Dynamical Learning and Tracking of Tremor and Dyskinesia From Wearable Sensors

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 22; no. 5; pp. 982 - 991
• Main Authors: Cole, Bryan T., Roy, Serge H., De Luca, Carlo J., Nawab, S. Hamid
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerometer signals; Aged; Algorithms; Artificial Intelligence; dynamical machine learning; Dyskinesias - physiopathology; electromyographic (EMG) signals; Electromyography; Electromyography - methods; Electromyography - statistics & numerical data; Female; Finite impulse response filters; Heuristic algorithms; Hidden Markov models; Humans; Machine learning algorithms; Male; Markov Chains; Middle Aged; Movement - physiology; Neural Networks (Computer); Parkinson Disease - physiopathology; Parkinson's disease (PD); Reproducibility of Results; Sensors; Signal processing algorithms; Support Vector Machine; Tremor - physiopathology; tremor and dyskinesia
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2014.2310904

We have developed and evaluated several dynamical machine-learning algorithms that were designed to track the presence and severity of tremor and dyskinesia with 1-s resolution by analyzing signals collected from Parkinson's disease (PD) patients wearing small numbers of hybrid sensors with both 3-D accelerometeric and surface-electromyographic modalities. We tested the algorithms on a 44-h signal database built from hybrid sensors worn by eight PD patients and four healthy subjects who carried out unscripted and unconstrained activities of daily living in an apartment-like environment. Comparison of the performance of our machine-learning algorithms against independent clinical annotations of disorder presence and severity demonstrates that, despite their differing approaches to dynamic pattern classification, dynamic neural networks, dynamic support vector machines, and hidden Markov models were equally effective in keeping error rates of the dynamic tracking well below 10%. A common set of experimentally derived signal features were used to train the algorithm without the need for subject-specific learning. We also found that error rates below 10% are achievable even when our algorithms are tested on data from a sensor location that is different from those used in algorithm training.