Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimizing the Classification of Mobility Data in Health Informatics

Rastegari, Elham; Orn, Donovan; Ali, Hesham

doi:10.1145/3388440.3412426

Cited by 3 publications

(8 citation statements)

References 36 publications

(55 reference statements)

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“…Their classification results showed that, first, the meta-classifiers based on the majority of voting achieved the best results and then, the range of motion (RoM) parameters extracted from the knee, which outperformed many spatiotemporal parameters, such as step length and step time. Rastegari et al [ 58 ] searched for the best feature selection methods to assess the movements of PD patients and geriatrics and proved that genetic algorithms, the maximum signal-to-noise ratio with minimum correlation, and a modified version of the maximum information gain with minimum correlation are the best performers.…”

Section: Results For Different Application Scenariosmentioning

confidence: 99%

“…It chooses feature subsets by criterion such as entropy and evaluates the subset performance by applying it to trained models [63]. For feature transformation, the Principal Component Analysis (PCA) uses an orthogonal transformation to convert raw features to compact uncorrelated new features, which is widely used to reduce the dimensions without sacrificing the accuracy [58,[64][65][66]. Inspired by the neurobiological model of the visual cortex [67], the CNN utilizes a series of weight-shared small filters to perform a convolutional operation over the whole input Signal, which results in two main advantages over other NN models: local dependency and scale invariance [68].…”

Section: Traditional ML Methodsmentioning

confidence: 99%

“…Ensemble learning is a powerful method, since it combines multiple learners through a certain strategy and usually can outperform most individual learners [ 57 ]. Besides bagging ensemble methods RF, boosting methods such as Adaboost [ 58 ], RUSBoost [ 59 ], and XGBoost [ 60 , 61 ] are frequently used for better accuracy and robustness.…”

Section: Imus For Monitoring Body Motionmentioning

confidence: 99%

Section: Imus For Monitoring Body Motionmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Traditional ML Methodsmentioning

confidence: 99%

Section: Imus For Monitoring Body Motionmentioning

confidence: 99%

Section: Imus For Monitoring Body Motionmentioning

confidence: 99%

See 2 more Smart Citations

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

Yerebakan

Dai

et al. 2022

Healthcare

View full text Add to dashboard Cite

show abstract

“…Ensemble learning is a powerful method since it combines multiple learners through a certain strategy and usually can outperform most individual learners [48]. Besides bagging ensemble methods RF, boosting methods such as Adaboost [49], RUSBoost [52], and XGBoost [44], [75] are frequently used for better accuracy and robustness.…”

Section: B ML Based Methodsmentioning

confidence: 99%

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

Fan¹,

Yerebakan²,

Dai³

et al. 2022

Preprint

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With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnosis carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement units (IMUs) 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. To help readers comprehensively understand the fundamentals and state-of-the-art techniques, this article systematically reviews this field, by introducing and explaining relevant application scenarios, discussing challenges, and predicting foreseeable future trends. <br>

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Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring

Rastegari

Ali

Marmelat

2022

Sensors

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Parkinson’s disease is a neurodegenerative disorder impacting patients’ movement, causing a variety of movement abnormalities. It has been the focus of research studies for early detection based on wearable technologies. The benefit of wearable technologies in the domain rises by continuous monitoring of this population’s movement patterns over time. The ubiquity of wrist-worn accelerometry and the fact that the wrist is the most common and acceptable body location to wear the accelerometer for continuous monitoring suggests that wrist-worn accelerometers are the best choice for early detection of the disease and also tracking the severity of it over time. In this study, we use a dataset consisting of one-week wrist-worn accelerometry data collected from individuals with Parkinson’s disease and healthy elderlies for early detection of the disease. Two feature engineering methods, including epoch-based statistical feature engineering and the document-of-words method, were used. Using various machine learning classifiers, the impact of different windowing strategies, using the document-of-words method versus the statistical method, and the amount of data in terms of number of days were investigated. Based on our results, PD was detected with the highest average accuracy value (85% ± 15%) across 100 runs of SVM classifier using a set of features containing features from every and all windowing strategies. We also found that the document-of-words method significantly improves the classification performance compared to the statistical feature engineering model. Although the best performance of the classification task between PD and healthy elderlies was obtained using seven days of data collection, the results indicated that with three days of data collection, we can reach a classification performance that is not significantly different from a model built using seven days of data collection.

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Smart Computational Approaches with Advanced Feature Selection Algorithms for Optimizing the Classification of Mobility Data in Health Informatics

Cited by 3 publications

References 36 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

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

Detection of Parkinson’s Disease Using Wrist Accelerometer Data and Passive Monitoring

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