InSEption: A Robust Mechanism for Predicting FoG Episodes in PD Patients

Dimoudis, Dimitris; Tsolakis, Nikos; Maga-Nteve, Christoniki; Meditskos, Georgios; Vrochidis, Stefanos; Kompatsiaris, Ioannis

doi:10.3390/electronics12092088

Cited by 3 publications

(7 citation statements)

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“…It is often cited by researchers, making it appropriate for machine learning model evaluation and training. The extensive use of the Daphnet dataset in research enables insightful comparisons and promotes improvements in FOG prediction techniques [ 40 , 60 , 80 , 82 , 94 , 106 , 123 ]. Even though the Daphnet dataset is widely used, it is important to recognize its limitations.…”

Section: Discussionmentioning

confidence: 99%

Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

Elbatanouny,

Kleanthous,

Dahrouj

et al. 2024

Sensors

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Parkinson’s Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients’ quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.

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Section: Discussionmentioning

confidence: 99%

Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

Elbatanouny,

Kleanthous,

Dahrouj

et al. 2024

Sensors

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“…A further expansion of the iSPLInception network has been conducted in which the inSEption and LN-Inception networks were proposed ( 19 ). Here, inSEption utilizes the inception module but also includes squeeze and excitation blocks.…”

Section: Discussionmentioning

confidence: 99%

“…A public dataset was added to the DaphNet dataset, containing the IMU data of 38 people with PD who were turning on the spot ( 34 ). The data contained 173 FOG episodes ( 19 , 34 ). They found remarkable detection of FOG for both models; the LN-Inception had a sensitivity of 97% and specificity of 99%, whereas the inSEption model resulted in a sensitivity of 98% and specificity of 99% ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

“…The data contained 173 FOG episodes ( 19 , 34 ). They found remarkable detection of FOG for both models; the LN-Inception had a sensitivity of 97% and specificity of 99%, whereas the inSEption model resulted in a sensitivity of 98% and specificity of 99% ( 19 ). This clearly illustrates the potential of an Inception-based model for the detection of FOG.…”

Section: Discussionmentioning

confidence: 99%

“…Rocket is further succeeded by the MiniRocket, which is a fast data mining classifier ( 16 ). InceptionTime is an ensemble of deep CNN models ( 17 ), and while Inception-based networks have previously been used to detect FOG with high accuracy (93.5%) ( 18 ), high sensitivity (98%), and high specificity (99%) ( 19 ), the InceptionTime model specifically has not been tested yet for FOG detection. The MiniRocket has also not been tested to detect FOG on IMU data ( 7 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

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

Klaver,

Heijink,

Silvestri

et al. 2023

Front. Neurol.

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IntroductionFreezing 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.MethodsWe 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.ResultsA 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).ConclusionWe 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.

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The Use of Artificial Intelligence and Wearable IMUs in Medicine: A Systematic Review (Preprint)

Smits Serena,

Hinterwimmer,

Burgkart

et al. 2024

Preprint

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BACKGROUND Artificial Intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with Wearable Inertial Measurement Units (IMUs), AI could transform healthcare again by opening new opportunities in patient care and medical research. OBJECTIVE This systematic review aims to evaluate the integration of Artificial Intelligence (AI) with Wearable Inertial Measurement Units (IMUs) in healthcare, identifying current applications, challenges, and future opportunities. The focus will be on the types of AI models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research. METHODS This systematic review examines this synergy of AI and IMU data by employing a systematic methodology, following PRISMA guidelines, to explore three core questions: Which medical fields are most actively researching AI and IMU data? Which AI models are being utilized in the analysis of IMU data within these medical fields? and what are the characteristics of the datasets used for AI training in this fields? RESULTS The median dataset size is of 50 participants, which poses significant limitations for AI methods given their dependency on large datasets for effective training and generalization. Moreover, our analysis reveals the current dominance of Machine Learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for Deep Learning models in this area. Impressively, 93% of the studies employed Supervised Learning, revealing an underutilization of Unsupervised Learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. Additionally, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underutilization of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts. CONCLUSIONS In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field towards the adoption of more complex Deep Learning models, and the expansion of the application of AI on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research.

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InSEption: A Robust Mechanism for Predicting FoG Episodes in PD Patients

Cited by 3 publications

References 30 publications

Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

Insights into Parkinson’s Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

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

The Use of Artificial Intelligence and Wearable IMUs in Medicine: A Systematic Review (Preprint)

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