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

Assessing impaired bed mobility in patients with Parkinson's disease: a scoping review

BACKGROUND

Although most patients with Parkinson's disease (PD) experience difficulties in bed mobility, evidence on the suitability of the methods for assessing impaired bed mobility in PD are lacking.

OBJECTIVES

To identify objective methods for assessing impaired bed mobility in PD and to discuss their clinimetric properties and feasibility for use in clinical practice.

DATA SOURCES

PubMed, Web of Science, and Cochrane Library were searched between 1995 and 2022.

SELECTION CRITERIA

Studies were included if they described an objective assessment method for assessing impaired bed mobility in PD.

DATA EXTRACTION AND DATA SYNTHESIS

Characteristics of the identified measurement methods such as clinimetric properties and feasibility were extracted by two authors. The methodological quality of studies was evaluated using the Appraisal of studies tool.

RESULTS

Twenty-three studies were included and categorised into three assessment methods: sensor-based assessments (48%), rating scales (39%), and timed-tests (13%). The risk of bias was low for all but one study, which was medium.

LIMITATIONS

Despite applying wide selection criteria, a relatively small number of studies were identified in our results.

CONCLUSION

Rating scales may be the most preferred for assessing impaired bed mobility in PD in clinical practice, until clinimetric validity are adequately demonstrated in the other assessment methods. CONTRIBUTION OF PAPER.

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson

BACKGROUND AND OBJECTIVES

Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention.

METHOD

The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG.

RESULTS

Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained.

CONCLUSION

This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

The validation of a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q)

OBJECTIVE

This study aimed to develop a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q) and investigate its validity and reliability.

METHODS

After translating the NFOG-Q according to a standardised protocol, 56 patients with Parkinson's disease (PD) were administered it. Additionally, the MDS-UPDRS parts II and III, Hoehn and Yahr (H&Y) stage, and number of falls over 1 month were evaluated. Spearman's correlation coefficients (rho) were used to determine construct validity, and Cronbach's alpha (α) was used to examine reliability.

RESULTS

The interquartile range of the NFOG-Q scores was 10.0-25.3 (range 0-29). The NFOG-Q scores were strongly correlated with the MDS-UPDRS part II, items 2.12 (walking and balance), 2.13 (freezing), 3.11 (freezing of gait), and 3.12 (postural stability) and the postural instability and gait difficulty score (rho = 0.515-0.669), but only moderately related to the MDS-UPDRS item 3.10 (gait), number of falls, disease duration, H&Y stage, and time of the Timed Up-and-Go test (rho = 0.319-0.434). No significant correlations were observed between age and the time of the 10-m walk test. The internal consistency was excellent (α = 0.96).

CONCLUSIONS

The Japanese version of the NFOG-Q is a valid and reliable tool for assessing the severity of freezing in patients with PD.

Validity and reliability of a new clinician-rated tool for freezing of gait severity

PURPOSE

The Freezing of Gait Severity Tool (FOG Tool) was developed because of limitations in existing assessments. This cross-sectional study investigated its validity and reliability.

METHODS

People with Parkinson's disease (PD) were recruited consecutively from clinics. Those who could not walk eight-metres independently (with or without an assistive device), comprehend instructions, or with co-morbidities affecting walking were excluded. Participants completed a set of assessments including the FOG Tool, Timed Up and Go (TUG), and Freezing of Gait Questionnaire. The FOG Tool was repeated and those reporting no medication state change evaluated for test-retest reliability. Validity and reliability were investigated through Spearman's correlations and ICC (two-way, random). McNemar's test was applied to compare the FOG Tool and TUG on the proportion of people with freezing.

RESULTS

Thirty-nine participants were recruited [79.5%(n = 31) male; Median(IQR): age-73.0(9.0) years; disease duration-4.0(5.8) years]. Fifteen (38.5%) contributed to test-retest reliability analyses. The FOG Tool demonstrated strongest associations with the Freezing of Gait Questionnaire (ρ = 0.67, 95%CI 0.43-0.83). Test-retest reliability was excellent (ICC = 0.96, 95%CI 0.88-0.99). The FOG Tool had 6.2 times the odds (95%CI 2.4-20.4, p < 0.001) of triggering freezing compared to the TUG.

CONCLUSIONS

The FOG Tool appeared adequately valid and reliable in this small sample of people with PD. It was more successful in triggering freezing than the TUG.

Video-Based Detection of Freezing of Gait in Daily Clinical Practice in Patients With Parkinsonism

Freezing of gait (FoG) is a prevalent symptom among individuals with Parkinson's disease and related disorders. FoG detection from videos has been developed recently; however, the process requires using videos filmed within a controlled environment. We attempted to establish an automatic FoG detection method from videos taken in uncontrolled environments such as in daily clinical practices. Motion features of 16 patients were extracted from timed-up-and-go test in 109 video data points, through object tracking and three-dimension pose estimation. These motion features were utilized to form the FoG detection model, which combined rule-based and machine learning-based models. The rule-based model distinguished the frames in which the patient was walking from those when the patient has stopped, using the pelvic position coordinates; the machine learning-based model distinguished between FoG and stop using a combined one-dimensional convolutional neural network and long short-term memory (1dCNN-LSTM). The model achieved a high intraclass correlation coefficient of 0.75-0.94 with a manually-annotated duration of FoG and %FoG. This method is novel as it combines object tracking, 3D pose estimation, and expert-guided feature selection in the preprocessing and modeling phases, enabling FoG detection even from videos captured in uncontrolled environments.

Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson's disease

Introduction

Freezing of gait (FoG) is a significant issue for those with Parkinson's disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life.

Methods

This research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson's disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration.

Results

The decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG.

A machine learning contest enhances automated freezing of gait detection and reveals time-of-day effects

Freezing of gait (FOG) is a debilitating problem that markedly impairs the mobility and independence of 38-65% of people with Parkinson's disease. During a FOG episode, patients report that their feet are suddenly and inexplicably "glued" to the floor. The lack of a widely applicable, objective FOG detection method obstructs research and treatment. To address this problem, we organized a 3-month machine-learning contest, inviting experts from around the world to develop wearable sensor-based FOG detection algorithms. 1,379 teams from 83 countries submitted 24,862 solutions. The winning solutions demonstrated high accuracy, high specificity, and good precision in FOG detection, with strong correlations to gold-standard references. When applied to continuous 24/7 data, the solutions revealed previously unobserved patterns in daily living FOG occurrences. This successful endeavor underscores the potential of machine learning contests to rapidly engage AI experts in addressing critical medical challenges and provides a promising means for objective FOG quantification.

On-Demand Cueing for Freezing of Gait in Parkinson's Disease: A Randomized Controlled Trial

BACKGROUND

Cueing can alleviate freezing of gait (FOG) in people with Parkinson's disease (PD), but using the same cues continuously in daily life may compromise effectiveness. Therefore, we developed the DeFOG-system to deliver personalized auditory cues on detection of a FOG episode.

OBJECTIVES

We aimed to evaluate the effects of DeFOG during a FOG-provoking protocol: (1) after 4 weeks of DeFOG-use in daily life against an active control group; (2) after immediate DeFOG-use (within-group) in different medication states.

