A bag-of-words feature engineering approach for assessing health conditions using accelerometer data

Rastegari, Elham; Ali, Hesham

doi:10.1016/j.smhl.2020.100116

Cited by 17 publications

(17 citation statements)

References 15 publications

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“…Nine studies included participants with PD of varying disease severity. 10 , 26 , 30 , 34 , 35 , 40 , 42 – 44 One study compared PD patients with progressive supranuclear palsy (PSP) which is an atypical Parkinsonian disorder that clinically shares certain features with PD. 34 This poses the potential for misdiagnosis of PD in PSP patients, therefore the use of the AI model with gait analysis has a role in supplementing the clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

“… 49 This is known as the ‘bias’ of the model. Five studies applied leave-one-out cross-validation model; 35 , 38 , 39 , 42 , 46 Three studies applied 10-fold cross-validation model. 34 , 43 , 44 It is difficult to determine which method is the most suitable validation tool, as the results can be subjected to multiple confounding variables in the studies.…”

Section: Discussionmentioning

confidence: 99%

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The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

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Background The purpose of this scoping review was to explore the current applications of objective gait analysis using inertial measurement units, custom algorithms and artificial intelligence algorithms in detecting neurological and musculoskeletal gait altering pathologies from healthy gait patterns. Methods Literature searches were conducted of four electronic databases (Medline, PubMed, Embase and Web of Science) to identify studies that assessed the accuracy of these custom gait analysis models with inputs derived from wearable devices. Data was collected according to the preferred reporting items for systematic reviews and meta-analysis statement guidelines. Results A total of 23 eligible studies were identified for inclusion in the present review, including 10 custom algorithms articles and 13 artificial intelligence algorithms articles. Nine studies evaluated patients with Parkinson’s disease of varying severity and subtypes. Support vector machine was the commonest adopted artificial intelligence algorithm model, followed by random forest and neural networks. Overall classification accuracy was promising for articles that use artificial intelligence algorithms, with nine articles achieving more than 90% accuracy. Conclusions Current applications of artificial intelligence algorithms are reasonably effective discrimination between pathological and non-pathological gait. Of these, machine learning algorithms demonstrate the additional capacity to handle complicated data input, when compared to other custom algorithms. Notably, there has been increasing application of machine learning algorithms for conducting gait analysis. More studies are needed with unsupervised methods and in non-clinical settings to better reflect the community and home-based usage.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

et al. 2022

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“…The first problem that will be discussed is PD diagnosis or equivalently the classification between PD patients and healthy controls, which is frequently addressed based on inertial signals. To this end, gait parameters have been extracted either manually via feature engineering techniques [49][50][51][52][53] or automatically via deep convolutional neural networks (CNNs) [54], to feed several classification algorithms. The deployed algorithms include support vector machines (SVMs), decision trees (DTs), random (RFs), bagged, boosted and fine trees, k-nearest neighbors (kNN), logistic regression (LR), linear discriminant analysis (LDA) and naïve Bayes (NB) classifiers, as well as multi-layered perceptrons (MLPs) or other neural networks (NNs).…”

Section: Inertial Sensorsmentioning

confidence: 99%

“…It appears that when IMUs are attached to the feet, researchers achieve slightly higher performance than when sensors are attached to the waist or to the lower spine. For example, in [49,51,53], PD diagnosis is performed with 90-99.33% accuracy by a DT, an MLP and an RF classifier, respectively, trained on feet signals, while 84.5-85.51% accuracy is obtained when kNN algorithms are trained over waist signals [50,52]. Moreover, the performance does not seem to improve significantly when deep CNNs are deployed in [54], leading to 0.87 area under the receiver operating characteristic curve (AUROC).…”

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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“…Other works use Discrete wavelet transforms to segment the data [14] and split the input signal into approximations. One of the most perpetual approaches to face annotation scarcity in HAR is sliding windows [15][16] [17]. A small sliding window can discard crucial information, while larger ones can contain action transactions.…”

Section: State Of the Artmentioning

confidence: 99%

Pattern Recognition. ICPR International Workshops and Challenges

2021

Lecture Notes in Computer Science

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Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.

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A bag-of-words feature engineering approach for assessing health conditions using accelerometer data

Cited by 17 publications

References 15 publications

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

The application of artificial intelligence and custom algorithms with inertial wearable devices for gait analysis and detection of gait-altering pathologies in adults: A scoping review of literature

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

Pattern Recognition. ICPR International Workshops and Challenges

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