TY - GEN
T1 - On-Device Machine Learning for Diagnosis of Parkinson's Disease from Hand Drawn Artifacts
AU - Venkata, Sai Vaibhav Polisetti
AU - Sabat, Shubhankar
AU - Deshpande, Chinmay Anand
AU - Arefeen, Asiful
AU - Peterson, Daniel
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Effective diagnosis of neuro-degenerative diseases is critical to providing early treatments, which in turn can lead to substantial savings in medical costs. Machine learning models can help with the diagnosis of such diseases like Parkinson's and aid in assessing disease symptoms. This work introduces a novel system that integrates pervasive computing, mobile sensing, and machine learning to classify hand-drawn images and provide diagnostic insights for the screening of Parkinson's disease patients. We designed a computational framework that combines data augmentation techniques with optimized convolutional neural network design for on-device and real-time image classification. We assess the performance of the proposed system using two datasets of images of Archimedean spirals drawn by hand and demonstrate that our approach achieves 76% and 83% accuracy respectively. Thanks to 4x memory reduction via integer quantization, our system can run fast on an Android smartphone. Our study demonstrates that pervasive computing may offer an inexpensive and effective tool for early diagnosis of Parkinson's disease1.
AB - Effective diagnosis of neuro-degenerative diseases is critical to providing early treatments, which in turn can lead to substantial savings in medical costs. Machine learning models can help with the diagnosis of such diseases like Parkinson's and aid in assessing disease symptoms. This work introduces a novel system that integrates pervasive computing, mobile sensing, and machine learning to classify hand-drawn images and provide diagnostic insights for the screening of Parkinson's disease patients. We designed a computational framework that combines data augmentation techniques with optimized convolutional neural network design for on-device and real-time image classification. We assess the performance of the proposed system using two datasets of images of Archimedean spirals drawn by hand and demonstrate that our approach achieves 76% and 83% accuracy respectively. Thanks to 4x memory reduction via integer quantization, our system can run fast on an Android smartphone. Our study demonstrates that pervasive computing may offer an inexpensive and effective tool for early diagnosis of Parkinson's disease1.
KW - Machine learning
KW - Parkinson's disease
KW - convolutional neural networks
KW - mobile sensing
KW - pervasive computing
UR - http://www.scopus.com/inward/record.url?scp=85142228429&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142228429&partnerID=8YFLogxK
U2 - 10.1109/BSN56160.2022.9928465
DO - 10.1109/BSN56160.2022.9928465
M3 - Conference contribution
AN - SCOPUS:85142228429
T3 - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
BT - BHI-BSN 2022 - IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022
Y2 - 27 September 2022 through 30 September 2022
ER -