METHOD

In this randomized controlled trial, 63 people with PD and daily FOG were allocated to the DeFOG or active control group. Both groups received feedback on their daily living step counts using the device, but the DeFOG group also received on-demand cueing. Video-rated FOG severity was compared pre- and post-intervention through a FOG-provoking protocol administered at home off and on-medication, but without using DeFOG. Within-group effects were tested by comparing FOG during the protocol with and without DeFOG.

RESULTS

DeFOG-use during the 4 weeks was similar between groups, but we found no between-group differences in FOG-severity. However, the within-group analysis showed that FOG was alleviated by DeFOG (effect size d = 0.57), regardless of medication state. Combining DeFOG and medication yielded an effect size of d = 0.67.

CONCLUSIONS

DeFOG reduced FOG considerably in a population of severe freezers both off and on medication. Nonetheless, 4 weeks of DeFOG-use in daily life did not ameliorate FOG during the protocol unless DeFOG was worn. These findings suggest that on-demand cueing is only effective when used, similar to other walking aids. © 2024 International Parkinson and Movement Disorder Society.

Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model

Introduction

Accurately and objectively quantifying the clinical features of Parkinson's disease (PD) is crucial for assisting in diagnosis and guiding the formulation of treatment plans. Therefore, based on the data on multi-site motor features, this study aimed to develop an interpretable machine learning (ML) model for classifying the "OFF" and "ON" status of patients with PD, as well as to explore the motor features that are most associated with changes in clinical symptoms.

Methods

We employed a support vector machine with a recursive feature elimination (SVM-RFE) algorithm to select promising motion features. Subsequently, 12 ML models were constructed based on these features, and we identified the model with the best classification performance. Then, we used the SHapley Additive exPlanations (SHAP) and the Local Interpretable Model agnostic Explanations (LIME) methods to explain the model and rank the importance of those motor features.

Results

A total of 96 patients were finally included in this study. The naive Bayes (NB) model had the highest classification performance (AUC = 0.956; sensitivity = 0.8947, 95% CI 0.6686-0.9870; accuracy = 0.8421, 95% CI 0.6875-0.9398). Based on the NB model, we analyzed the importance of eight motor features toward the classification results using the SHAP algorithm. The Gait: range of motion (RoM) Shank left (L) (degrees) [Mean] might be the most important motor feature for all classification horizons.

Conclusion

The symptoms of PD could be objectively quantified. By utilizing suitable motor features to construct ML models, it became possible to intelligently identify whether patients with PD were in the "ON" or "OFF" status. The variations in these motor features were significantly correlated with improvement rates in patients' quality of life. In the future, they might act as objective digital biomarkers to elucidate the changes in symptoms observed in patients with PD and might be used to assist in the diagnosis and treatment of patients with PD.

Freezing of gait assessment with inertial measurement units and deep learning: effect of tasks, medication states, and stops

BACKGROUND

Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson's Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance.

METHODS

Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.02 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A temporal convolutional network (TCN) was used to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts' video annotation was assessed by the intra-class correlation coefficient (ICC).

RESULTS

For FOG assessment in trials without stopping, the agreement of our model was strong (ICC (%TF) = 0.92 [0.68, 0.98]; ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the agreement of our model was moderately strong (ICC (%TF) = 0.95 [0.73, 0.99]; ICC (#FOG) = 0.79 [0.46, 0.94]), but only when stopping was included in the training data.

CONCLUSION

A TCN trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.

Freezing of Gait Is Associated with Daily Activity Limitations among Individuals with Parkinson's Disease and Mild Cognitive Impairment

This study investigated the relationship between freezing of gait and daily activities among individuals with mild cognitive impairment due to Parkinson's disease by determining differences in caregiver-reported daily activity performance between individuals with and without freezing of gait. Cross-sectional baseline data from a longitudinal cohort study were used with 24 participants. Caregivers completed the Activities of Daily Living Questionnaire (ADLQ). Using a Mann-Whitney U test, findings indicated that participants with freezing of gait reported overall higher functional impairment levels on the ADLQ (p=.001), including the household, travel, self-care, employment and recreation, and communication subscores, indicating more perceived impairment. Findings show freezing of gait is associated with daily activity limitations in the home and the community among individuals with mild cognitive impairment due to Parkinson's disease. Clinicians should consider assessing freezing of gait, as early detection can inform the selection of interventions and strategies to minimize its impact on the performance of daily activities.

Detection and prediction of freezing of gait with wearable sensors in Parkinson's disease

Freezing of gait (FoG) is one of the most distressing symptoms of Parkinson's Disease (PD), commonly occurring in patients at middle and late stages of the disease. Automatic and accurate FoG detection and prediction have emerged as a promising tool for long-term monitoring of PD and implementation of gait assistance systems. This paper reviews the recent development of FoG detection and prediction using wearable sensors, with attention on identifying knowledge gaps that need to be filled in future research. This review searched the PubMed and Web of Science databases to collect studies that detect or predict FoG with wearable sensors. After screening, 89 of 270 articles were included. The data description, extracted features, detection/prediction methods, and classification performance were extracted from the articles. As the number of papers of this area is increasing, the performance has been steadily improved. However, small datasets and inconsistent evaluation processes still hinder the application of FoG detection and prediction with wearable sensors in clinical practice.

High-frequency rTMS over bilateral primary motor cortex improves freezing of gait and emotion regulation in patients with Parkinson's disease: a randomized controlled trial

Background

Freezing of gait (FOG) is a common and disabling phenomenon in patients with Parkinson's disease (PD), but effective treatment approach remains inconclusive. Dysfunctional emotional factors play a key role in FOG. Since primary motor cortex (M1) connects with prefrontal areas via the frontal longitudinal system, where are responsible for emotional regulation, we hypothesized M1 may be a potential neuromodulation target for FOG therapy. The purpose of this study is to explore whether high-frequency rTMS over bilateral M1 could relieve FOG and emotional dysregulation in patients with PD.

Methods

This study is a single-center, randomized double-blind clinical trial. Forty-eight patients with PD and FOG from the Affiliated Hospital of Xuzhou Medical University were randomly assigned to receive 10 sessions of either active (N = 24) or sham (N = 24) 10 Hz rTMS over the bilateral M1. Patients were evaluated at baseline (T0), after the last session of treatment (T1) and 30 days after the last session (T2). The primary outcomes were Freezing of Gait Questionnaire (FOGQ) scores, with Timed Up and Go Test (TUG) time, Standing-Start 180° Turn (SS-180) time, SS-180 steps, United Parkinson Disease Rating Scales (UPDRS) III, Hamilton Depression scale (HAMD)-24 and Hamilton Anxiety scale (HAMA)-14 as secondary outcomes.

Results

Two patients in each group dropped out at T2 and no serious adverse events were reported by any subject. Two-way repeated ANOVAs revealed significant group × time interactions in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14. Post-hoc analyses showed that compared to T0, the active group exhibited remarkable improvements in FOGQ, TUG, SS-180 turn time, SS-180 turning steps, UPDRS III, HAMD-24 and HAMA-14 at T1 and T2. No significant improvement was found in the sham group. The Spearman correlation analysis revealed a significantly positive association between the changes in HAMD-24 and HAMA-14 scores and FOGQ scores at T1.

