Gait Parameters Change Prior to Freezing in Parkinson's Disease: A Data-Driven Study with Wearable Inertial Units

Ferster, Maria Laura; Mazilu, Sinziana; Tröster, Gerhard

doi:10.4108/eai.28-9-2015.2261411

Cited by 19 publications

(18 citation statements)

References 26 publications

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“…This reflects a pattern of high frequency movements that is not only present when FOG is in place ( 4 , 6 – 13 ) but also just before it starts. This is in line with the results by Ferster et al ( 32 ), obtained on a subset of the subjects of this study.…”

Section: Discussionsupporting

confidence: 94%

“…The pre-FOG phase has been studied using different measurement systems, such as camera-based motion capture systems ( 26 , 27 ), electromyography ( 28 ), electroencephalography ( 29 ), functional near infrared spectroscopy ( 16 ), electrocardiography ( 17 , 30 ), and skin conductance ( 30 ). To the best of our knowledge, only two exploratory studies have used wearable inertial sensors to analyze the pre-FOG phase ( 31 , 32 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Discussionsupporting

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…Patients have the impression that their feet are glued to the ground [4] and they lose control over their gait. FOG is transient and lasts from a few seconds to up to 1–2 min [4,5]. This phenomenon happens mainly during gait initiation, turning, performing dual tasks or approaching narrow spaces [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…Even though current medical drugs can successfully relieve most of the motor symptoms, FOG is one of the least responsive to medical treatments [5]. However, external cueing to induce an external auditory or visual stimulus are promising for tackling FOG and helps resume natural gait for patients.…”

Section: Introductionmentioning

confidence: 99%

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Soltaninejad

Cheng

Basu

2019

Sensors

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Parkinson’s disease (PD) is one of the leading neurological disorders in the world with an increasing incidence rate for the elderly. Freezing of Gait (FOG) is one of the most incapacitating symptoms for PD especially in the later stages of the disease. FOG is a short absence or reduction of ability to walk for PD patients which can cause fall, reduction in patients’ quality of life, and even death. Existing FOG assessments by doctors are based on a patient’s diaries and experts’ manual video analysis which give subjective, inaccurate, and unreliable results. In the present research, an automatic FOG assessment system is designed for PD patients to provide objective information to neurologists about the FOG condition and the symptom’s characteristics. The proposed FOG assessment system uses an RGB-D sensor based on Microsoft Kinect V2 for capturing data for 5 healthy subjects who are trained to imitate the FOG phenomenon. The proposed FOG assessment system is called “Kin-FOG”. The analysis of foot joint trajectory of the motion captured by Kinect is used to find the FOG episodes. The evaluation of Kin-FOG is performed by two types of experiments, including: (1) simple walking (SW); and (2) walking with turning (WWT). Since the standing mode has features similar to a FOG episode, our Kin-FOG system proposes a method to distinguish between the FOG and standing episodes. Therefore, two general groups of experiments are conducted with standing state (WST) and without standing state (WOST). The gradient displacement of the angle between the foot and the ground is used as the feature for discriminating between FOG and standing modes. These experiments are conducted with different numbers of FOGs for getting reliable and general results. The Kin-FOG system reports the number of FOGs, their lengths, and the time slots when they occur. Experimental results demonstrate Kin-FOG has around 90% accuracy rate for FOG prediction in both experiments for different tasks (SW, WWT). The proposed Kin-FOG system can be used as a remote application at a patient’s home or a rehabilitation clinic for sending a neurologist the required FOG information. The reliability and generality of the proposed system will be evaluated for bigger data sets of actual PD subjects.

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“…An important forthcoming development is that of automatic detection and prediction of FOG (17,(78)(79)(80)(81)(82)(83). Reliable automatic FOG detection and prediction would enable the delivery of cues during or even prior to the start of FOG episodes ('cueing-on-demand'), as well as objective identification and quantification of FOG for research and clinical purposes.…”

Section: Future Perspectivesmentioning

confidence: 99%

Virtual visual cues : vice or virtue?

Janssen¹

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Gait Parameters Change Prior to Freezing in Parkinson's Disease: A Data-Driven Study with Wearable Inertial Units

Cited by 19 publications

References 26 publications

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

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

Kin-FOG: Automatic Simulated Freezing of Gait (FOG) Assessment System for Parkinson’s Disease

Virtual visual cues : vice or virtue?

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