Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance

Pardoel, Scott; Shalin, Gaurav; Nantel, Julie; Lemaire, Edward D.; Kofman, Jonathan

doi:10.1109/embc44109.2020.9176607

Cited by 7 publications

(11 citation statements)

References 25 publications

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“…To create a multi-modal wearable system that is also comfortable to wear, it is likely that only the most robust signals from each modality will be included. Some progress has been made in identifying specific gait parameters that are the best for recognising abnormal steps ( O’Day et al, 2020 ), and also in minimising intrusiveness of such devices, for example, the use of pressure-sensing insoles that were able to detect FOG in high agreement with clinical ratings ( Pardoel et al, 2020 ).…”

Section: What Approaches Could Help Us Identify a New Treatment?mentioning

confidence: 99%

Future Therapeutic Strategies for Freezing of Gait in Parkinson’s Disease

Cui

Lewis

2021

Front. Hum. Neurosci.

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Freezing of gait (FOG) is a common and challenging clinical symptom in Parkinson’s disease. In this review, we summarise the recent insights into freezing of gait and highlight the strategies that should be considered to improve future treatment. There is a need to develop individualised and on-demand therapies, through improved detection and wearable technologies. Whilst there already exist a number of pharmacological (e.g., dopaminergic and beyond dopamine), non-pharmacological (physiotherapy and cueing, cognitive training, and non-invasive brain stimulation) and surgical approaches to freezing (i.e., dual-site deep brain stimulation, closed-loop programming), an integrated collaborative approach to future research in this complex area will be necessary to systematically investigate new therapeutic avenues. A review of the literature suggests standardising how gait freezing is measured, enriching patient cohorts for preventative studies, and harnessing the power of existing data, could help lead to more effective treatments for freezing of gait and offer relief to many patients.

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Section: What Approaches Could Help Us Identify a New Treatment?mentioning

confidence: 99%

Future Therapeutic Strategies for Freezing of Gait in Parkinson’s Disease

Cui

Lewis

2021

Front. Hum. Neurosci.

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“…The features used in this research were based on [ 34 ] ( Table 1 ). In total, 861 individual features were extracted from the 71,067 data windows.…”

Section: Methodsmentioning

confidence: 99%

“…mRMR is a multivariate approach that selects features such that mutual information between a feature and class is maximized, while pairwise information between features is minimized [ 41 ]. mRMR has been used for FOG detection [ 34 , 35 ]. Relief-F incorporates interactions between features [ 42 ] and has been used in activity monitoring situations with plantar pressure data collected during walking [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, the pressure distribution may vary distinctly between phases of normal walking, during transition from normal walking into freeze, and during a freeze. Preliminary research using plantar pressure data for FOG detection and prediction has shown promise [ 33 , 34 ]; thus, plantar pressure analysis may open new avenues in predicting FOG events.…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Pardoel

Shalin

Nantel

et al. 2021

Sensors

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Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

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“…This partially reflects the fact that the accuracy of most IMU-algorithms partly relies on frequency-based analysis of the ‘signature’ high-frequency motions of FOG, as well as the sliding time windows implemented by these systems. To reduce this problem, high agreement with clinical video-ratings has been demonstrated in recent work on insoles that can record foot pressure data with a 3D sensor collected during standardized testing in the home and in the laboratory [ 19 ]. Therefore, this patient-friendly methodology holds great promise for detecting different phenotypes of FOG in the home environment, which could provide more real-world data for computational modeling, helping to explain the commonly observed heterogeneity potentially down to the level of specific triggers/relievers in the individual.…”

Section: Standardising Definitions Assessments and Measurementsmentioning

confidence: 99%

Stepping up to meet the challenge of freezing of gait in Parkinson’s disease

Lewis

Factor

Giladi

et al. 2022

Transl Neurodegener

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There has been a growing appreciation for freezing of gait as a disabling symptom that causes a significant burden in Parkinson’s disease. Previous research has highlighted some of the key components that underlie the phenomenon, but these reductionist approaches have yet to lead to a paradigm shift resulting in the development of novel treatment strategies. Addressing this issue will require greater integration of multi-modal data with complex computational modeling, but there are a number of critical aspects that need to be considered before embarking on such an approach. This paper highlights where the field needs to address current gaps and shortcomings including the standardization of definitions and measurement, phenomenology and pathophysiology, as well as considering what available data exist and how future studies should be constructed to achieve the greatest potential to better understand and treat this devastating symptom.

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Selection of Plantar-Pressure and Ankle-Acceleration Features for Freezing of Gait Detection in Parkinson's Disease using Minimum-Redundancy Maximum-Relevance

Cited by 7 publications

References 25 publications

Future Therapeutic Strategies for Freezing of Gait in Parkinson’s Disease

Future Therapeutic Strategies for Freezing of Gait in Parkinson’s Disease

Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Stepping up to meet the challenge of freezing of gait in Parkinson’s disease

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