Tensor Decomposition of Gait Dynamics in Parkinson's Disease

Conclusion: Tensor decomposition is a useful method for the modeling and analysis of multisensor time series in patients with Parkinson's disease. Significance: Tensor-decomposition factors can be potentially used as physiological markers for Parkinson's disease, and effective features for machine learning that can provide early prediction of the disease progression.
Source: IEEE Transactions on Biomedical Engineering - Category: Biomedical Engineering Source Type: research