Classifying human motion quality for knee osteoarthritis using accelerometers

Taylor, Portia E.; Almeida, Gustavo J.; Kanade, Takeo; Hodgins, Jessica K.

doi:10.1109/iembs.2010.5627665

Cited by 60 publications

(60 citation statements)

References 11 publications

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“…The use of LOSOCV ensures that there is no biasing of the classifiers, in such that the test subjects data is completely unseen by the classifier prior to testing. Previous research by Taylor et al [5] has shown that not employing this method of testing can skew results by up to 21%.…”

Section: Discussionmentioning

confidence: 97%

“…Previous work has evaluated the use of multiple inertial sensors to evaluate exercise quality [4][5][6][7]. However, all of the previous research employs multiple sensor units, therefore reducing their ability to be used in an in-home environment.…”

Section: Introductionmentioning

confidence: 99%

“…Minimising the number of sensors used both reduces the cost and makes the platform more user friendly which is extremely important for older adults who may use this technology. Previous work has also attempted to classify between good and bad movements for particular exercises [5,10], however this work is novel as it examines the use of machine learning techniques to classify between different performed exercises.…”

Section: Introductionmentioning

confidence: 99%

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The use of inertial sensors for the classification of rehabilitation exercises

Giggins

Sweeney

Caulfield

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The use of inertial sensors for the classification of rehabilitation exercises

Giggins

Sweeney

Caulfield

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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“…The use of LOSOCV ensures that there is no biasing of the classifiers, meaning the test subjects data is completely unseen by the classifier prior to testing. Previous research by Taylor et al (2010) has shown that not employing this method of testing can skew results significantly. In our system, each individual reach direction was classified.…”

Section: Discussionmentioning

confidence: 99%

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

O’Reilly¹,

Dolan²,

Reid³

et al. 2016

Proceedings of the 4th International Congress on Sport Sciences Research and Technology Support

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Abstract:The Y Balance Test (YBT) is one of the most commonly used dynamic balance assessments in clinical and research settings. This study sought to investigate the ability of a single lumbar inertial measurement unit (IMU) to discriminate between the three YBT reach directions, and between pre and post-fatigue balance performance during the YBT. Fifteen subjects (age: 23±4, weight: 67.5±8, height: 175±8, BMI: 22±2) were fitted with a lumbar IMU. Three YBTs were performed on the dominant leg at 0, 10 and 20 minutes. A modified Wingate fatiguing intervention was conducted to introduce a balance deficit. This was followed immediately by three post-fatigue YBTs. Features were extracted from the IMU, and used to train and evaluate the random-forest classifiers. Reach direction classification achieved an accuracy of 97.80%, sensitivity of 97.86±0.89% and specificity of 98.90±0.56%. "Normal" and "abnormal" balance performance, as influenced by fatigue, was classified with an accuracy of 61.90%-71.43%, sensitivity of 61.90%-69.04% and specificity of 61.90%-78.57% depending on which reach direction was chosen. These results demonstrate that a single lumbar IMU is capable of accurately distinguishing between the different YBT reach directions and can classify between pre and post-fatigue balance with moderate levels of accuracy.

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“…While such a camera-based sensor can observe people only within its field of view, people are not required to carry any wearable sensors [20] and their motions are not affected by these sensors. In addition, for our goal (i.e., finding the symptoms in a gait motion), 24-hour observation using wearable sensors is not necessarily required.…”

Section: Related Workmentioning

confidence: 99%

Classification of Gait Anomaly due to Lesion Using Full-Body Gait Motions

Higashiguchi

Shimoyama

Ukita

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a method for evaluating a physical gait motion based on a 3D human skeleton measured by a depth sensor. While similar methods measure and evaluate the motion of only a part of interest (e.g., knee), the proposed method comprehensively evaluates the motion of the full body. The gait motions with a variety of physical disabilities due to lesioned body parts are recorded and modeled in advance for gait anomaly detection. This detection is achieved by finding lesioned parts a set of pose features extracted from gait sequences. In experiments, the proposed features extracted from the full body allowed us to identify where a subject was injured with 83.1% accuracy by using the model optimized for the individual. The superiority of the full-body features was validated in in contrast to local features extracted from only a body part of interest (77.1% by lower-body features and 65% by upper-body features). Furthermore, the effectiveness of the proposed full-body features was also validated with single universal model used for all subjects; 55.2%, 44.7%, and 35.5% by the full-body, lower-body, and upper-body features, respectively.

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Classifying human motion quality for knee osteoarthritis using accelerometers

Cited by 60 publications

References 11 publications

The use of inertial sensors for the classification of rehabilitation exercises

The use of inertial sensors for the classification of rehabilitation exercises

Objective Classification of Dynamic Balance Using a Single Wearable Sensor

Classification of Gait Anomaly due to Lesion Using Full-Body Gait Motions

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