Gait analysis in frequency domain for freezing detection in patients with Parkinson’s disease

Zabaleta, Haritz; Keller, Thierry; Fimbel, Eric

doi:10.4017/gt.2008.07.02.184.00

Cited by 8 publications

(13 citation statements)

References 7 publications

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“…In this final algorithm, a FoG episode is then determined to occur if FI>FTH and PI>PTH. The MBFA algorithm is the most advanced FoG detection algorithm due to its low computational cost and good performance [ 22 – 24 , 26 , 27 , 31 , 46 – 50 ].…”

Section: Related Workmentioning

confidence: 99%

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

et al. 2017

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Among Parkinson’s disease (PD) symptoms, freezing of gait (FoG) is one of the most debilitating. To assess FoG, current clinical practice mostly employs repeated evaluations over weeks and months based on questionnaires, which may not accurately map the severity of this symptom. The use of a non-invasive system to monitor the activities of daily living (ADL) and the PD symptoms experienced by patients throughout the day could provide a more accurate and objective evaluation of FoG in order to better understand the evolution of the disease and allow for a more informed decision-making process in making adjustments to the patient’s treatment plan. This paper presents a new algorithm to detect FoG with a machine learning approach based on Support Vector Machines (SVM) and a single tri-axial accelerometer worn at the waist. The method is evaluated through the acceleration signals in an outpatient setting gathered from 21 PD patients at their home and evaluated under two different conditions: first, a generic model is tested by using a leave-one-out approach and, second, a personalised model that also uses part of the dataset from each patient. Results show a significant improvement in the accuracy of the personalised model compared to the generic model, showing enhancement in the specificity and sensitivity geometric mean (GM) of 7.2%. Furthermore, the SVM approach adopted has been compared to the most comprehensive FoG detection method currently in use (referred to as MBFA in this paper). Results of our novel generic method provide an enhancement of 11.2% in the GM compared to the MBFA generic model and, in the case of the personalised model, a 10% of improvement with respect to the MBFA personalised model. Thus, our results show that a machine learning approach can be used to monitor FoG during the daily life of PD patients and, furthermore, personalised models for FoG detection can be used to improve monitoring accuracy.

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Section: Related Workmentioning

confidence: 99%

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

et al. 2017

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“…It is important to have good results in both the severity and effect of FoG detection and rating. In order to come out with successful management of PD patients with FoG, it is required to have attentive assessment and gait pattern analysis [ 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ].…”

Section: Related Research On Pd Advancement Through Technological mentioning

confidence: 99%

“…In Haritz et al . [ 75 ] research, he had proposed a method for freezing detection in PWP using gait analysis in the frequency domain. This pilot study used an accelerometer to measure the acceleration in 3 axes and a gyroscope to compute angular velocity in 2 axes in three locations of the lower extremities for monitoring foot, shank and thigh movements.…”

Section: Related Research On Pd Advancement Through Technological mentioning

confidence: 99%

“…Changes of the PSD towards higher frequency will be interpreted as FoG as the power of the signal above this frequency increase during FoG episodes. Results shown that sensor placement at the lower limbs (80.8% of FoG episodes detected correctly) and heel perpendicular to the coronal plane (82.7% of FoG episodes detected correctly) with a sensing magnitude of the angular velocity and acceleration respectively shown the best classification variables [ 75 ].…”

Section: Related Research On Pd Advancement Through Technological mentioning

confidence: 99%

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Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

Oung

Hariharan

Lee

et al. 2015

Sensors

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Parkinson’s Disease (PD) is characterized as the commonest neurodegenerative illness that gradually degenerates the central nervous system. The goal of this review is to come out with a summary of the recent progress of numerous forms of sensors and systems that are related to diagnosis of PD in the past decades. The paper reviews the substantial researches on the application of technological tools (objective techniques) in the PD field applying different types of sensors proposed by previous researchers. In addition, this also includes the use of clinical tools (subjective techniques) for PD assessments, for instance, patient self-reports, patient diaries and the international gold standard reference scale, Unified Parkinson Disease Rating Scale (UPDRS). Comparative studies and critical descriptions of these approaches have been highlighted in this paper, giving an insight on the current state of the art. It is followed by explaining the merits of the multiple sensor fusion platform compared to single sensor platform for better monitoring progression of PD, and ends with thoughts about the future direction towards the need of multimodal sensor integration platform for the assessment of PD.

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“…In [19], the authors developed a monitoring system that provided motor assessments by simultaneously analyzing current motor activity of the patients (e.g., sitting, walking) in a simulated daily living environment. Other related works performing accelerometer-based PD motor symptom assessment include [7], [17], [9].…”

Section: Introductionmentioning

confidence: 99%

Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

Das

Amoedo

Torre

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-In this paper, we propose to use a weakly supervised machine learning framework for automatic detection of Parkinson's Disease motor symptoms in daily living environments. Our primary goal is to develop a monitoring system capable of being used outside of controlled laboratory settings. Such a system would enable us to track medication cycles at home and provide valuable clinical feedback. Most of the relevant prior works involve supervised learning frameworks (e.g., Support Vector Machines). However, in-home monitoring provides only coarse ground truth information about symptom occurrences, making it very hard to adapt and train supervised learning classifiers for symptom detection. We address this challenge by formulating symptom detection under incomplete ground truth information as a multiple instance learning (MIL) problem. MIL is a weakly supervised learning framework that does not require exact instances of symptom occurrences for training; rather, it learns from approximate time intervals within which a symptom might or might not have occurred on a given day. Once trained, the MIL detector was able to spot symptom-prone time windows on other days and approximately localize the symptom instances. We monitored two Parkinson's disease (PD) patients, each for four days with a set of five triaxial accelerometers and utilized a MIL algorithm based on axis parallel rectangle (APR) fitting in the feature space. We were able to detect subject specific symptoms (e.g. dyskinesia) that conformed with a daily log maintained by the patients.

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Gait analysis in frequency domain for freezing detection in patients with Parkinson’s disease

Cited by 8 publications

References 7 publications

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Home detection of freezing of gait using support vector machines through a single waist-worn triaxial accelerometer

Technologies for Assessment of Motor Disorders in Parkinson’s Disease: A Review

Detecting Parkinsons' symptoms in uncontrolled home environments: A multiple instance learning approach

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