Prediction of Freezing of Gait in Parkinson’s Disease from Foot Plantar-Pressure Arrays using a Convolutional Neural Network

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

doi:10.1109/embc44109.2020.9176382

Cited by 23 publications

(18 citation statements)

References 21 publications

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“…Extracted gait and balance parameters have been used successfully for discrimination between the groups (Mazumder et al, 2018;Chatzaki et al, 2021), in line with the findings from established laboratory gait assessments. Furthermore, instrumented insoles have been investigated for their ability to recognize and quantify FoG in PD (Popovic et al, 2010;Shalin et al, 2020;Pardoel et al, 2021). Pardoel et al (2021) combined features derived from a pressure-sensing insole and IMUs on the leg to detect FoG in 11 PD patients.…”

Section: Applicationsmentioning

confidence: 99%

“…The authors reported that the combination of both modalities outperformed classification models that used data from a single sensor type. In a small data sample (n = 5), Shalin et al (2020) demonstrated that foot pressure distributions from 60 × 21 sensor-arrays could be used for FoG prediction (0.5-3 s before FoG onset). Therefore, together with inertial sensors, instrumented insoles could be integrated into therapeutic cueing devices for treating gait disorders in PD (cf.…”

Section: Applicationsmentioning

confidence: 99%

See 1 more Smart Citation

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Salchow-Hömmen

Skrobot

Jochner

et al. 2022

Front. Hum. Neurosci.

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The understanding of locomotion in neurological disorders requires technologies for quantitative gait analysis. Numerous modalities are available today to objectively capture spatiotemporal gait and postural control features. Nevertheless, many obstacles prevent the application of these technologies to their full potential in neurological research and especially clinical practice. These include the required expert knowledge, time for data collection, and missing standards for data analysis and reporting. Here, we provide a technological review of wearable and vision-based portable motion analysis tools that emerged in the last decade with recent applications in neurological disorders such as Parkinson's disease and Multiple Sclerosis. The goal is to enable the reader to understand the available technologies with their individual strengths and limitations in order to make an informed decision for own investigations and clinical applications. We foresee that ongoing developments toward user-friendly automated devices will allow for closed-loop applications, long-term monitoring, and telemedical consulting in real-life environments.

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Salchow-Hömmen

Skrobot

Jochner

et al. 2022

Front. Hum. Neurosci.

View full text Add to dashboard Cite

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“…Another method that proved useful for predicting the aforementioned incident involves plantar pressure data represented in the form of 2D images and evaluated by a convolutional neural network (CNN) (25). The newest research designed by Bevilacqua et al included 195 subjects divided into three groups: A control group that followed traditional therapy sessions with a duration of 50 min, and two technological intervention groups, one that used the Tymo system, and another that used the Walker View (in addition to a 30-min traditional rehabilitation session, and another 20 min of treatment with a robotic system) (26).…”

Section: Robotic Systems and Ai Applications For Gait Managementmentioning

confidence: 99%

Artificial intelligence applications and robotic systems in Parkinson's disease (Review)

et al. 2021

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Parkinson's disease (PD) is the second most frequent neurodegenerative disorder following Alzheimer's disease. Advanced stages of PD, 4 or 5 of the Hoehn and Yahr Scale, are characterized by severe motor complications, limited mobility without assistance, risk of falling, and non-motor complications. The aim of this review was to provide a practical overview on specific artificial intelligence (AI) systems for the management of advanced stages of PD, as well as relevant technological limitations. The authors conducted a systematic search on PubMed and EMBASE with predefined keywords searching for studies published until December 2020. Full articles that satisfied the inclusion criteria were included in the systematic review. To minimize results bias, the reference list was manually searched for pertinent articles to identify any additional relevant missed publications. Exclusion criteria included the following: Other stages of PD than 4 and 5 of the Hoehn and Yahr Scale, case reports, reviews, practice guidelines, commentaries, opinions, letters, editorials, short surveys, articles in press, conference abstracts, conference papers, and abstracts published without a full article. The search identified 21 studies analyzing AI-based applications and robotic systems used for the management of advanced stages of PD, out of which 6 articles analyzed AI-based applications for autonomous management of pharmacologic therapy, 5 articles analyzed home-based telemedicine systems and 10 articles analysed robot-assisted gait training systems. The authors identified significant evidence demonstrating that current AI-based technologies are feasible for automatic management of patients with advanced stages of PD. Improving the quality of care and reducing the cost for patients and healthcare systems are the most important advantages. Contents1. Introduction 2. Methods 3. AI applications for autonomous management of pharmacologic therapy 4. Home-based telemedicine systems 5. Robotic systems and AI applications for gait management 6. Conclusions

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“…However, statistical analysis did not identified any significant difference between the multi-modal approach and the accelerometers-only based approach. A FOG prediction system based on pressure sensors positioned on insoles was discussed in a recent paper [ 37 ]. Finally, the ECG was analyzed together with SC, observing significant variations just before the occurrence of a FOG episode [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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

Mesin

Porcu

Russu

et al. 2022

Sensors

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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.

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Prediction of Freezing of Gait in Parkinson’s Disease from Foot Plantar-Pressure Arrays using a Convolutional Neural Network

Cited by 23 publications

References 21 publications

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Review—Emerging Portable Technologies for Gait Analysis in Neurological Disorders

Artificial intelligence applications and robotic systems in Parkinson's disease (Review)

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

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