Conclusion

High-frequency rTMS over bilateral M1 can improve FOG and reduce depression and anxiety in patients with PD.

Effects of Continuous Dopaminergic Stimulation on Parkinson's Disease Gait: A Longitudinal Prospective Study with Levodopa Intestinal Gel Infusion

Background

Gait issues, including reduced speed, stride length and freezing of gait (FoG), are disabling in advanced phases of Parkinson's disease (PD), and their treatment is challenging. Levodopa/carbidopa intestinal gel (LCIG) can improve these symptoms in PD patients with suboptimal control of motor fluctuations, but it is unclear if continuous dopaminergic stimulation can further improve gait issues, independently from reducing Off-time.

Objective

To analyze before (T0) and after 3 (T1) and 6 (T2) months of LCIG initiation: a) the objective improvement of gait and balance; b) the improvement of FoG severity; c) the improvement of motor complications and their correlation with changes in gait parameters and FoG severity.

Methods

This prospective, longitudinal 6-months study analyzed quantitative gait parameters using wearable inertial sensors, FoG with the New Freezing of Gait Questionnaire (NFoG-Q), and motor complications, as per the MDS-UPDRS part IV scores.

Results

Gait speed and stride length increased and duration of Timed up and Go and of sit-to-stand transition was significantly reduced comparing T0 with T2, but not between T0-T1. NFoG-Q score decreased significantly from 19.3±4.6 (T0) to 11.8±7.9 (T1) and 8.4±7.6 (T2) (T1-T0 p = 0.018; T2-T0 p < 0.001). Improvement of MDS-UPDRS-IV (T0-T2, p = 0.002, T0-T1 p = 0.024) was not correlated with improvement of gait parameters and NFoG-Q from T0 to T2. LEDD did not change significantly after LCIG initiation.

Conclusion

Continuous dopaminergic stimulation provided by LCIG infusion progressively ameliorates gait and alleviates FoG in PD patients over time, independently from improvement of motor fluctuations and without increase of daily dosage of dopaminergic therapy.

Soft robotic apparel to avert freezing of gait in Parkinson's disease

Freezing of gait (FoG) is a profoundly disruptive gait disturbance in Parkinson's disease, causing unintended stops while walking. Therapies for FoG reveal modest and transient effects, resulting in a lack of effective treatments. Here we show proof of concept that FoG can be averted using soft robotic apparel that augments hip flexion. The wearable garment uses cable-driven actuators and sensors, generating assistive moments in concert with biological muscles. In this n-of-1 trial with five repeated measurements spanning 6 months, a 73-year-old male with Parkinson's disease and substantial FoG demonstrated a robust response to robotic apparel. With assistance, FoG was instantaneously eliminated during indoor walking (0% versus 39 ± 16% time spent freezing when unassisted), accompanied by 49 ± 11 m (+55%) farther walking compared to unassisted walking, faster speeds (+0.18 m s-1) and improved gait quality (-25% in gait variability). FoG-targeting effects were repeatable across multiple days, provoking conditions and environment contexts, demonstrating potential for community use. This study demonstrated that FoG was averted using soft robotic apparel in an individual with Parkinson's disease, serving as an impetus for technological advancements in response to this serious yet unmet need.

Triggers for freezing of gait in individuals with Parkinson's disease: a systematic review

Background

Freezing of Gait (FOG) is a motor symptom frequently observed in advanced Parkinson's disease. However, due to its paroxysmal nature and diverse presentation, assessing FOG in a clinical setting can be challenging. Before FOG can be fully investigated, it is critical that a reliable experimental setting is established in which FOG can be evoked in a standardized manner, but the efficacy of various gait tasks and triggers for eliciting FOG remains unclear.

Objectives

This study aimed to conduct a systematic review of the existing literature and evaluate the available evidence for the relationship between specific motor tasks, triggers, and FOG episodes in individuals with Parkinson's disease (PwPD).

Methods

We conducted a literature search on four online databases (PubMed, Web of Science, EMBASE, and Cochrane Library) using the keywords "Parkinson's disease," "Freezing of Gait", "triggers" and "tasks". A total of 128 articles met the inclusion criteria and were included in our analysis.

Results

The review found that a wide range of gait tasks were employed in studies assessing FOG among PD patients. However, three tasks (turning, dual tasking, and straight walking) emerged as the most frequently used. Turning (28%) appears to be the most effective trigger for eliciting FOG in PwPD, followed by walking through a doorway (14%) and dual tasking (10%).

Conclusion

This review thereby supports the utilisation of turning, especially a 360-degree turn, as a reliable trigger for FOG in PwPD. This finding could be beneficial to clinicians conducting clinical evaluations and researchers aiming to assess FOG in a laboratory environment.

Challenges of Teleneurology in the Care of Complex Neurodegenerative Disorders: The Case of Parkinson's Disease with Possible Solutions

Teleneurology is a specialist field within the realm of telemedicine, which is dedicated to delivering neurological care and consultations through virtual encounters. Teleneurology has been successfully used in acute care (e.g., stroke) and outpatient evaluation for chronic neurological conditions such as epilepsy and headaches. However, for some neurologic entities like Parkinson's disease, in which an in-depth physical examination by palpating muscles and performing neurologic maneuvers is the mainstay of monitoring the effects of medication, the yield and feasibility of a virtual encounter are low. Therefore, in this prospective review, we discuss two promising teleneurology approaches and propose adjustments to enhance the value of virtual encounters by improving the validity of neurological examination: 'hybrid teleneurology', which involves revising the workflow of virtual encounters; and 'artificial intelligence (AI)-assisted teleneurology', namely the use of biosensors and wearables and data processing using AI.

Scoring festination and gait freezing in people with Parkinson's: The freezing of gait severity tool-revised

BACKGROUND AND PURPOSE

To improve existing clinical assessments for freezing of gait (FOG) severity, a new clinician-rated tool which integrates the varied types of freezing (FOG Severity Tool-Revised) was developed. This cross-sectional study investigated its validity and reliability.

METHODS

People with Parkinson's disease who were able to independently ambulate eight-metres and understand study instructions were consecutively recruited from outpatient clinics of a tertiary hospital. Those with co-morbidities severely affecting gait were excluded. Participants were assessed with the FOG Severity Tool-Revised, three functional performance tests, the FOG Questionnaire, and outcomes measuring anxiety, cognition, and disability. The FOG Severity Tool-Revised was repeated for test-retest reliability. Exploratory factor analysis and Cronbach's alpha were computed for structural validity and internal consistency. Reliability and measurement error were estimated with ICC (two-way, random), standard error of measurement, and smallest detectable change (SDC95 ). Criterion-related and construct validity were calculated with Spearman's correlations.

RESULTS

Thirty-nine participants were enrolled [79.5% (n = 31) male; Median (IQR): age-73.0 (9.0) years; disease duration-4.0 (5.8) years], with fifteen (38.5%) who reported no medication state change contributing a second assessment for reliability estimation. The FOG Severity Tool-Revised demonstrated sufficient structural validity and internal consistency (α = 0.89-0.93), and adequate criterion-related validity compared to the FOG Questionnaire (ρ = 0.73, 95% CI 0.54-0.85). Test-retest reliability (ICC = 0.96, 95%CI 0.86-0.99) and random measurement error (%SDC95  = 10.4%) was acceptable in this limited sample.

