Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson’s Disease Using Wearable Sensors

Abstract

Freezing of gait (FOG) is a disabling symptom that is common among patients with advanced Parkinson’s disease (PD). External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens) is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase) is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens). The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.

Clinical and methodological challenges for assessing freezing of gait: Future perspectives

“…In order for this online cueing modality to work, a “pre‐freezing phase” needs to be characterized reliably. A few studies have investigated the presence of a pre‐FOG phase in the laboratory using different apporaches, including motion analysis systems, surface EMG, electrocardiography, and, more recently, with mobile imaging such as ambulatory EEG, functional near‐infrared spectroscopy, and wearable inertial sensors . Nieuwboer and colleagues found signs of deterioration of the gait pattern as early as three steps before a FOG episode, reflected by a decreased stride length and increased or stable cadence .…”

“…Only few reports have characterized the pre‐FOG phase with inertial sensors . The results were promising, particularly in a study of 11 subjects with PD and FOG where information was combined from accelerometers and gyroscopes.…”

Is a Wearable Sensor-Based Characterisation of Gait Robust Enough to Overcome Differences Between Measurement Protocols? A Multi-Centric Pragmatic Study in Patients with Multiple Sclerosis

“…Abrupt change points and their locations were then searched in θ z (t) using a predefined Matlab ® function based on the minimisation of a linear computational cost function [50]. Resting breaks were automatically detected by checking in 2-s window increments if: (i) the norm of the lumbar IMU angular velocity was less than 0.5 rad/s; (ii) the norm of the lumbar IMU acceleration was within ±10% of 9.81 m/s 2 [51]. A 2-s window was considered motionless if more than 50% of its samples fulfilled both criteria mentioned above.…”

The turning and barrier course reveals gait parameters for detecting freezing of gait and measuring the efficacy of deep brain stimulation

“…Abbreviations FOG = freezing of gait; DBS = deep brain stimulation; STN = subthalamic nucleus; TBC = Turning and Barrier Course; FW = forward walking; Inertial Measurement Unit = IMU; FOG-Q = Freezing of Gait Questionnaire; LOOCV = leave-one-out cross validation; AUROC = Area Under Receiver Operator Curve; UPDRS = Unified Parkinson's Disease Rating Scale assessments, and clinic-based measurements (Barthel et al, 2016). The authors concluded that there is no "unique methodological tool that encompasses the entire complexity of FOG" and "further development of such an assessment tool requires understanding and thorough analysis of the specific FOG characteristics" (Barthel et al, 2016).Several studies have employed wearable inertial sensors in a variety of different tasks, such as turning 360 degrees in place for two minutes, walking around cones, or walking during dual tasking, to monitor, detect and predict FOG (Coste et al, 2014;Khemani et al, 2015;Kim et al, 2015;Kwon et al, 2014;Palmerini et al, 2017;Rezvanian and Lockhart, 2016;Silva de Lima et al, 2017;Zach et al, 2015). These tasks have improved the detection resolution of FOG but are either not representative of real-world environments or still require a clinical rater to detect freezing episodes, and cannot objectively measure gait impairment, such as arrhythmicity, that is correlated with FOG (Anidi et al, 2018;Hausdorff, 2009;Nantel et al, 2011;Plotnik and Hausdorff, 2008;Syrkin-Nikolau et al, 2017).…”

“…Several studies have employed wearable inertial sensors in a variety of different tasks, such as turning 360 degrees in place for two minutes, walking around cones, or walking during dual tasking, to monitor, detect and predict FOG (Coste et al, 2014;Khemani et al, 2015;Kim et al, 2015;Kwon et al, 2014;Palmerini et al, 2017;Rezvanian and Lockhart, 2016;Silva de Lima et al, 2017;Zach et al, 2015). These tasks have improved the detection resolution of FOG but are either not representative of real-world environments or still require a clinical rater to detect freezing episodes, and cannot objectively measure gait impairment, such as arrhythmicity, that is correlated with FOG (Anidi et al, 2018;Hausdorff, 2009;Nantel et al, 2011;Plotnik and Hausdorff, 2008;Syrkin-Nikolau et al, 2017).…”