they provide comprehensive coverage. This segmentation allows the system to maintain measurement precision across extended ranges by ensuring that any gesture occurs within the effective detection range of at least one device
Solution Approach 2:
The system implements feedback mechanisms where multiple devices continuously monitor wireless channel state information and share detection results. This feedback loop allows the system to identify and weight observations from devices with stronger signals, compensating for signal decay and maintaining precision across extended detection ranges
3Measurement precision
If fine motor gestures are detected, then measurement precision is improved, but device complexity increases due to requirement for specialized hardware
Solution Approach 1:
The patent replaces specialized hardware systems with a software-based collaborative approach using standard wireless devices. Instead of requiring dedicated gesture detection hardware, the system uses off-the-shelf wireless communication devices that leverage existing wireless channel state information and apply signal processing algorithms to detect fine motor gestures, thereby achieving high measurement precision without increasing hardware complexity
4Measurement precision
If multiple peer-to-peer connections are formed, then measurement precision is improved through cooperative detection, but device complexity increases
Solution Approach 1:
The patent implements a universal protocol that enables multiple wireless devices to participate in gesture detection through standardized peer-to-peer connections. The same communication and coordination mechanisms are used across all device pairs, allowing the system to manage multiple connections efficiently without proportionally increasing complexity. Each device can simultaneously maintain multiple connections while following the same universal interaction rules
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy and reliability of gesture detection across various distances and environments, enabling the detection of fine motor gestures and reducing the need for specialized hardware, while maintaining efficiency in power and computational consumption.
Implementation Method 1
Channel changes are detected at the receiver as, for example, received signal strength indicator (RSSI) modifications, channel state information (CSI) changes, a Doppler effect or Doppler shift, and/or a frequency shift in the received signal
Data Source
AI summary
Methods and systems are described for gesture and movement detection using wireless signals. Peer devices collaborate to detect hand and/or finger gestures. Gesture detection using one or more cooperative devices is provided. The gesture is detected cooperatively in a session. Device-to-device gesture detection improves accuracy such that fine motor gestures are inferred. Artificial intelligence systems, including neural networks, are trained for improving the gesture and movement detection. Models are developed for the gesture and movement detection. Related apparatuses, devices, techniques, and articles are also described.