DISCUSSION AND CONCLUSIONS

The FOG Severity Tool-Revised appeared valid in this initial sample of people with Parkinson's. While its psychometric properties remain to be confirmed in a larger sample, it may be considered for use in the clinical setting.

Exploring the levodopa-paradox of freezing of gait in dopaminergic medication-naïve Parkinson's disease populations

The relationship between dopaminergic treatment and freezing of gait (FOG) in Parkinson's disease (PD) is complex: levodopa is the most effective symptomatic treatment for FOG, but long-term pulsatile levodopa treatment has also been linked to an increase in the occurrence of FOG. This concept, however, continues to be debated. Here, we compared the occurrence of FOG between a levodopa-naive PD cohort and a levodopa-treated cohort. Forty-nine treatment-naive patients and 150 levodopa-treated patients were included. The time since first motor symptoms was at least 5 years. Disease severity was assessed using the MDS-UPDRS part III. Occurrence of FOG was assessed subjectively (new freezing-of-gait-questionnaire) and objectively (rapid turns test and Timed Up-and-Go test). The presence of FOG was compared between the levodopa-treated and levodopa-naive groups using a chi-square test of homogeneity. We also performed a binomial Firth logistic regression with disease duration, disease severity, country of inclusion, location of measurement, and executive function as covariates. Subjective FOG was more common in the levodopa-treated cohort (n = 41, 27%) compared to the levodopa-naive cohort (n = 2, 4%, p < 0.001). The association between FOG and levodopa treatment remained after adjustment for covariates (OR = 6.04, 95%Cl [1.60, 33.44], p = 0.006). Objectively verified FOG was more common in the levodopa-treated cohort (n = 21, 14%) compared to the levodopa-naive cohort (n = 1, 2%, p = 0.02). We found an association between long-term pulsatile levodopa treatment and an increased occurrence of FOG. Future studies should further explore the role of nonphysiological stimulation of dopamine receptors in generating FOG, as a basis for possible prevention studies.

The effect of rehabilitation interventions on freezing of gait in people with Parkinson's disease is unclear: a systematic review and meta-analyses

PURPOSE

To summarize the effects of rehabilitation interventions to reduce freezing of gait (FOG) in people with Parkinson's disease.

METHODS

A systematic review with meta-analyses of randomized trials of rehabilitation interventions that reported a FOG outcome was conducted. Quality of included studies and certainty of FOG outcome were assessed using the PEDro scale and GRADE framework.

RESULTS

Sixty-five studies were eligible, with 62 trialing physical therapy/exercise, and five trialing cognitive and/or behavioral therapies. All meta-analyses produced very low-certainty evidence. Physical therapy/exercise had a small effect on reducing FOG post-intervention compared to control (Hedges' g= -0.26, 95% CI= -0.38 to -0.14, 95% prediction interval (PI)= -0.38 to -0.14). We are uncertain of the effects on FOG post-intervention when comparing: exercise with cueing to without cueing (Hedges' g= -0.58, 95% CI= -0.86 to -0.29, 95% PI= -1.23 to 0.08); action observation training plus movement strategy practice to practice alone (Hedges' g= -0.56, 95% CI= -1.16 to 0.05); and dance to multimodal exercises (Hedges' g= -0.64, 95% CI= -1.53 to 0.25).

CONCLUSIONS

We are uncertain if physical therapy/exercise, cognitive or behavioral therapies, are effective at reducing FOG.Implications for rehabilitationFOG leads to impaired mobility and falls, but the effect of rehabilitation interventions (including physical therapy/exercise and cognitive/behavioral therapies) on FOG is small and uncertain.Until more robust evidence is generated, clinicians should assess FOG using both self-report and physical measures, as well as other related impairments such as cognition, anxiety, and fear of falling.Interventions for FOG should be personalized based on the individual's triggers and form part of a broader exercise program addressing gait, balance, and falls prevention.Interventions should continue over the long term and be closely monitored and adjusted as individual circumstances change.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Context Recognition Algorithms for Energy-Efficient Freezing-of-Gait Detection in Parkinson's Disease

Freezing of gait (FoG) is a disabling clinical phenomenon of Parkinson's disease (PD) characterized by the inability to move the feet forward despite the intention to walk. It is one of the most troublesome symptoms of PD, leading to an increased risk of falls and reduced quality of life. The combination of wearable inertial sensors and machine learning (ML) algorithms represents a feasible solution to monitor FoG in real-world scenarios. However, traditional FoG detection algorithms process all data indiscriminately without considering the context of the activity during which FoG occurs. This study aimed to develop a lightweight, context-aware algorithm that can activate FoG detection systems only under certain circumstances, thus reducing the computational burden. Several approaches were implemented, including ML and deep learning (DL) gait recognition methods, as well as a single-threshold method based on acceleration magnitude. To train and evaluate the context algorithms, data from a single inertial sensor were extracted using three different datasets encompassing a total of eighty-one PD patients. Sensitivity and specificity for gait recognition ranged from 0.95 to 0.96 and 0.80 to 0.93, respectively, with the one-dimensional convolutional neural network providing the best results. The threshold approach performed better than ML- and DL-based methods when evaluating the effect of context awareness on FoG detection performance. Overall, context algorithms allow for discarding more than 55% of non-FoG data and less than 4% of FoG episodes. The results indicate that a context classifier can reduce the computational burden of FoG detection algorithms without significantly affecting the FoG detection rate. Thus, implementation of context awareness can present an energy-efficient solution for long-term FoG monitoring in ambulatory and free-living settings.

Dealing with the heterogeneous presentations of freezing of gait: how reliable are the freezing index and heart rate for freezing detection?

BACKGROUND

Freezing of gait (FOG) is an unpredictable gait arrest that hampers the lives of 40% of people with Parkinson's disease. Because the symptom is heterogeneous in phenotypical presentation (it can present as trembling/shuffling, or akinesia) and manifests during various circumstances (it can be triggered by e.g. turning, passing doors, and dual-tasking), it is particularly difficult to detect with motion sensors. The freezing index (FI) is one of the most frequently used accelerometer-based methods for FOG detection. However, it might not adequately distinguish FOG from voluntary stops, certainly for the akinetic type of FOG. Interestingly, a previous study showed that heart rate signals could distinguish FOG from stopping and turning movements. This study aimed to investigate for which phenotypes and evoking circumstances the FI and heart rate might provide reliable signals for FOG detection.

METHODS

Sixteen people with Parkinson's disease and daily freezing completed a gait trajectory designed to provoke FOG including turns, narrow passages, starting, and stopping, with and without a cognitive or motor dual-task. We compared the FI and heart rate of 378 FOG events to baseline levels, and to stopping and normal gait events (i.e. turns and narrow passages without FOG) using mixed-effects models. We specifically evaluated the influence of different types of FOG (trembling vs akinesia) and triggering situations (turning vs narrow passages; no dual-task vs cognitive dual-task vs motor dual-task) on both outcome measures.

