Abstract
Results Systems detected FOG and other gait postures and showed time-frequency range by examining differentiated decomposed signals by DWT. Energy distribution and PSD graph proved the accuracy of the system. Validation is done by the LOSO method which shows 90% accuracy for the proposed method. Conclusion Observations of the clinical trials validate the proposed technique. In comparison to the previous techniques reported in literature, it is seen that the proposed method shows improvement in time and frequency resolution as well as processing time.
Highlights
Parkinson’s disease (PD) is a neurogenerative disorder prevalent in persons above the age of 65 due to the loss of dopaminergic neurons in the basal ganglia [1]
After preprocessing the input signal, discrete wavelet transform (DWT) has been applied to the input accelerometer signal, and the procedure is continued until the computation of the DWT coefficient reached at level 6
DWT on acceleration sensor data which is sampled at 64 Hz has been illustrated
Summary
Parkinson’s disease (PD) is a neurogenerative disorder prevalent in persons above the age of 65 due to the loss of dopaminergic neurons in the basal ganglia [1]. This results in major cardinal movements like bradykinesia, stiffness, and distal tremor [2]. Freezing of gait (FOG) is the reduction or episodic absence of the forward progression of feet due to which PD patients cannot move their feet leading to falling [3, 4]. According to a survey report, about 6620 patients are suffering from FOG [4,5,6]
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