A Data Set of Human Body Movements for Physical Rehabilitation Exercises

Vakanski, Aleksandar; Jun, Hey Jung; Paul, David R.; Baker, Russell T.

doi:10.3390/data3010002

Cited by 94 publications

(77 citation statements)

References 20 publications

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“…The subjects performed the exercises both in a correct manner, hereafter referred to as correct movements, and in an incorrect manner, i.e., simulating performance by patients with musculoskeletal constraints, hereafter referred to as incorrect movements. A detailed description of the UI-PRMD dataset is provided in [13].…”

Section: A Datasetmentioning

confidence: 99%

A Deep Learning Framework for Assessing Physical Rehabilitation Exercises

Liao

Vakanski

Xian

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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142

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Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, by processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved rehabilitation outcomes and reduced healthcare costs, existing approaches for computeraided monitoring and evaluation of patient performance lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quantifying movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for regressing quality scores of input movements via supervised learning. A performance metric based on the log-likelihood of a Gaussian mixture model used for encoding low-dimensional data representation obtained with a deep autoencoder network, is proposed in the paper. Multiple deep network architectures are repurposed for the task in hand and are validated by using a dataset of rehabilitation exercises. To the best of our knowledge, this is the first work that implements deep neural networks for the assessment of rehabilitation performance.

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Section: A Datasetmentioning

confidence: 99%

A Deep Learning Framework for Assessing Physical Rehabilitation Exercises

Liao

Vakanski

Xian

2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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142

120

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“…The UI-PRMD dataset includes skeleton data from typical exercises and movements which are performed by patients during therapy and rehabilitation programs [19]. It consists of 10 healthy subjects, performing 10 different movements 10 times each, and recorded simultaneously using a Kinect and a VICON (gold standard) motion-capture system.…”

Section: Physical Rehabilitation Movements Data Setmentioning

confidence: 99%

Sit-to-Stand Analysis in the Wild Using Silhouettes for Longitudinal Health Monitoring

Masullo

Burghardt

Perrett

et al. 2019

Lecture Notes in Computer Science

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We present the first fully automated Sit-to-Stand or Stand-to-Sit (StS) analysis framework for long-term monitoring of patients in free-living environments using video silhouettes. Our method adopts a coarse-to-fine time localisation approach, where a deep learning classifier identifies possible StS sequences from silhouettes, and a smart peak detection stage provides fine localisation based on 3D bounding boxes. We tested our method on data from real homes of participants and monitored patients undergoing total hip or knee replacement. Our results show 94.4% overall accuracy in the coarse localisation and an error of 0.026 m/s in the speed of ascent measurement, highlighting important trends in the recuperation of patients who underwent surgery. * a.masullo@bristol.ac.uk 1 In this work, by StS we do in fact mean both 'Sit-to-Stand' and 'Stand-to-Sit', but will specify which of the two, if and when necessary.

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“…The availability of large datasets in the scientific literature calls for novel and efficient algorithms to build models that are potentially able to explain the complex non-linear relationships between attributes [1]. Examples of diverse publicly available datasets published in the Data journal are [2][3][4][5][6][7][8][9], which range from protein synthesis data to human body movements acquisitions. Non-linear modeling is ubiquitous in applied sciences and engineering, as in agriculture, forestry and finance [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Statistical Modeling of Trivariate Static Systems: Isotonic Models

Fiori

Vitali

2019

Data

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This paper presents an improved version of a statistical trivariate modeling algorithm introduced in a short Letter by the first author. This paper recalls the fundamental concepts behind the proposed algorithm, evidences its criticalities and illustrates a number of improvements which lead to a functioning modeling algorithm. The present paper also illustrates the features of the improved statistical modeling algorithm through a comprehensive set of numerical experiments performed on four synthetic and five natural datasets. The obtained results confirm that the proposed algorithm is able to model the considered synthetic and the natural datasets faithfully.

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A Data Set of Human Body Movements for Physical Rehabilitation Exercises

Cited by 94 publications

References 20 publications

A Deep Learning Framework for Assessing Physical Rehabilitation Exercises

A Deep Learning Framework for Assessing Physical Rehabilitation Exercises

Sit-to-Stand Analysis in the Wild Using Silhouettes for Longitudinal Health Monitoring

Statistical Modeling of Trivariate Static Systems: Isotonic Models

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