Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson’s Disease Classification of Gait Patterns

Som, Anirudh; Krishnamurthi, Narayanan; Buman, Matthew P.; Turaga, Pavan

doi:10.1109/embc44109.2020.9176572

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Using nine DOF IMUs mounted on the hands and feet, Butt et al [ 70 ] built a Bi-LSTM model to classify 64 PD patients and 50 healthy controls, achieving an accuracy of 82.4%. To concur with the requirements of a large amount of training data of deep learning models, Som et al [ 72 ] proposed a novel method of pretraining the model in healthy subjects performing activities of daily living (ADL) dataset to extract the relevant features for PD classification. The results showed that the autoencoder obtained a better performance than hand-engineered features in multiple sensor locations.…”

Section: Results For Different Application Scenariosmentioning

confidence: 99%

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IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

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With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

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Section: Results For Different Application Scenariosmentioning

confidence: 99%

“…Pretrained models are the new trend in ML, and using an autoencoder to learn a latent representation of the input data for advanced feature extraction has proven to be useful and reduce the need for a large number of labeled data [ 72 , 73 ].…”

Section: Imus For Monitoring Body Motionmentioning

confidence: 99%

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Yerebakan

Dai

et al. 2022

Healthcare

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“…However, their exploitation has not significantly improved the classification performance of the previously discussed studies. Some indicative approaches are [61][62][63][64]. In [62], an SVM classifier achieves 95% accuracy, while in [63], an RF classifier achieves 86-94.6% accuracy.…”

Section: Inertial Sensorsmentioning

confidence: 99%

“…In [62], an SVM classifier achieves 95% accuracy, while in [63], an RF classifier achieves 86-94.6% accuracy. The highest performance (96% accuracy) is obtained in [64], with the help of a majority voting scheme over several conventional ML algorithms, while the lowest performance (68.64-73.81% accuracy) is obtained in [61] with an MLP, trained with features extracted by a convolutional autoencoder (AE), which was pre-trained on healthy subjects' data.…”

Section: Inertial Sensorsmentioning

confidence: 99%

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Giannakopoulou

Roussaki

Demestichas

2022

Sensors

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Parkinson’s disease is a chronic neurodegenerative disease that affects a large portion of the population, especially the elderly. It manifests with motor, cognitive and other types of symptoms, decreasing significantly the patients’ quality of life. The recent advances in the Internet of Things and Artificial Intelligence fields, including the subdomains of machine learning and deep learning, can support Parkinson’s disease patients, their caregivers and clinicians at every stage of the disease, maximizing the treatment effectiveness and minimizing the respective healthcare costs at the same time. In this review, the considered studies propose machine learning models, trained on data acquired via smart devices, wearable or non-wearable sensors and other Internet of Things technologies, to provide predictions or estimations regarding Parkinson’s disease aspects. Seven hundred and seventy studies have been retrieved from three dominant academic literature databases. Finally, one hundred and twelve of them have been selected in a systematic way and have been considered in the state-of-the-art systematic review presented in this paper. These studies propose various methods, applied on various sensory data to address different Parkinson’s disease-related problems. The most widely deployed sensors, the most commonly addressed problems and the best performing algorithms are highlighted. Finally, some challenges are summarized along with some future considerations and opportunities that arise.

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“…Despite the very small sample size, a correlation between the speech recognition performance and Frenchay Dysarthria Assessment scores could be reported. Training background models from larger datasets that only included healthy contributors was also performed for gait analysis [11]. Robust features could be learned from the signals of healthy participants that were then used to classify PD.…”

Section: Introductionmentioning

confidence: 99%

The Phonetic Footprint of Parkinson's Disease

Klumpp,

Arias-Vergara,

Vásquez-Correa

et al. 2021

Preprint

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As one of the most prevalent neurodegenerative disorders, Parkinson's disease (PD) has a significant impact on the fine motor skills of patients. The complex interplay of different articulators during speech production and realization of required muscle tension become increasingly difficult, thus leading to a dysarthric speech. Characteristic patterns such as vowel instability, slurred pronunciation and slow speech can often be observed in the affected individuals and were analyzed in previous studies to determine the presence and progression of PD. In this work, we used a phonetic recognizer trained exclusively on healthy speech data to investigate how PD affected the phonetic footprint of patients. We rediscovered numerous patterns that had been described in previous contributions although our system had never seen any pathological speech previously. Furthermore, we could show that intermediate activations from the neural network could serve as feature vectors encoding information related to the disease state of individuals. We were also able to directly correlate the expert-rated intel-

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Unsupervised Pre-trained Models from Healthy ADLs Improve Parkinson’s Disease Classification of Gait Patterns

Cited by 12 publications

References 18 publications

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

The Phonetic Footprint of Parkinson's Disease

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