Wearable Multi-Modal Output Using GAN-Generated Sensory Data
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Solution Overview
Problem
Existing wearable devices primarily offer mono-modality interactions, lacking the capability to generate multi-sensory experiences such as visual, aural, and tactile sensations, which are essential for enhancing immersion in virtual reality environments.
Innovation Solution
A wearable device equipped with a neural network to generate image, text, and sound data not initially present in input data, and convert sound data into pulse-width modulation (PWM) signals for tactile feedback, utilizing a generative adversarial network (GAN) and integrate-and-fire neuron (IFN) models to deliver multi-modality experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wearable devices use mono-modality interactions (single type of data output), then device complexity is reduced and ease of manufacture is improved, but user immersion and multi-sensory experience are insufficient
Solution Approach 1:
The wearable device is designed to perform multiple functions by generating and outputting different types of data (image, text, sound) through a single integrated system. The processor executes instructions to determine what data is included in source data and generates missing modalities, allowing the device to provide visual, auditory, and tactile experiences through unified components (display, speaker, actuator), thereby achieving multi-functionality without proportionally increasing complexity.
Solution Approach 2:
A neural network acts as an intermediary component between the processor and the output devices. The neural network receives source data and generates additional data modalities (e.g., generating sound data from text data, or generating image data from sound data), serving as a mediator that enables multi-modality output while keeping the overall system architecture manageable and integrated.
2Adaptability or versatility
If wearable devices generate multiple data modalities (image, text, sound) using neural networks, then multi-modality output capability is improved, but processing time and computational requirements increase
Solution Approach 1:
The device determines in advance what types of data are included in the source data before generating additional modalities. By first assessing the content of source data and then selectively generating only the necessary additional data types, the system avoids unnecessary processing steps and reduces overall processing time while still achieving comprehensive multi-modality output.
Solution Approach 2:
The neural network generates only the specific data modalities that are needed based on the source data content, rather than generating all possible modalities regardless of need. This partial action approach (generating only what is necessary) reduces computational overhead and processing time while still achieving the required multi-modality capability for the given input.
3Adaptability or versatility
If wearable devices convert sound data to PWM signals for tactile feedback, then tactile modality delivery is improved, but energy consumption increases
Solution Approach 1:
The device converts sound data into PWM (pulse-width modulation) signals to control actuators for tactile feedback. This substitution of direct mechanical actuation with PWM-based control allows for more efficient energy usage, as PWM enables precise control of actuator activation timing and duration, reducing unnecessary energy consumption while maintaining effective tactile output.
Data Source
AI summary
Provided are a wearable device for providing a multi-modality, and an operation method of the wearable device. The operation method of the wearable device including obtaining source data including at least one of image data, text data, or sound data, determining whether the image data, the text data, and the sound data are included in the source data, based on determining that at least one of the image data, the text data, or the sound data is not included in the source data, generating the image data, the text data, and the sound data, which are not included in the source data, by using a generator of an generative adversarial network (GAN), which receives the source data as an input, generating a pulse-width modulation (PWM) signal based on the sound data, and outputting the multi-modality based on the image data, the text data, the sound data, and the PWM signal.