Good vibrations: tactile cueing for freezing of gait in Parkinson's disease

Assisted technology in Parkinson's disease gait: what's up?

BACKGROUND

 Gait disturbances are prevalent and debilitating symptoms, diminishing mobility and quality of life for Parkinson's disease (PD) individuals. While traditional treatments offer partial relief, there is a growing interest in alternative interventions to address this challenge. Recently, a remarkable surge in assisted technology (AT) development was witnessed to aid individuals with PD.

OBJECTIVE

 To explore the burgeoning landscape of AT interventions tailored to alleviate PD-related gait impairments and describe current research related to such aim.

METHODS

 In this review, we searched on PubMed for papers published in English (2018-2023). Additionally, the abstract of each study was read to ensure inclusion. Four researchers searched independently, including studies according to our inclusion and exclusion criteria.

RESULTS

 We included studies that met all inclusion criteria. We identified key trends in assistive technology of gait parameters analysis in PD. These encompass wearable sensors, gait analysis, real-time feedback and cueing techniques, virtual reality, and robotics.

CONCLUSION

 This review provides a resource for guiding future research, informing clinical decisions, and fostering collaboration among researchers, clinicians, and policymakers. By delineating this rapidly evolving field's contours, it aims to inspire further innovation, ultimately improving the lives of PD patients through more effective and personalized interventions.

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.

Gait-modifying effects of augmented-reality cueing in people with Parkinson's disease

Introduction

External cueing can improve gait in people with Parkinson's disease (PD), but there is a need for wearable, personalized and flexible cueing techniques that can exploit the power of action-relevant visual cues. Augmented Reality (AR) involving headsets or glasses represents a promising technology in those regards. This study examines the gait-modifying effects of real-world and AR cueing in people with PD.

Methods

21 people with PD performed walking tasks augmented with either real-world or AR cues, imposing changes in gait speed, step length, crossing step length, and step height. Two different AR headsets, differing in AR field of view (AR-FOV) size, were used to evaluate potential AR-FOV-size effects on the gait-modifying effects of AR cues as well as on the head orientation required for interacting with them.

Results

Participants modified their gait speed, step length, and crossing step length significantly to changes in both real-world and AR cues, with step lengths also being statistically equivalent to those imposed. Due to technical issues, step-height modulation could not be analyzed. AR-FOV size had no significant effect on gait modifications, although small differences in head orientation were observed when interacting with nearby objects between AR headsets.

Conclusion

People with PD can modify their gait to AR cues as effectively as to real-world cues with state-of-the-art AR headsets, for which AR-FOV size is no longer a limiting factor. Future studies are warranted to explore the merit of a library of cue modalities and individually-tailored AR cueing for facilitating gait in real-world environments.

Mesencephalic Locomotor Region and Presynaptic Inhibition during Anticipatory Postural Adjustments in People with Parkinson's Disease

Individuals with Parkinson's disease (PD) and freezing of gait (FOG) have a loss of presynaptic inhibition (PSI) during anticipatory postural adjustments (APAs) for step initiation. The mesencephalic locomotor region (MLR) has connections to the reticulospinal tract that mediates inhibitory interneurons responsible for modulating PSI and APAs. Here, we hypothesized that MLR activity during step initiation would explain the loss of PSI during APAs for step initiation in FOG (freezers). Freezers (n = 34) were assessed in the ON-medication state. We assessed the beta of blood oxygenation level-dependent signal change of areas known to initiate and pace gait (e.g., MLR) during a functional magnetic resonance imaging protocol of an APA task. In addition, we assessed the PSI of the soleus muscle during APA for step initiation, and clinical (e.g., disease duration) and behavioral (e.g., FOG severity and APA amplitude for step initiation) variables. A linear multiple regression model showed that MLR activity (R2 = 0.32, p = 0.0006) and APA amplitude (R2 = 0.13, p = 0.0097) explained together 45% of the loss of PSI during step initiation in freezers. Decreased MLR activity during a simulated APA task is related to a higher loss of PSI during APA for step initiation. Deficits in central and spinal inhibitions during APA may be related to FOG pathophysiology.

Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson's disease

Introduction

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson's disease (PD). FOG detection is possible using acceleration data from wearable sensors, and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime.

Methods

We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as an unseen test set. The data were windowed (2 s) and five-fold cross-validation was applied. The CNN, MiniRocket, and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and its area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set.

Results

A total of 70 participants (23.7 h, 9% FOG) were included in this study for training and testing, and in addition, 10 participants provided an unseen test set (2.4 h, 11% FOG). The CNN performed best (AUC = 0.86) in comparison to the InceptionTime (AUC = 0.82) and MiniRocket (AUC = 0.76) models. For the CNN, we found a similar performance for a seven-sensor configuration (lumbar, upper and lower legs and feet; AUC = 0.86), six-sensor configuration (upper and lower legs and feet; AUC = 0.87), and two-sensor configuration (lower legs; AUC = 0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and a specificity of 58% for the hold-out set (AUC = 0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC = 0.90).

Conclusion

We confirmed that deep learning can be used to detect FOG in a large, heterogeneous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.

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.