Parkinson's disease (PD) poses challenges in diagnosis and monitoring due to its progressive nature and complex symptoms. This study introduces a novel approach utilizing surface electromyography (sEMG) to objectively assess PD severity, focusing on the biceps brachii muscle. Initial analysis of sEMG data from five PD patients and five healthy controls revealed significant neuromuscular differences. A traditional Support Vector Machine (SVM) model achieved up to 83% accuracy, while enhancements with a Graph Convolutional Network-Support Vector Machine (GCN-SVM) model increased accuracy to 92%. Despite the preliminary nature of these results, the study outlines a detailed experimental methodology for future research with larger cohorts to validate these findings and integrate the approach into clinical practice. The proposed approach holds promise for advancing PD severity assessment and improving patient care in Parkinson's disease management.

Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accuracy, particularly by leveraging wearable sensor data. This survey comprehensively reviews current ML methodologies used in classifying Parkinsonian tremors, evaluating various tremor data acquisition methodologies, signal preprocessing techniques, and feature selection methods across time and frequency domains, highlighting practical approaches for tremor classification. The survey explores ML models utilized in existing studies, ranging from traditional methods such as Support Vector Machines (SVM) and Random Forests to advanced deep learning architectures like Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM). We assess the efficacy of these models in classifying tremor patterns associated with PD, considering their strengths and limitations. Furthermore, we discuss challenges and discrepancies in current research and broader challenges in applying ML to PD diagnosis using wearable sensor data. We also outline future research directions to advance ML applications in PD diagnostics, providing insights for researchers and practitioners.

In a study from Oxford University, researchers found that by using a combination of wearable sensor data and machine learning algorithms the progression of Parkinson's disease can be monitored more accurately than in traditional clinical observation. Monitoring movement data collected by sensor technology may not only improve predictions about disease progression but also allows for more precise diagnoses. Parkinson's disease is a neurological condition that affects motor control and movement. Although there is currently no cure, early intervention can help delay the progression of the disease in patients. Diagnosing and tracking the progression of Parkinson's disease currently involves a neurologist using the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the patient's motor symptoms by assigning scores to the performance of specific movements.

Researchers at MIT have developed an AI system that can diagnose Parkinson's disease and track its progression, simply by monitoring someone's breathing patterns as they sleep. The device looks like an internet router and can be mounted on the wall in a bedroom. It emits radio waves and then a neural network analyzes the reflected waves to assess breathing patterns. Crucially, the technology may be able to assist in diagnosing Parkinson's disease much earlier than many conventional techniques and it is highly convenient and non-invasive compared with traditional diagnostics. It may also be particularly beneficial in testing new treatments for Parkinson's as a non-invasive method to monitor disease progression.

A new MIT-developed artificial intelligence model can make an early detection of Parkinson's Disease -- which is notoriously hard to diagnose -- from a person's breathing patterns, the university announced Monday. A news release about the technology said that Parkinson's disease is hard to diagnose because it relies primarily on the appearance of motor symptoms, such as tremors, stiffness, and slowness, which often appear several years after the disease onset. But Dina Katabi, an MIT electrical engineering and computer science professor, and her team have now developed an artificial intelligence model that can detect Parkinson's from a person's breathing patterns, the release said. The tech is a neural network -- a series of connected algorithms that mimic the way a human brain works -- capable of assessing whether someone has Parkinson's from how they breathe while they sleep. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's and track the progression of their disease over time, the release said.

Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang is first author on a new paper describing the work, published today in Nature Medicine.

A compelling new study indicates Parkinson's disease (PD) could be diagnosed by remotely tracking a person's breathing patterns. Led by researchers from MIT, the study presents an AI system that uses radio waves to monitor breathing while a person sleeps. Dina Katabi, principal investigator on the new research, said the study was inspired by 200-year-old observations from James Parkinson, the first doctor to clinically catalog signs of the degenerative neurological disease. "A relationship between Parkinson's and breathing was noted as early as 1817, in the work of Dr. James Parkinson," explained Katabi. "This motivated us to consider the potential of detecting the disease from one's breathing without looking at movements. Some medical studies have shown that respiratory symptoms manifest years before motor symptoms, meaning that breathing attributes could be promising for risk assessment prior to Parkinson's diagnosis."

Parkinson's disease is notoriously difficult to diagnose as it relies primarily on the appearance of motor symptoms such as tremors, stiffness, and slowness, but these symptoms often appear several years after the disease onset. Now, Dina Katabi, the Thuan (1990) and Nicole Pham Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT and principal investigator at MIT Jameel Clinic, and her team have developed an artificial intelligence model that can detect Parkinson's just from reading a person's breathing patterns. The tool in question is a neural network, a series of connected algorithms that mimic the way a human brain works, capable of assessing whether someone has Parkinson's from their nocturnal breathing -- i.e., breathing patterns that occur while sleeping. The neural network, which was trained by MIT PhD student Yuzhe Yang and postdoc Yuan Yuan, is also able to discern the severity of someone's Parkinson's disease and track the progression of their disease over time. Yang and Yuan are co-first authors on a new paper describing the work, published today in Nature Medicine.

Autism is a serious developmental disorder that impairs the ability to communicate and interact. Autism spectrum disorder impacts the nervous system and affects the overall cognitive, emotional, social and physical health of the affected individual. According to a new study from the Stanford University School of Medicine, brain organisation differs between boys and girls with autism. The study was published in'The British Journal of Psychiatry'. The differences, identified by analyzing hundreds of brain scans with artificial intelligence techniques, were unique to autism and not found in typically developing boys and girls.

Researchers have developed algorithms that work with wearable sensors to continuously monitor tremor, and estimate total tremor, in Parkinson's patients as they go about their daily routines. Analyses of sensor results using one algorithm, in particular, were similar to an established test assessing tremor without being dependent on the time the test is given. The study, "Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements," was published in Sensors. Resting tremor, or the rhythmic shaking of muscles while relaxed, is among the motor symptoms of Parkinson's disease (PD), and some patients also have active tremor, or shaking while engaged in voluntary muscle movement. Others motor symptoms are slowness of movement (bradykinesia), rigidity, and problems with posture, balance, and gait.