A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario

Capecci, Marianna; Ceravolo, M.G.; D'Orazio, Francesco; Ferracuti, Francesco; Iarlori, Sabrina; Lazzaro, Giuseppe; Longhi, Sauro; Romeo, Luca; Verdini, Federica

doi:10.1109/embc.2015.7320257

Cited by 11 publications

(12 citation statements)

References 13 publications

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“…In general, feature engineering involves feature extraction and feature selection. In many related works, feature engineering is performed manually based on authors' understanding of human movements [33]- [37], [54], [97], [98], [126], [127]. For example, in [34], underarm angles and Euclidean distance between the elbows were used to describe the lifting of the arms.…”

Section: Feature Engineeringmentioning

confidence: 99%

“…In [61] and [62], a score function was derived using the log-likelihood of a trained HSMM. The total score for the ℎ subject is = In contrast to these template-based score functions, Capecci et al introduced the following two target-based score functions in [33] and [126], respectively: score(…”

Section: Scoring Functionsmentioning

confidence: 99%

“…Full-body skeletal data are highly redundant, thus, they are rarely applied directly for modeling and analysis of human motions. Consequently, feature engineering via selection of important skeletal dimensions or distances is often employed for extracting relevant information from skeletal data [33]- [35]. Another common avenue for feature engineering entails creating new local features for representing the motions, based on a set of kinematic parameters that are predefined for an exercise, or by employing a functional mapping [36], [37].…”

Section: Introductionmentioning

confidence: 99%

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A review of computational approaches for evaluation of rehabilitation exercises

Liao

Vakanski

Xian

et al. 2020

Computers in Biology and Medicine

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Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been conducive to the development of approaches for automated assessment of patient performance and progress toward functional recovery. The presented study reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. Such approaches will play an important role in supplementing traditional rehabilitation assessment performed by trained clinicians, and in assisting patients participating in home-based rehabilitation. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.The review places an emphasis on the application of machine learning methods for movement evaluation in rehabilitation. Related work in the literature on data representation, feature engineering, movement segmentation, and scoring functions is presented. The study also reviews existing sensors for capturing rehabilitation movements and provides an informative listing of pertinent benchmark datasets. The significance of this paper is in being the first to provide a comprehensive review of computational methods for evaluation of patient performance in rehabilitation programs.

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Section: Feature Engineeringmentioning

confidence: 99%

Section: Scoring Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review of computational approaches for evaluation of rehabilitation exercises

Liao

Vakanski

Xian

et al. 2020

Computers in Biology and Medicine

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“…The aim of the telerehabilitation platform presented in this paper was to enhance the motor abilities of patients suffering of LBP. The clinicians selected five exercises widely used for physiotherapy of axial disorders as a case study [36]. Exercises 1-4 involve the upper body and include: the lifting of the arms (Fig.…”

Section: Exercisesmentioning

confidence: 99%

Collaborative design of a telerehabilitation system enabling virtual second opinion based on fuzzy logic

Capecci

Ciabattoni

Ferracuti

et al. 2018

IET Computer Vision

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Here, the authors present a low cost telerehabilitation system made up of a commercial red-green-blue depth (RGB-D) camera and a web-based platform. The authors goal is to monitor and assess subject movement providing acceptable and usable at-home remote rehabilitation services without the presence of a clinician. Clinical goals, defined by physiotherapists, are firstly translated into motion analysis features. A Takagi Sugeno fuzzy inference system (FIS) is then proposed to evaluate and combine these features into scores. In this stage, the 'collaborative design' paradigm is used in depth and complete manner: the contribution of the clinician is not limited only to the rules definition but enters in the core of the evaluation algorithm through the definition of the fuzzy rules. A case study on low back pain rehabilitation involving 40 subjects, 5 exercises, and 4 physiotherapists is then presented to the effectiveness of the proposed system. Results of the validation of the system aimed at the assessment of the reliability of the proposed approach show high correlations between clinician evaluation and FIS scores. In this scenario, due to the high correlation, each FIS could represent a virtual alter-ego of the physiotherapist which enable a real time and free second opinion.

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“…This completely hands-free control of electronic devices was enabled by using an infrared projector and camera with a special microchip to track the three-dimensional (3-D) movement of objects and individuals [1,2]. Microsoft Kinect is an inexpensive, unobtrusive, and easily applied technology that is useful for monitoring subjects during clinical features [3][4][5][6]. Microsoft Kinect for the Windows software development kit (SDK) includes skeletal tracking, whereby 20 virtual anatomical joint trajectories are extracted from a depth map using a pixel-level semantic-segmentation algorithm [7][8][9][10].…”

mentioning

confidence: 99%

Evaluation of Skills in Cardiopulmonary Resuscitation (CPR) Using Microsoft Kinect

Okamoto¹,

Iwashiki²,

Sato³

et al. 2018

JMEA

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Cardiopulmonary resuscitation, commonly known as CPR, is an emergency procedure that normally combines chest compression with artificial ventilation in an effort to preserve intact brain function manually until further measures can be taken to restore spontaneous blood circulation and breathing in a person who is in cardiac arrest. In this study, we evaluated the skills of CPR practitioners on the basis of kinematic data obtained from their body movements while performing CPR. In particular, we used a Microsoft Kinect sensor to evaluate CPR performance by new and more experienced practitioners and to analyze CPR skill-building techniques. Such measurement using the Kinect sensor enabled detailed information about motion at body joints to be displayed quickly and objectively, thus facilitating identification of any problems. However, we could not confirm gesture recognition and detailed motion analysis based on using a high-speed camera to capture three-dimensional (3-D) motion of the entire body, we determined the Kinect sensor to be an easily applied evaluation tool that can provide body-motion information quickly and thus serve as an objective index for evaluating CPR performance.

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A tool for home-based rehabilitation allowing for clinical evaluation in a visual markerless scenario

Cited by 11 publications

References 13 publications

A review of computational approaches for evaluation of rehabilitation exercises

A review of computational approaches for evaluation of rehabilitation exercises

Collaborative design of a telerehabilitation system enabling virtual second opinion based on fuzzy logic

Evaluation of Skills in Cardiopulmonary Resuscitation (CPR) Using Microsoft Kinect

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