Time-frequency representations from inertial sensors to characterize the gait in Parkinson’s disease
Parkinson’s Disease (PD) is a neurodegenerative disorder of the central nervous system whose main symptoms include rigidity, bradykinesia, and loss of postural reflexes. PD diagnosis is based on an analysis of the medical record and physical examinations of the patient. Besides, the neurological state of patients is monitored with subjective evaluations by neurologists. Gait analysis using inertial sensors was introduced as a simple and useful tool that supports the diagnosis and monitoring of PD patients. This work used the eGaIT system to capture the signals of the accelerometer and the gyroscope of the gait in order to evaluate the motor skills of patients. Fourier and wavelet transform were used to extract measurements based on energy and entropy in the time-frequency domain. The extracted characteristics were used to recognize differences between PD patients and healthy individuals. The results enabled to classify said groups with an accuracy of up to 94%.
 A. A. Moustafa et al., “Motor symptoms in Parkinson’s disease: A unified framework,” Neurosci. Biobehav. Rev., vol. 68, pp. 727–740, Sep. 2016.
 C. G. Goetz et al., “Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): Scale presentation and clinimetric testing results,” Mov. Disord., vol. 23, no. 15, pp. 2129–2170, Nov. 2008.
 R. B. Postuma et al., “MDS clinical diagnostic criteria for Parkinson’s disease,” Mov. Disord., vol. 30, no. 12, pp. 1591–1601, Oct. 2015.
 M. E. Morris, F. Huxham, J. McGinley, K. Dodd, and R. Iansek, “The biomechanics and motor control of gait in Parkinson disease,” Clin. Biomech., vol. 16, no. 6, pp. 459–470, Jul. 2001.
 J. Barth et al., “Combined analysis of sensor data from hand and gait motor function improves automatic recognition of Parkinson’s disease,” in International Conference of the Engineering in Medicine and Biology Society (EMBC), 2012, pp. 5122–5125.
 B. Mariani, M. C. Jiménez, F. J. G. Vingerhoets, and K. Aminian, “On-Shoe Wearable Sensors for Gait and Turning Assessment of Patients With Parkinson’s Disease,” IEEE Trans. Biomed. Eng., vol. 60, no. 1, pp. 155–158, Jan. 2013.
 E. Sejdic, K. A. Lowry, J. Bellanca, M. S. Redfern, and J. S. Brach, “A Comprehensive Assessment of Gait Accelerometry Signals in Time, Frequency and Time-Frequency Domains,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 22, no. 3, pp. 603–612, May 2014.
 J. C. M. Schlachetzki et al., “Wearable sensors objectively measure gait parameters in Parkinson’s disease,” PLoS One, vol. 12, no. 10, p. e0183989, Oct. 2017.
 J. C. Vásquez-Correa et al., “Multi-view representation learning via gcca for multimodal analysis of Parkinson’s disease,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 2966–2970.
 A. Martínez-Ramírez et al., “Frailty assessment based on trunk kinematic parameters during walking,” J. Neuroeng. Rehabil., vol. 12, no. 1, p. 48, Dec. 2015.
 B. Boashash, “Time-frequency signal analysis and processing: a comprehensive reference,” in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, 2nd ed., Academic Press, 2015, pp. 65–100.
 J. R. Orozco-Arroyave et al., “NeuroSpeech: An open-source software for Parkinson’s speech analysis,” Digit. Signal Process., vol. 77, pp. 207–221, Jun. 2018.
 L. Cohen, “Time-frequency distributions-a review,” Proc. IEEE, vol. 77, no. 7, pp. 941–981, Jul. 1989.
 S. Mallat, A wavelet tour of signal processing: the sparse way, 3rd ed. Academic press, 2008.
 C. K. Chui, An introduction to wavelets. Academic Press, 2016.
 T. Arias-Vergara, J. C. Vásquez-Correa, and J. R. Orozco-Arroyave, “Parkinson’s Disease and Aging: Analysis of Their Effect in Phonation and Articulation of Speech,” Cognit. Comput., vol. 9, no. 6, pp. 731–748, Dec. 2017
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