Automatic Gait Phases Detection in Parkinson Disease: A Comparative Study

摘要

Background: Parkinson's disease (PD) is a chronic condition that can be diagnosed and monitored by evaluating changes in the gait and arm movement parameters. In the gait movement, each cycle consists of two phases: stance and swing. Using gait analysis techniques, it is possible to get spatiotemporal variables derived from both phases. Objective: In this paper, we compared two techniques: wavelet and peak detection. Previously, the wavelet technique was assessed for the gait phases detection, and peak detection was evaluated for arm swing analysis. These methods were evaluated using a low-cost RGB-D camera as data input source. This comparison could provide a unified and integrated method to analyze gait and arm swing signals. Methods: Twenty-five PD patients and 25 age-matched, healthy subjects were included. Mann-Whitney U test was used to compare the continuous variables between groups. Hamming distances and Spearman rank correlation were used to evaluate the agreement between the signals and the spatiotemporal variables obtained by both methods. Results: PD group showed significant reductions in speed (wavelet p = 0.001, peak detection p <0.001) and significantly greater swing (wavelet p = 0.003, peak detection p =0.005) and stance times (wavelet p = 0.003, peak detection p =0.004). Hamming distances showed small differences between the signals obtained by both methods (16 to 18 signal points). A very strong correlation (Spearman rho > 0.8, p <0.05) was found between the spatiotemporal variables obtained by each signal processing technique. Conclusion: Wavelet and peak detection techniques showed a high agreement in the signal obtained from gait data. The spatiotemporal variables obtained by both methods showed significant differences between the walking patterns of PD patients and healthy subjects. The peak detection technique can be used for integral motion analysis, providing the identification of the phases in the gait cycle, and arm swing parameters.Clinical Relevance— this establishes that peaks and wavelet techniques are comparable and may use it interchangeably to process signals from the gait of Parkinson's disease patients to support diagnosis and follow up made by a clinical expert.