Proof of Concept in Artificial-Intelligence-Based Wearable Gait Monitoring for Parkinson's Disease Management Optimization

 Nowadays, the research is oriented towards AI-based wearables for early diagnosis and Parkinson's disease monitoring. Our objective is the monitoring and assessment of gait in PD patients. We tried to classify gait patterns assessed by means of correlation using convolutional neural networks. 

 Goal and proposed solution: Wearable sensors have the potential to revolutionize the healthcare industry by reducing many types of diseases to mathematical decisions. The data collected by wearables can be easily classified with a well-trained AI model and provide a specific diagnosis that can be difficult to provide without computer intelligence. Our project goal is to be able to precisely classify Parkinson's disease from the data we collect and create a smart and easy-to-use solution to implement in the medical sector. 

 Solution: Our solution is a proposed wearable miniature physiograph with AI decisional support for gait monitoring and assessment in Parkinson's disease. 

 The Physiograph: The wearable physiograph consists of a bunch of sensors that have the purpose of collecting gait data from people, including three plantar pressure sensors for each leg, two EMG channels for each leg, and one accelerometer mounted on the user's dominant wrist.

Data: The data for this project was collected from a study group consisting of eleven patients diagnosed with Parkinson's disease and three healthy people. For each person from the study group, we generated a set of images representing a coefficient matrix surface plot to visualize the biomechanical and temporal parameters of gait. 

AI-Decisional support: The AI decisional support consists of an AI model that is trained to classify the images generated from the data. To find the best model, we compared 3 architectures, MobileNet, EfficientNetB0 and Xception. 

Generative Adversarial Networks (GAN): To increase the training dataset, we proposed a conditional deep convolutional generative adversarial network to generate images. The network is designed to generate 512x512 images of healthy individuals as well as patients with neurodegenerative disease. 

 

Conclusion: In conclusion, our project has a significant potential to be successful in classifying Parkinson's disease in incipient stages before any visible symptoms, which could substantially extend a patient's life. To strengthen our project, we propose complementary solutions such as classifying writing, voice, and even facial muscle behavior, which would give doctors a much better diagnosis of the disease.