RESULTS

The FI increased significantly during trembling and akinetic FOG, but increased similarly during stopping and was therefore not significantly different from FOG. In contrast, heart rate change during FOG was for all types and during all triggering situations statistically different from stopping, but not from normal gait events.

CONCLUSION

When the power in the locomotion band (0.5-3 Hz) decreases, the FI increases and is unable to specify whether a stop is voluntary or involuntary (i.e. trembling or akinetic FOG). In contrast, the heart rate can reveal whether there is the intention to move, thus distinguishing FOG from stopping. We suggest that the combination of a motion sensor and a heart rate monitor may be promising for future FOG detection.

One cue does not fit all: a systematic review with meta-analysis of the effectiveness of cueing on freezing of gait in Parkinson's disease

The difficulty in assessing FOG and the variety of existing cues, hamper to determine which cueing modality should be applied and which FOG-related aspect should be targeted to reach personalized treatments for FOG. This systematic review aimed to highlight: i) whether cues could reduce FOG and improve FOG-related gait parameters, ii) which cues are the most effective, iii) whether medication state (ON-OFF) affects cues-related results. Thirty-three repeated measure design studies assessing cueing effectiveness were included and subdivided according to gait tasks (gait initiation, walking, turning) and to the medication state. Main results reveal that: preparatory phase of gait initiation benefit from visual and auditory cues; spatio-temporal parameters (e.g., step and stride length) and are improved by visual cues during walking; turning time and step time variability are reduced by applying auditory and visual cues. Some findings on the potential benefits of cueing on FOG and FOG gait-related parameters were found. Questions remain about which are the best behavioral strategies according to FOG features and PD clinical characteristics.

Measurement instruments to assess basic functional mobility in Parkinson's Disease: A systematic review of clinimetric properties and feasibility for use in clinical practice

Taniguchi S, Yamamoto A. Measurement instruments to assess basic functional mobility in Parkinson's Disease: A systematic review of clinimetric properties and feasibility for use in clinical practice. Jpn J Compr Rehabil Sci 2023; 14: 16-25.

Objective

To systematically review the evaluation of clinimetric properties and feasibility of the "Modified Parkinson Activity Scale (M-PAS)" and the "Lindop Parkinson's Disease Mobility Assessment (LPA)," which are Parkinson's Disease (PD)-specific measurement instruments to assess basic functional mobility, and to discuss their considerations for use in clinical practice.

Methods

A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A risk of bias assessment was also performed.

Results

Eleven studies were included: five studies used M-PAS (45%), five studies used LPA (45%), and one study used M-PAS and LPA (13%). The risk of bias was low for all evaluated studies.

Conclusion

M-PAS and LPA showed adequate reliability, validity, and responsiveness in detecting intervention changes. M-PAS has more detailed qualitative scoring options, a lack of ceiling effect, and can be used by a non-expert in PD.In contrast, the drawback of M-PAS is that it is time-consuming to apply in everyday clinical practice. On the other hand, LPA with greater simplicity may lead to lower burdens for both patients and raters in situations with strict time limitations. Further research is required to identify new resources.

Cerebellar oscillatory dysfunction during lower-limb movement in Parkinson's disease with freezing of gait

Studies have demonstrated dysfunctional connectivity between the cortico-basal ganglia and cerebellar networks in Parkinson's disease (PD). These networks are critical for appropriate motor and cognitive functions, specifically to control gait and postural tasks in PD. Our recent reports have shown abnormal cerebellar oscillations during rest, motor, and cognitive tasks in people with PD compared to healthy individuals, however, the role of cerebellar oscillations in people with PD and freezing of gait (PDFOG+) during lower-limb movements has not been examined. Here, we evaluated cerebellar oscillations using electroencephalography (EEG) electrodes during cue-triggered lower-limb pedaling movement in 13 PDFOG+, 13 PDFOG-, and 13 age-matched healthy subjects. We focused analyses on the mid-cerebellar Cbz as well as lateral cerebellar Cb1 and Cb2 electrodes. PDFOG+ performed the pedaling movement with reduced linear speed and higher variation compared to healthy subjects. PDFOG+ exhibited attenuated theta power during pedaling motor tasks in the mid-cerebellar location compared to PDFOG- or healthy subjects. Cbz theta power was also associated with FOG severity. No significant differences between groups were seen in Cbz beta power. In the lateral cerebellar electrodes, lower theta power was seen between PDFOG+ and healthy subjects. Our cerebellar EEG data demonstrate the occurrence of reduced theta oscillations in PDFOG+ during lower-limb movement and suggest a potential cerebellar biosignature for neurostimulation therapy to improve gait dysfunctions.

Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection

Freezing of gait (FoG) is identified as a sudden and brief episode of movement cessation despite the intention to continue walking. It is one of the most disabling symptoms of Parkinson's disease (PD) and often leads to falls and injuries. Many computer-aided FoG detection methods have been proposed to use data collected from unimodal sources, such as motion sensors, pressure sensors, and video cameras. However, there are limited efforts of multimodal-based methods to maximize the value of all the information collected from different modalities in clinical assessments and improve the FoG detection performance. Therefore, in this study, a novel end-to-end deep architecture, namely graph fusion neural network (GFN), is proposed for multimodal learning-based FoG detection by combining footstep pressure maps and video recordings. GFN constructs multimodal graphs by treating the encoded features of each modality as vertex-level inputs and measures their adjacency patterns to construct complementary FoG representations, thus reducing the representation redundancy among different modalities. In addition, since GFN is devised to process multimodal graphs of arbitrary structures, it is expected to achieve superior performance with inputs containing missing modalities, compared to the alternative unimodal methods. A multimodal FoG dataset was collected, which included clinical assessment videos and footstep pressure sequences of 340 trials from 20 PD patients. Our proposed GFN demonstrates a great promise of multimodal FoG detection with an area under the curve (AUC) of 0.882. To the best of our knowledge, this is one of the first studies to utilize multimodal learning for automated FoG detection, which offers significant opportunities for better patient assessments and clinical trials in the future.

Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease

BACKGROUND

Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments.

OBJECTIVES

To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use.

METHODS

We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not.

RESULTS

Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG.

CONCLUSIONS

It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.

The development of a home-based technology to improve gait in people with Parkinson's disease: a feasibility study

BACKGROUND

People with Parkinson's disease (PwP) may experience gait impairment and freezing of gait (FOG), a major cause of falls. External cueing, including visual (e.g., spaced lines on the floor) and auditory (e.g., rhythmic metronome beats) stimuli, are considered effective in alleviating mobility deficits and FOG. Currently, there is a need for a technology that delivers automatic, individually adjusted cues in the homes of PwP. The aims of this feasibility study were to describe the first step toward the development of a home-based technology that delivers external cues, test its effect on gait, and assess user experience.

