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.

Surface electromyography (sEMG) non-invasively measures signals generated by muscle activity with sufficient sensitivity to detect individual spinal neurons and richness to identify dozens of gestures and their nuances. Wearable wrist-based sEMG sensors have the potential to offer low friction, subtle, information rich, always available human-computer inputs. To this end, we introduce emg2qwerty, a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard, together with ground-truth annotations and reproducible baselines. With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date. These data demonstrate non-trivial, but well defined hierarchical relationships both in terms of the generative process, from neurons to muscles and muscle combinations, as well as in terms of domain shift across users and user sessions.

In order to achieve it, we proposed and developed the classification framework EasiCS to obtain the relative stability The cervical spondylosis(CS), a common degenerative clustering results, which consists of dimension reduction, disease, harms human life and health, affects up clustering algorithm EasiSOM, spectral clustering algorithm to two-thirds of the population, and poses an serious EasiSC as shown in the Figure 1. To the best of our burden on individuals and society (Matz et al. 2009; knowledge, the EasiCS is the first effort to utilize the clustering Kotil and Bilge 2008; Cai et al. 2016; Nana Wang; algorithm and sEMG. Compared with the seven commonly Wang et al. 2018). Currently, the neck disability index used clustering algorithms, the novelty framework (Howard Vernon) is the most commonly used tool EasiCS provide the best overall performance. The cervical to assess the neck dysfunction (Vernon and Mior 1991), spondylosis(CS), a common degenerative disease, harms human The availability of which are mainly undermined by the life and health, affects up to two-thirds of the population, coarse-grained and unreasonable classification, despite that and poses an serious burden on individuals and society the NDI information is subjective and not accurate enough.