A Rehabilitation Exercise Assessment System Based on Wearable Sensors for Knee Osteoarthritis

Chen, Po-Chao; Huang, Chih-Ning; Chen, I‐Chun; Chan, Chia-Tai

doi:10.1007/978-3-642-39470-6_34

Cited by 5 publications

(5 citation statements)

References 5 publications

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“…Ten studies were identified for which automated detection of the exercise being completed was a key objective [14,[17][18][19][63][64][65][66][67][68]. These studies are summarised in Table 9.…”

Section: Exercise Detectionmentioning

confidence: 99%

“…Eleven studies investigated the utilisation of wearable IMU systems for quantifying exercise technique [3,19,23,64,[69][70][71][72][73][74][75]. Table 10 summarises the sensing set-ups, movement measure which was classified, methodology and performance metrics for each system identified in this area.…”

Section: Movement Classificationmentioning

confidence: 99%

“…Ten studies included in this survey have demonstrated the efficacy of wearable inertial sensor systems to automatically identify the exercises being completed by users [14,[17][18][19][63][64][65][66][67][68]. Exercise classification may serve as a useful input to an automated exercise tracking system or automated exercise logbook system.…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

“…This can produce unrealistically high accuracy scores following crossvalidation on a data set. This may have happened in a number of studies which used cross-validation methods such as leave-one-out-cross-validation (LOOCV), repeated random sub-sampling (RRSS) and K-fold crossvalidation where each fold did not represent all of one participant's data [14,19,63,64]. Future work may also benefit from utilising deep learning techniques for classification such as the convolutional neural networks approach demonstrated by Veiga et al [68].…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

“…Future work may also benefit from utilising deep learning techniques for classification such as the convolutional neural networks approach demonstrated by Veiga et al [68]. Such classification methodologies have recently been shown to have many benefits when compared to traditional machine learning classification techniques when analysing timeseries data, including reducing the risk of overfitting and improving system accuracy [77,78] (Table 10) [3,19,23,64,[70][71][72]74]. There is therefore potential to investigate movement classification with larger data sets and across a range of other exercises.…”

Section: Exercise Detection Systemsmentioning

confidence: 99%

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Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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BackgroundAnalysis of lower limb exercises is traditionally completed with four distinct methods (i) 3D motion capture; (ii) depth-camera based systems (iii) visual analysis from a qualified exercise professional; (iv) self-assessment. Each method is associated with a number of limitations. ObjectiveThe aim of this systematic review is to synthesize and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises. Data SourcesA systematic review of PubMed, ScienceDirect and Scopus was conducted. Study Eligibility CriteriaArticles written in English and published in the last 10 years which contained an IMU system for the analysis of repetition-based targeted lower limb exercises were included. Study Appraisal and Synthesis MethodsThe quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised in to three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised. ResultsFrom the 2452 articles that were identified with the search strategies, 47 papers are included in this review. ConclusionsWearable inertial sensor systems for analysing lower limb exercises are a rapidly growing technology. Research over the past ten years has predominantly focused on validating measurements that the systems produce and classifying users' exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date. Key PointsInertial measurement unit (IMU) systems have been extensively validated to successfully measure joint angle and temporal features during lower limb exercises.It is less understood if IMU systems can validly compute kinetic measures pertaining to lower limb exercises.IMU systems, which incorporate machine learning in to their data analysis pathways, have also been found to be effective in automated exercise detection and in classifying movement quality across a range of lower limb exercises.3

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Section: Exercise Detectionmentioning

confidence: 99%

Section: Movement Classificationmentioning

confidence: 99%

Section: Exercise Detection Systemsmentioning

confidence: 99%

Section: Exercise Detection Systemsmentioning

confidence: 99%

Section: Exercise Detection Systemsmentioning

confidence: 99%

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Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

et al. 2018

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Using Non-Traditional Interfaces to Support Physical Therapy for Knee Strengthening

2016

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Physical therapy consists mainly in the execution of rehabilitation processes that aim to help overcome injuries, as well as develop, maintain, or restore maximum body movement. Knee rehabilitation is one kind of physical therapy that requires daily exercises which could be considered monotonous and boring by the patients, discouraging their improvement. This is coupled with the fact that most physical therapists assess exercise performance through verbal and visual means with mostly manual measurements, making it difficult to constantly verify and validate if patients perform the exercises correctly. This article describes a physical therapy monitoring system that uses wearable technology to assess exercise performance and patient progress. This wearable device is able to measure and transfer the movement's data from the patient's limb to a mobile device. Moreover, the user interface is a game, which provides an entertaining approach to therapy exercising. In this article, it is shown that the developed system significantly increases daily user engagement in rehabilitation exercises, through a gameplay that matches physical therapy requirements for knee rehabilitation, as well as offering useful quantitative information to therapists.

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Energy Considerations for Continuous Group Activity Recognition Using Mobile Devices: The Case of GroupSense

Abkenar

Loke

Rahayu

et al. 2016

2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)

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A Rehabilitation Exercise Assessment System Based on Wearable Sensors for Knee Osteoarthritis

Cited by 5 publications

References 5 publications

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review

Using Non-Traditional Interfaces to Support Physical Therapy for Knee Strengthening

Energy Considerations for Continuous Group Activity Recognition Using Mobile Devices: The Case of GroupSense

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