METHODS

Iterative system development was performed by our multidisciplinary team. The system was designed to deliver visual and auditory cues: light stripes projected on the floor and metronome beats, separately. Initial testing was performed using the feedback of five healthy elderly individuals on the cues' clarity (clear visibility of the light stripes and the sound of metronome beats) and discomfort experienced. A pilot study was subsequently conducted in the homes of 15 PwP with daily FOG. We measured participants' walking under three conditions: baseline (with no cues), walking with light stripes, and walking to metronome beats. Outcome measures included step length and step time. User experience was also captured in semi-structured interviews.

RESULTS

Repeated-measures ANOVA of gait assessment in PwP revealed that light stripes significantly improved step length (p = 0.009) and step time (p = 0.019) of PwP. No significant changes were measured in the metronome condition. PwP reported that both cueing modalities improved their gait, confidence, and stability. Most PwP did not report any discomfort in either modality and expressed a desire to have such a technology in their homes. The metronome was preferred by the majority of participants.

CONCLUSIONS

This feasibility study demonstrated the usability and potential effect of a novel cueing technology on gait, and represents an important first step toward the development of a technology aimed to prevent FOG by delivering individually adjusted cues automatically. A further full-scale study is needed. Trial registration This study was registered in ClinicalTrials.gov at 1/2/2022 NCT05211687.

Human gait-labeling uncertainty and a hybrid model for gait segmentation

Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems. To date, their reliability and limitations in manual labeling of gait events have not been studied.

Objectives

Evaluate manual labeling uncertainty and introduce a hybrid stride detection and gait-event estimation model for autonomous, long-term, and remote monitoring.

Methods

Estimate inter-labeler inconsistencies by computing the limits-of-agreement. Develop a hybrid model based on dynamic time warping and convolutional neural network to identify valid strides and eliminate non-stride data in inertial (walking) data collected by a wearable device. Finally, detect gait events within a valid stride region.

Results

The limits of inter-labeler agreement for key gait events heel off, toe off, heel strike, and flat foot are 72, 16, 24, and 80 ms, respectively; The hybrid model's classification accuracy for stride and non-stride are 95.16 and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24, 5, 9, and 13 ms, respectively, when compared to the average human labels.

Conclusions

The results show the inherent labeling uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers, and it is a valid model to reliably detect strides and estimate the gait events in human gait data.

Significance

This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.

Daily-Living Freezing of Gait as Quantified Using Wearables in People With Parkinson Disease: Comparison With Self-Report and Provocation Tests

OBJECTIVE

Freezing of gait (FOG) is an episodic, debilitating phenomenon that is common among people with Parkinson disease. Multiple approaches have been used to quantify FOG, but the relationships among them have not been well studied. In this cross-sectional study, we evaluated the associations among FOG measured during unsupervised daily-living monitoring, structured in-home FOG-provoking tests, and self-report.

METHODS

Twenty-eight people with Parkinson disease and FOG were assessed using self-report questionnaires, percentage of time spent frozen (%TF) during supervised FOG-provoking tasks in the home while off and on dopaminergic medication, and %TF evaluated using wearable sensors during 1 week of unsupervised daily-living monitoring. Correlations between those 3 assessment approaches were analyzed to quantify associations. Further, based on the %TF difference between in-home off-medication testing and in-home on-medication testing, the participants were divided into those responding to Parkinson disease medication (responders) and those not responding to Parkinson disease medication (nonresponders) in order to evaluate the differences in the other FOG measures.

RESULTS

The %TF during unsupervised daily living was mild to moderately correlated with the %TF during a subset of the tasks of the in-home off-medication testing but not the on-medication testing or self-report. Responders and nonresponders differed in the %TF during the personal "hot spot" task of the provoking protocol while off medication (but not while on medication) but not in the total scores of the self-report questionnaires or the measures of FOG evaluated during unsupervised daily living.

CONCLUSION

The %TF during daily living was moderately related to FOG during certain in-home FOG-provoking tests in the off-medication state. However, this measure of FOG was not associated with self-report or FOG provoked in the on-medication state. These findings suggest that to fully capture FOG severity, it is best to assess FOG using a combination of all 3 approaches.

IMPACT

These findings suggest that several complementary approaches are needed to provide a complete assessment of FOG severity.

The Ziegler Test Is Reliable and Valid for Measuring Freezing of Gait in People With Parkinson Disease

OBJECTIVE

The purpose of this study was to determine interrater and test-retest reliability of the Ziegler test to measure freezing of gait (FOG) severity in people with Parkinson disease. Secondary aims were to evaluate test validity and explore Ziegler test duration as a proxy FOG severity measure.

METHODS

Physical therapists watched 36 videos of people with Parkinson disease and FOG perform the Ziegler test and rated FOG severity using the rating scale in real time. Two researchers rated 12 additional videos and repeated the ratings at least 1 week later. Interrater and test-retest reliability were calculated using intraclass correlation coefficients (ICCs). Bland-Altman plots were used to visualize agreement between the researchers for test-retest reliability. Correlations between the Ziegler scores, Ziegler test duration, and percentage of time frozen (based on video annotations) were determined using Pearson r.

RESULTS

Twenty-four physical therapists participated. Overall, the Ziegler test showed good interrater (ICC2,1 = 0.80; 95% CI = 0.65-0.92) and excellent test-retest (ICC3,1 = 0.91; 95% CI = 0.82-0.96) reliability when used to measure FOG. It was also a valid measure, with a high correlation (r = 0.72) between the scores and percentage of time frozen. Ziegler test duration was moderately correlated (r = 0.67) with percentage of time frozen and may be considered a proxy FOG severity measure.

CONCLUSION

The Ziegler test is a reliable and valid tool to measure FOG when used by physical therapists in real time. Ziegler test duration may be used as a proxy for measuring FOG severity.

IMPACT

Despite FOG being a significant contributor to falls and poor mobility in people with Parkinson disease, current tools to assess FOG are either not suitably responsive or too resource intensive for use in clinical settings. The Ziegler test is a reliable and valid measure of FOG, suitable for clinical use, and may be used by physical therapists regardless of their level of clinical experience.

Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care

Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.

The Grey Area of Freezing of Gait Annotation: a Guideline and Open‐source Practical Tool

Background

Freezing of gait, a disabling episodic symptom, is difficult to assess as the exact begin‐ and endpoint of an episode is not easy to specify. This hampers scientific and clinical progress. The current golden standard is video annotation by two independent raters. However, the comparison of the two ratings gives rise to non‐overlapping, gray areas.

Objective

To provide a guideline for dealing with these gray areas.

Methods/Results

We propose a standardized procedure for handling the gray areas based on two parameters, the tolerance and correction parameter. Furthermore, we recommend the use of positive agreement, negative agreement, and prevalence index to report interrater agreement instead of the commonly used intraclass correlation coefficient or Cohen's kappa. This theoretical guideline was implemented in an open‐source practical tool, FOGtool (https://github.com/helenacockx/FOGtool).

Conclusion

This paper aims to contribute to the standardization of freezing of gait assessment, thereby improving data sharing procedures and replicability of study results.

Intelligent wearable system with accurate detection of abnormal gait and timely cueing for mobility enhancement of people with Parkinson's disease

Previously reported wearable systems for people with Parkinson's disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37 s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.

