Automated scoring of rehabilitative tests via time-frequency informed singular spectrum analysis

Lee, T. K. M.; Leo, K. H.; Chew, Effie; Zhao, Ling; Sanei, Saeid

doi:10.1109/iwcim.2015.7347093

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An automated system based on the WMFT is proposed in [ 13 ], using wearable sensors to measure the time spent in completion on 7 of the 17 test items. The sub-set 4 of ARAT is considered for automation in [ 14 ], using a sensorized cube of 7.5-cm size. Additionally, a study to automate the Timed Up and Go (TUG) test in order to discriminate low vs. high risk of fall individuals as objectively as possible using several quantitative parameters is presented in [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Oña

Sanchez-Herrera

Cuesta-Gómez

et al. 2018

Sensors

View full text Add to dashboard Cite

Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson’s disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes’ displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user’s performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Oña

Sanchez-Herrera

Cuesta-Gómez

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…Utilizando sensores vestibles, se estima el tiempo que se tarda en completar 7 de las 17 tareas del WMFT. Un estudio enfocado en la automatización el ARAT se presenta en [2]. En dicho trabajo, se ha planteado la automatización de la sección 4 del ARAT, por medio de la sensorización de uno de los objetos utilizados en la tarea, en este caso un cubo de 7.5 cm.…”

Section: Trabajos Relacionadosunclassified

Conteo automático de cubos en evaluación de destreza manual usando sensores de proximidad

Pozo¹,

Oña²,

Jardón³

et al. 2020

Actas De Las XXXIX Jornadas De Automática, Badajoz, 5-7 De Septiembre De 2018

View full text Add to dashboard Cite

ResumenEn este artículo, se presenta el desarrollo de un sistema basado en sensores de proximidad que permite obtener de forma automática la puntuación del Box and Blocks Test (BBT), destinado a medir la destreza manual gruesa. La evaluación del BBT se basa en el máximo número de cubos desplazados en una ventana de tiempo. Por ese motivo, se han utilizado varios sensores de proximidad basados en detección infrarroja, para sensorizar la trayectoria de desplazamiento de los cubos. En primer lugar, se describe el método utilizado para la automatización de la puntuación del test, describiendo los sensores y el diseño del prototipo. A continuación se muestra la tasa de acierto obtenida en base a varios experimentos. Finalmente, se exponen las conclusiones y futuros trabajos. Los resultados presentados apoyan el uso de métodos automatizados en procesos de rehabilitación, que pueden generar resultados objetivos e información adicional sobre el desempeño del usuario.

show abstract

Autonomous modeling of repetitive movement for rehabilitation exercise monitoring

Jatesiktat

Lim

Kuah

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Insightful feedback generation for daily home-based stroke rehabilitation is currently unavailable due to the inefficiency of exercise inspection done by therapists. We aim to produce a compact anomaly representation that allows a therapist to pay attention to only a few specific sections in a long exercise session record and boost their efficiency in feedback generation. Methods This study proposes a data-driven technique to model a repetitive exercise using unsupervised phase learning on an artificial neural network and statistical learning on principal component analysis (PCA). After a model is built on a set of normal healthy movements, the model can be used to extract a sequence of anomaly scores from a movement of the same prescription. Results The method not only works on a standard marker-based motion capture system but also performs well on a more compact and affordable motion capture system based-on Kinect V2 and wrist-worn inertial measurement units that can be used at home. An evaluation of four different exercises shows its potential in separating anomalous movements from normal ones with an average area under the curve (AUC) of 0.9872 even on the compact motion capture system. Conclusions The proposed processing technique has the potential to help clinicians in providing high-quality feedback for telerehabilitation in a more scalable way.

show abstract

Automated scoring of rehabilitative tests via time-frequency informed singular spectrum analysis

Cited by 3 publications

References 11 publications

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Automatic Outcome in Manual Dexterity Assessment Using Colour Segmentation and Nearest Neighbour Classifier

Conteo automático de cubos en evaluación de destreza manual usando sensores de proximidad

Autonomous modeling of repetitive movement for rehabilitation exercise monitoring

Contact Info

Product

Resources

About