Wearable Sensor Neural Network for Mental Health Detection
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Solution Overview
Problem
Current methods for detecting mental health disorders rely heavily on self-reported data and lack effective strategies for continuous monitoring, which can lead to delayed interventions and adverse outcomes.
Innovation Solution
A machine-learning based system utilizing wearable medical sensors (WMSs) and neural networks that bypass feature extraction, generates synthetic data, and employs a grow-and-prune paradigm to create efficient mental health disorder inference models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on raw physiological data from wearable sensors, then detection accuracy is improved, but computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts and removes the feature extraction step from the traditional machine learning pipeline. Instead of extracting features manually and then training models on those features, the system trains neural networks directly on raw physiological sensor data, eliminating the complex feature extraction intermediary layer while maintaining high detection accuracy
Solution Approach 2:
The patent introduces synthetic data as an intermediary to bridge the gap between limited real patient data and the need for extensive training data. The synthetic data generator creates virtual patient datasets that mimic real physiological patterns, allowing the neural networks to be trained on a larger, more diverse dataset without collecting additional real patient information
2Productivity
If synthetic data is generated to augment training datasets, then model training efficiency is improved, but data generation computational cost increases
Solution Approach 1:
The patent performs preliminary actions by pre-training a synthetic data generator model on a small initial dataset of real patient information. Once this generator is trained, it can efficiently produce large amounts of synthetic training data without requiring continuous computational resources, as the generator can be deployed on edge devices or with minimal cloud computing
3Measurement precision
If neural networks process physiological data directly without feature extraction, then detection accuracy is improved, but data processing time increases
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically learn and extract relevant features from the raw physiological data during the training process. The model autonomously identifies important patterns and relationships in the sensor data without requiring manual feature engineering or pre-processing, thereby reducing both processing time and computational complexity
Data Source
AI summary
According to various embodiments, a machine-learning based system for mental health disorder identification and monitoring is disclosed. The system includes one or more processors configured to interact with a plurality of wearable medical sensors (WMSs). The processors are configured to receive physiological data from the WMSs. The processors are further configured to train at least one neural network based on raw physiological data augmented with synthetic data and subjected to a grow-and-prune paradigm to generate at least one mental health disorder inference model. The processors are also configured to output a mental health disorder-based decision by inputting the received physiological data into the generated mental health disorder inference model.