Automatic Segmentation of Stabilometric Signals Using Hidden Markov Model Regression

Posture analysis in quiet standing is an essential element in evaluating human balance control. Many factors enhance the human control system’s ability to maintain stability, such as the visual system and base of support (feet) placement. In contrast, many neural pathologies, such as Parkinson’s disease (PD) and cerebellar disorder, disturb human stability. This paper addresses the problem of the automatic segmentation of stabilometric signals recorded under four different conditions related to vision and foot position. This is achieved for both control subjects and PD subjects. A hidden Markov model (HMM)-regression-based approach is used to carry out the segmentation between the different conditions using simple and multiple regression processes. Twenty-eight control subjects and thirty-two PD subjects participated in this study. They were asked to stand upright while recording stabilometric signals in mediolateral and anteroposterior directions under two permutations: feet apart and together with eyes open or closed. The results show high values for the correct segmentation rates, up to 98%, for the separation between the different conditions. The present findings could help clinicians better understand the motor strategies used by the patients during their orthostatic postures and may guide the rehabilitation process. The proposed method compares favorably with standard segmentation approaches.Note to Practitioners—In this paper, the problem of human balance control assessment is analyzed through the segmentation of the multidimensional time series of the center of pressure (CoP) displacement measurements during orthostatic postures of healthy and Parkinsonian subjects. The proposed model for automatic temporal segmentation is a specific statistical latent process model that assumes that the observed stabilometric sequence is governed by a sequence of hidden (unobserved) states or conditions. More specifically, the proposed approach is based on a specific multiple regression model that incorporates a hidden Markov process that governs the switching from one condition to another over time. The model is learned in an unsupervised context by maximizing the observed data log-likelihood via a dedicated expectation–maximization algorithm. We applied it on a real-world automatic CoP displacement excursion segmentation problem and assessed its performance by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard HMM. The results obtained are very encouraging and show that the proposed approach is quite competitive, though it works in an entirely unsupervised fashion and does not requires a feature extraction preprocessing step. The present findings could help clinicians to better understand the motor strategies used by the patients during their orthostatic postures and may guide the rehabilitation process.