A New Paradigm in Parkinson's Disease Evaluation With Wearable Medical Devices: A Review of STAT-ONTM

In the past decade, the use of wearable medical devices has been a great breakthrough in clinical practice, trials, and research. In the Parkinson's disease field, clinical evaluation is time limited, and healthcare professionals need to rely on retrospective data collected through patients' self-filled diaries and administered questionnaires. As this often leads to inaccurate evaluations, a more objective system for symptom monitoring in a patient's daily life is claimed. In this regard, the use of wearable medical devices is crucial. This study aims at presenting a review on STAT-ONTM, a wearable medical device Class IIa, which provides objective information on the distribution and severity of PD motor symptoms in home environments. The sensor analyzes inertial signals, with a set of validated machine learning algorithms running in real time. The device was developed for 12 years, and this review aims at gathering all the results achieved within this time frame. First, a compendium of the complete journey of STAT-ONTM since 2009 is presented, encompassing different studies and developments in funded European and Spanish national projects. Subsequently, the methodology of database construction and machine learning algorithms design and development is described. Finally, clinical validation and external studies of STAT-ONTM are presented.

A Single Wearable Sensor for Gait Analysis in Parkinson’s Disease: A Preliminary Study

Movement monitoring in patients with Parkinson’s disease (PD) is critical for quantifying disease progression and assessing how a subject responds to medication administration over time. In this work, we propose a continuous monitoring system based on a single wearable sensor placed on the lower back and an algorithm for gait parameters evaluation. In order to preliminarily validate the proposed system, seven PD subjects took part in an experimental protocol in preparation for a larger randomized controlled study. We validated the feasibility of our algorithm in a constrained environment through a laboratory scenario. Successively, it was tested in an unsupervised environment, such as the home scenario, for a total of almost 12 h of daily living activity data. During all phases of the experimental protocol, videos were shot to document the tasks. The obtained results showed a good accuracy of the proposed algorithm. For all PD subjects in the laboratory scenario, the algorithm for step identification reached a percentage error low of 2%, 99.13% of sensitivity and 100% of specificity. In the home scenario the Bland–Altman plot showed a mean difference of −3.29 and −1 between the algorithm and the video recording for walking bout detection and steps identification, respectively.

Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks

BACKGROUND

Freezing of gait (FOG) is a common and debilitating gait impairment in Parkinson's disease. Further insight into this phenomenon is hampered by the difficulty to objectively assess FOG. To meet this clinical need, this paper proposes an automated motion-capture-based FOG assessment method driven by a novel deep neural network.

METHODS

Automated FOG assessment can be formulated as an action segmentation problem, where temporal models are tasked to recognize and temporally localize the FOG segments in untrimmed motion capture trials. This paper takes a closer look at the performance of state-of-the-art action segmentation models when tasked to automatically assess FOG. Furthermore, a novel deep neural network architecture is proposed that aims to better capture the spatial and temporal dependencies than the state-of-the-art baselines. The proposed network, termed multi-stage spatial-temporal graph convolutional network (MS-GCN), combines the spatial-temporal graph convolutional network (ST-GCN) and the multi-stage temporal convolutional network (MS-TCN). The ST-GCN captures the hierarchical spatial-temporal motion among the joints inherent to motion capture, while the multi-stage component reduces over-segmentation errors by refining the predictions over multiple stages. The proposed model was validated on a dataset of fourteen freezers, fourteen non-freezers, and fourteen healthy control subjects.

RESULTS

The experiments indicate that the proposed model outperforms four state-of-the-art baselines. Moreover, FOG outcomes derived from MS-GCN predictions had an excellent (r = 0.93 [0.87, 0.97]) and moderately strong (r = 0.75 [0.55, 0.87]) linear relationship with FOG outcomes derived from manual annotations.

CONCLUSIONS

The proposed MS-GCN may provide an automated and objective alternative to labor-intensive clinician-based FOG assessment. Future work is now possible that aims to assess the generalization of MS-GCN to a larger and more varied verification cohort.

Measurement Accuracy of Freezing of Gait Scoring Based on Videos

Freezing of gait (FOG) is a common symptom in the late stages of Parkinson’s disease and related disorders. Videos are the gold standard method to conduct FOG scoring; however, the measurement accuracy of FOG scoring based on videos has not been formally assessed, despite its use in previous studies. This study aimed to calculate the measurement accuracy of video-based FOG scoring. Three evaluators scored the FOG based on 157 video data points collected from 21 patients using an annotation tool. One evaluator measured the intra-rater reliability of the retest. The total duration of observed FOG, percentage of the time spent with FOG during the walking task (%FOG), and FOG phenotypes (shuffling, trembling, and complete akinesia) were evaluated. Intraclass correlation coefficients were used to determine the intra- and inter-rater reliabilities. The duration of FOG and %FOG showed good measurement accuracy for both intra-rater and inter-rater reliabilities. However, the FOG phenotypes showed poor measurement accuracy in inter-rater reliability. These results indicate that the temporal characteristics of FOG can be scored with a high degree of measurement accuracy, even with different evaluators; conversely, the FOG phenotypes need to be scored by several evaluators.

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

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.

Monoamine Oxidase-B Inhibitors for the Treatment of Parkinson's Disease: Past, Present, and Future

Monoamine oxidase-B (MAO-B) inhibitors are commonly used for the symptomatic treatment of Parkinson’s disease (PD). MAO-B inhibitor monotherapy has been shown to be effective and safe for the treatment of early-stage PD, while MAO-B inhibitors as adjuvant drugs have been widely applied for the treatment of the advanced stages of the illness. MAO-B inhibitors can effectively improve patients’ motor and non-motor symptoms, reduce “OFF” time, and may potentially prevent/delay disease progression. In this review, we discuss the effects of MAO-B inhibitors on motor and non-motor symptoms in PD patients, their mechanism of action, and the future development of MAO-B inhibitor therapy.

A Multi-Modal Analysis of the Freezing of Gait Phenomenon in Parkinson’s Disease

Background: Freezing of Gait (FOG) is one of the most disabling motor complications of Parkinson’s disease, and consists of an episodic inability to move forward, despite the intention to walk. FOG increases the risk of falls and reduces the quality of life of patients and their caregivers. The phenomenon is difficult to appreciate during outpatients visits; hence, its automatic recognition is of great clinical importance. Many types of sensors and different locations on the body have been proposed. However, the advantages of a multi-sensor configuration with respect to a single-sensor one are not clear, whereas this latter would be advisable for use in a non-supervised environment. Methods: In this study, we used a multi-modal dataset and machine learning algorithms to perform different classifications between FOG and non-FOG periods. Moreover, we explored the relevance of features in the time and frequency domains extracted from inertial sensors, electroencephalogram and skin conductance. We developed both a subject-independent and a subject-dependent algorithm, considering different sensor subsets. Results: The subject-independent and subject-dependent algorithms yielded accuracies of 85% and 88% in the leave-one-subject-out and leave-one-task-out test, respectively. Results suggest that the inertial sensors positioned on the lower limb are generally the most significant in recognizing FOG. Moreover, the performance impairment experienced when using a single tibial accelerometer instead of the optimal multi-modal configuration is limited to 2–3%. Conclusions: The achieved results disclose the possibility of getting a good FOG recognition using a minimally invasive set-up made of a single inertial sensor. This is very significant in the perspective of implementing a long-term monitoring of patients in their homes, during activities of daily living.

