OMAD: On-device Mental Anomaly Detection for Substance and Non-Substance Users

by   Emon Dey, et al.

Stay at home order during the COVID-19 helps flatten the curve but ironically, instigate mental health problems among the people who have Substance Use Disorders. Measuring the electrical activity signals in brain using off-the-shelf consumer wearable devices such as smart wristwatch and mapping them in real time to underlying mood, behavioral and emotional changes play striking roles in postulating mental health anomalies. In this work, we propose to implement a wearable, On-device Mental Anomaly Detection (OMAD) system to detect anomalous behaviors and activities that render to mental health problems and help clinicians to design effective intervention strategies. We propose an intrinsic artifact removal model on Electroencephalogram (EEG) signal to better correlate the fine-grained behavioral changes. We design model compression technique on the artifact removal and activity recognition (main) modules. We implement a magnitude-based weight pruning technique both on convolutional neural network and Multilayer Perceptron to employ the inference phase on Nvidia Jetson Nano; one of the tightest resource-constrained devices for wearables. We experimented with three different combinations of feature extractions and artifact removal approaches. We evaluate the performance of OMAD in terms of accuracy, F1 score, memory usage and running time for both unpruned and compressed models using EEG data from both control and treatment (alcoholic) groups for different object recognition tasks. Our artifact removal model and main activity detection model achieved about ≈ 93% and 90% accuracy, respectively with significant reduction in model size (70%) and inference time (31%).


page 1

page 5


Detecting Anomalous User Behavior in Remote Patient Monitoring

The growth in Remote Patient Monitoring (RPM) services using wearable an...

Mental State Recognition via Wearable EEG

The increasing quality and affordability of consumer electroencephalogra...

Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices

Photoplethysmogram (PPG) signals are easily contaminated by motion artif...

Mental Health and Sensing

Mental health is a global epidemic, affecting close to half a billion pe...

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communi...

Enabling High-Accuracy Human Activity Recognition with Fine-Grained Indoor Localization

While computers play an increasingly important role in every aspect of o...

Please sign up or login with your details

Forgot password? Click here to reset