Monitoring the Rehabilitation Progress Using a DCNN and Kinematic Data for Digital Healthcare

Alcaraz, Javier Conte; Moghaddamnia, Sanam; Penner, Maxim; Peissig, Jürgen

doi:10.23919/eusipco47968.2020.9287324

Cited by 8 publications

(15 citation statements)

References 20 publications

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“…Features such as spatiotemporal parameters and joint angles are proposed as the input for a SVM model, achieving an accuracy of 97.0%. DCNN is employed for monitoring the progress of the rehabilitation of hip unilateral arthroplasty surgery by mapping the sensor’s gait cycles data to days after surgery [ 116 ]. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring.…”

Section: Results For Different Application Scenariosmentioning

confidence: 99%

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%

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

Yerebakan

Dai

et al. 2022

Healthcare

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“…Research [24,32] addresses issues related to the measurement of the angle of the prosthesis in real-time; Refs. [25,26] focus on the follow-up of rehabilitation through DCNN and distance training.…”

Section: Rq2-what Are the Main Functions Performed By Inertial Sensor...mentioning

confidence: 99%

“…Refs. [24][25][26] advocate accurate classification, real time feedback, and monitoring for effective rehabilitation using remote rehabilitation ap proaches and technologies. Ref.…”

Section: Rq2-what Are the Main Functions Performed By Inertial Sensor...mentioning

confidence: 99%

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Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review

Acosta-Vargas

Flor

Salvador-Acosta

et al. 2023

Sensors

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The objective of this scoping review is to characterize the current panorama of inertia sensors for the rehabilitation of hip arthroplasty. In this context, the most widely used sensors are IMUs, which combine accelerometers and gyroscopes to measure acceleration and angular velocity in three axes. We found that data collected by the IMU sensors are used to analyze and detect any deviation from the normal to measure the position and movement of the hip joint. The main functions of inertial sensors are to measure various aspects of training, such as speed, acceleration, and body orientation. The reviewers extracted the most relevant articles published between 2010 and 2023 in the ACM Digital Library, PubMed, ScienceDirect, Scopus, and Web of Science. In this scoping review, the PRISMA-ScR checklist was used, and a Cohen’s kappa coefficient of 0.4866 was applied, implying moderate agreement between reviewers; 23 primary studies were extracted from a total of 681. In the future, it will be an excellent challenge for experts in inertial sensors with medical applications to provide access codes for other researchers, which will be one of the most critical trends in the advancement of applications of portable inertial sensors for biomechanics.

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“…Features like spatiotemporal parameters and joint angles are proposed as an input for an SVM model, achieving an accuracy of 97.0%. DCNN is employed for monitoring the progress of the rehabilitation of hip unilateral arthroplasty surgery by mapping the sensor's gait cycles data to days after surgery [112]. The proposed DCNN achieved up to 98% classification accuracy for the rehabilitation progress monitoring.…”

Section: Musculoskeletal Disorders 1) Osteoarthritismentioning

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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Monitoring the Rehabilitation Progress Using a DCNN and Kinematic Data for Digital Healthcare

Cited by 8 publications

References 20 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

Inertial Sensors for Hip Arthroplasty Rehabilitation: A Scoping Review

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

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