Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Borzì, Luigi; Mazzetta, Ivan; Zampogna, Alessandro; Suppa, Antonio; Olmo, Gabriella; Irrera, Fernanda

doi:10.3390/s21020614

Cited by 68 publications

(38 citation statements)

References 46 publications

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“…For the window-based evaluation, there was very little difference in model performance for all detection models, across merging thresholds, and a slight difference in performance for prediction models. Model performance was similar to other person-independent FOG detection [ 32 , 46 , 51 , 57 , 58 ] and prediction [ 55 , 59 – 61 ] models in the literature.…”

Section: Discussionsupporting

confidence: 69%

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease

et al. 2021

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Freezing of gait (FOG) is an intermittent walking disturbance experienced by people with Parkinson’s disease (PD). Wearable FOG identification systems can improve gait and reduce the risk of falling due to FOG by detecting FOG in real-time and providing a cue to reduce freeze duration. However, FOG prediction and prevention is desirable. Datasets used to train machine learning models often generate ground truth FOG labels based on visual observation of specific lower limb movements (event-based definition) or an overall inability to walk effectively (period of gait disruption based definition). FOG definition ambiguity may affect model performance, especially with respect to multiple FOG in rapid succession. This research examined whether merging multiple freezes that occurred in rapid succession could improve FOG detection and prediction model performance. Plantar pressure and lower limb acceleration data were used to extract a feature set and train decision tree ensembles. FOG was labeled using an event-based definition. Additional datasets were then produced by merging FOG that occurred in rapid succession. A merging threshold was introduced where FOG that were separated by less than the merging threshold were merged into one episode. FOG detection and prediction models were trained for merging thresholds of 0, 1, 2, and 3 s. Merging slightly improved FOG detection model performance; however, for the prediction model, merging resulted in slightly later FOG identification and lower precision. FOG prediction models may benefit from using event-based FOG definitions and avoiding merging multiple FOG in rapid succession.

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Section: Discussionsupporting

confidence: 69%

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease

et al. 2021

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“…Advanced gait-assessment techniques processed in this way are also able to predict in which patients FoG may arise based on gait parameters, allowing therapies to be targeted early. 106 …”

Section: Key Associations With Gait Dysfunction In Pdmentioning

confidence: 99%

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

Smith¹,

Brazier²,

Henderson³

2021

NDT

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Gait dysfunction is a key defining feature of Parkinson’s disease (PD), and is associated with symptoms of freezing and an increased risk of falls. In this narrative review, we cover the putative mechanisms of gait dysfunction in PD, the assessment of gait abnormalities, and the management of symptoms caused by the inherent difficulty in walking. Our understanding of the causes of gait problems in PD has progressed in recent times, moving from neurocognitive theory to correlates of affected neuronal pathways. In particular, this can be shown to correspond with abnormalities in responses to dual-task paradigms and dysfunction in cholinergic signaling. Great progress has been made in the sophistication and precision of gait assessment; however, it has firmly remained in the research domain. There is significant momentum behind wearable technologies that can be used by patients in their own environment, acting as digital biomarkers that can not only reflect progression but also independently discriminate PD from non-PD individuals. The treatment of gait dysfunction has historically relied on physical therapies and training combined with a view to mitigating the impact of such consequences as falls. Pharmacological therapies that are the mainstay of treatment in PD have tended to address symptoms like bradykinesia; however, optimization of dopaminergic therapies likely has a positive effect on quality of gait. Other targets have been assessed with the goal of improving gait, of which medications that improve cholinergic signaling appear most promising. Neuromodulation techniques are increasingly used in the form of deep-brain stimulation; however, standard targets, such as the globus pallidus interna, have a modest effect on gait. Considerable benefit has been seen through targeting the pedunculopontine nucleus, and a dual-target approach may be warranted. Stimulation of the spinal cord and brain through direct or magnetic approaches has been assessed, but requires further evidence.

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“…While 90% or greater specificity would be ideal, specificity below this threshold is common in the FOG prediction literature. Specificities of 67.0% [21], 80.25% [44], and recently 86% [45] have been reported.…”

Section: Discussionmentioning

confidence: 99%

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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Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

Cited by 68 publications

References 46 publications

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease

Grouping successive freezing of gait episodes has neutral to detrimental effect on freeze detection and prediction in Parkinson’s disease

Current Perspectives on the Assessment and Management of Gait Disorders in Parkinson’s Disease

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

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