Edge Computing System for Privacy-Preserving Mental Health Assessment
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Solution Overview
Problem
Current computational systems for modeling human behavior and mental health rely on remote machines and cloud computing, raising security, privacy, and latency concerns, particularly in privacy-aware clinical settings, and lack on-the-edge computation capabilities necessary for efficient and secure data processing.
Innovation Solution
A self-organizing edge computing system that utilizes resource-constrained devices like smartphones and IoT devices to collect and analyze patient data locally, forming a decentralized network for privacy-protective data processing and analysis, enabling on-device computation and communication without reliance on remote servers, using edge devices to form a localized mesh network for data aggregation and summary generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If cloud computing systems are used for mental health data processing, then computational power and data storage capacity are improved, but data security and user privacy control deteriorate
Solution Approach 1:
The system segments computational tasks between edge devices (smartphones, wearables) and cloud infrastructure. Sensitive mental health data is processed locally on edge devices using embedded machine learning models, while only aggregated insights or non-sensitive data are transmitted to cloud systems. This segmentation allows the system to leverage cloud computational power for non-sensitive operations while maintaining data security and user privacy control through local processing.
2Productivity
If centralized cloud infrastructure is used, then system resource utilization is improved, but data processing latency increases
Solution Approach 1:
The system performs preliminary data processing and analysis directly on edge devices before data leaves the user's device. Machine learning models are pre-loaded on edge devices to enable immediate processing of mental health data, generating insights locally without waiting for cloud server availability. This preliminary action significantly reduces data processing latency while cloud infrastructure continues to provide resource utilization benefits for batch processing and model updates.
3Speed
If edge computing devices are used for local data processing, then data privacy and processing speed are improved, but device computational resources are constrained
Solution Approach 1:
The system implements partial edge computing by deploying only essential machine learning models and processing functions on resource-constrained edge devices. Rather than attempting to run complete analysis pipelines locally, the edge devices perform initial data filtering, feature extraction, and basic classification, then transmit only necessary processed data to cloud systems for more complex analysis. This partial action approach enables improved processing speed for time-critical functions while acknowledging the computational resource constraints of edge devices.
Data Source
AI summary
In an embodiment, a computer-implemented method comprises using a computer system comprising one or more edge devices in a local computing environment, programming a first edge device to operate as an edge computing node, wherein the edge devices are configured to execute operations without requiring telecommunications to a remote server via an internet; and by the first edge device: identifying second edge devices being activated in the local computing environment, adding the first and second edge devices in an edge network, retrieving data from the second edge devices via the edge network, wherein the retrieved data comprises sensor data collected by the second edge devices, generating (at the first edge device) data summaries from the retrieved data, storing the data summaries in a storage associated with the first edge device, and presenting the data summaries via an application executing on the first edge device.