Assessing joint time-frequency methods in the detection of dysfunctional movement

Movement disorders affect millions of people and lead to increased rates of mortality and morbidity in the elderly population. To explore new treatments and facilitate preventative medicine, researchers are actively studying the epidemiology of movement disorder and employing technology to help expose its symptoms. A wearable device, TEMPO, developed at the University of Virginia, has enabled the collection of inertial data that accurately and precisely quantifies symptoms and physical manifestations of dysfunctional movement. To effectively leverage this data, however, flexible and extensible signal processing is necessary. This paper demonstrates the utility of the Short-Time Fourier Transform and Haar Discrete Wavelet Transform in the detection of transient episodes of freezing behavior and tripping in simulated gait datasets. Results show an improvement in exposing the anomalous events over existing frequency-domain measures.