Assessing inertial measurement unit locations for freezing of gait detection and patient preference

Background

Freezing of gait, a common symptom of Parkinson’s disease, presents as sporadic episodes in which an individual’s feet suddenly feel stuck to the ground. Inertial measurement units (IMUs) promise to enable at-home monitoring and personalization of therapy, but there is a lack of consensus on the number and location of IMUs for detecting freezing of gait. The purpose of this study was to assess IMU sets in the context of both freezing of gait detection performance and patient preference.

Methods

Sixteen people with Parkinson’s disease were surveyed about sensor preferences. Raw IMU data from seven people with Parkinson’s disease, wearing up to eleven sensors, were used to train convolutional neural networks to detect freezing of gait. Models trained with data from different sensor sets were assessed for technical performance; a best technical set and minimal IMU set were identified. Clinical utility was assessed by comparing model- and human-rater-determined percent time freezing and number of freezing events.

Results

The best technical set consisted of three IMUs (lumbar and both ankles, AUROC = 0.83), all of which were rated highly wearable. The minimal IMU set consisted of a single ankle IMU (AUROC = 0.80). Correlations between these models and human raters were good to excellent for percent time freezing (ICC = 0.93, 0.89) and number of freezing events (ICC = 0.95, 0.86) for the best technical set and minimal IMU set, respectively.

Conclusions

Several IMU sets consisting of three IMUs or fewer were highly rated for both technical performance and wearability, and more IMUs did not necessarily perform better in FOG detection. We openly share our data and software to further the development and adoption of a general, open-source model that uses raw signals and a standard sensor set for at-home monitoring of freezing of gait.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-00992-x.

Adaptive deep brain stimulation: Retuning Parkinson's disease

A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.

Dual Task Turning in Place: A Reliable, Valid, and Responsive Outcome Measure of Freezing of Gait

BACKGROUND

Freezing of gait (FOG) is a complex symptom in Parkinson's disease (PD) that is both elusive to elicit and varied in its presentation. These complexities present a challenge to measuring FOG in a sensitive and reliable way, precluding therapeutic advancement.

OBJECTIVE

We investigated the reliability, validity, and responsiveness of manual video annotations of the turning-in-place task and compared it to the sensor-based FOG ratio.

METHODS

Forty-five optimally medicated people with PD and FOG performed rapid alternating 360° turns without and with an auditory stroop dual task, thrice over two consecutive days. The tasks were video recorded, and inertial sensors were placed on the lower back and shins. Interrater reliability between three raters, criterion validity with self-reported FOG, and responsiveness to single-session split-belt treadmill (SBT) training were investigated and contrasted with the sensor-based FOG ratio.

RESULTS

Visual ratings showed excellent agreement between raters for the percentage time frozen (%TF) (ICC [intra-class correlation coefficient] = 0.99), the median duration of a FOG episode (ICC = 0.90), and the number of FOG episodes (ICC = 0.86). Dual tasking improved the sensitivity and validity of visual FOG ratings resulting in increased FOG detection, criterion validity with self-reported FOG ratings, and responsiveness to a short SBT intervention. The sensor-based FOG ratio, on the contrary, showed complex FOG presentation-contingent relationships with visual and self-reported FOG ratings and limited responsiveness to SBT training.

CONCLUSIONS

Manual video annotations of FOG during dual task turning in place generate reliable, valid, and sensitive outcomes for investigating therapeutic effects on FOG. © 2021 International Parkinson and Movement Disorder Society.

Discussion of Research Priorities for Gait Disorders in Parkinson's Disease

Gait and balance abnormalities develop commonly in Parkinson's disease and are among the motor symptoms most disabling and refractory to dopaminergic or other treatments, including deep brain stimulation. Efforts to develop effective therapies are challenged by limited understanding of these complex disorders. There is a major need for novel and appropriately targeted research to expedite progress in this area. The Scientific Issues Committee of the International Parkinson and Movement Disorder Society has charged a panel of experts in the field to consider the current knowledge gaps and determine the research routes with highest potential to generate groundbreaking data. © 2021 International Parkinson and Movement Disorder Society.

Translation, Cultural Adaptation, and Reliability and Validity Testing of a Chinese Version of the Freezing of Gait Questionnaire (FOGQ-CH)

Freezing of gait is a disabling symptom with a complex episodic nature that is frequently experienced by people with Parkinson's disease (PD). Although China has the largest population with PD in the world, no Chinese version of the freezing of gait questionnaire (FOGQ), the instrument that has been most widely used to assess FOG, has yet been developed. This study aimed to translate and adapt the original version of FOGQ to create a Chinese version, the FOGQ-CH, then assess its reliability, calculate the Minimal Detectable Change (MDC) and investigate its validity. The forward-backwards translation model was adopted, and cultural adaptation included expert review and pretesting. For the reliability study, 31 Chinese native speaking patients with PD were assessed two times in a 7–10 days interval. Internal consistency and test-retest reliability of the FOGQ-CH were measured by Cronbach's alpha (Cα) and the Intraclass Correlation Coefficient (ICC). For the validity study, 34 native speakers of Chinese with PD were included. To explore the convergent validity, relationships between the FOGQ-CH and the Unified Parkinson's Disease Rating Scale Part II (UPDRS II) and Part III (UPDRS III), Timed Up and Go Test (TUGT), Timed Up and Go Test in cognitive task (TUGT-Cog), walking speed (10 MWT speed), and step length (10 MWT step length) in a 10-m Walk Test were tested. To explore predictive validity, the number of falls followed up for 6 months were assessed. The area under the ROC curve (AUC) was employed to test the capacity of FOGQ-CH to discriminate those with falls. From the reliability study, Cα = 0.823, ICC = 0.786. The MDC_0.90 = 4.538. From the validity study, the FOGQ-CH showed moderate correlations with UPDRS II (rho = 0.560, p = 0.001), UPDRS III (rho = 0.451, p = 0.007), TUGT (rho = 0.556, p = 0.007), TUGT-Cog (rho = 0.557, p = 0.001), 10MWT-speed (rho = −0.478, p = 0.004), 10MWT-step length (rho = −0.419, p = 0.014), and the number of falls followed up for 6 months (rho = 0.356, p = 0.045). The AUC = 0.777 (p = 0.036) for predicting whether the participants will have multiple falls (two or more) in the following 6 months. The FOGQ-CH showed good reliability and validity for assessing Chinese native speaking patients with PD. In addition, the FOGQ-CH showed good efficacy for predicting multiple falls in the following 6 months.

Application of Wavelet in Quantitative Evaluation of Gait Events of Parkinson's Disease

RESULTS

Systems detected FOG and other gait postures and showed time-frequency range by examining differentiated decomposed signals by DWT. Energy distribution and PSD graph proved the accuracy of the system. Validation is done by the LOSO method which shows 90% accuracy for the proposed method.

CONCLUSION

Observations of the clinical trials validate the proposed technique. In comparison to the previous techniques reported in literature, it is seen that the proposed method shows improvement in time and frequency resolution as well as processing time.