DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test / Renaud Hage in Sensors, 20 (3) (Novembre 2019)  

Public ISBD [article]  inSensors > 20 (3) (Novembre 2019) Titre : DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test Type de document : document électronique Auteurs : Renaud Hage ; Christine Detrembleur ; Frédéric Dierick ; Laurent Pitance ; L. Jojczyk ; Wesley Estievenart ; Fabien Buisseret Année de publication : 2019 Note générale : https://doi.org/10.3390/s20030833 Langues : Anglais (eng) Mots-clés : inertial sensor  kinematics  head rotation  ecological research Résumé : first_page settings Open AccessArticle DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test by Renaud Hage 1,2,* [OrcID] , Christine Detrembleur 1 [OrcID] , Frédéric Dierick 2,3, Laurent Pitance 1, Laurent Jojczyk 2, Wesley Estievenart 2 and Fabien Buisseret 2,4 1 Laboratoire NMSK, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1200 Brussels, Belgium 2 CeREF, Chaussée de Binche 159, 7000 Mons, Belgium 3 Centre National de Rééducation Fonctionnelle et de Réadaptation—Rehazenter, Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), 2674 Luxembourg, Luxembourg 4 Service de Physique Nucléaire et Subnucléaire, UMONS, Research Institute for Complex Systems, 20 Place du Parc, 7000 Mons, Belgium * Author to whom correspondence should be addressed. Sensors 2020, 20(3), 833; https://doi.org/10.3390/s20030833 Received: 27 November 2019 / Revised: 10 January 2020 / Accepted: 3 February 2020 / Published: 4 February 2020 (This article belongs to the Special Issue Low-Cost Sensors and Biological Signals) Download PDF Browse Figures Cite This Paper Abstract Various noninvasive measurement devices can be used to assess cervical motion. The size, complexity, and cost of gold-standard systems make them not suited to clinical practice, and actually difficult to use outside a dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available, but without any packaging or a user-friendly interface. The so-called DYSKIMOT is a home-designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in “real-life situations”. DYSKIMOT was compared with a gold-standard optoelectronic system (Elite). Our goal was to evaluate the DYSKIMOT accuracy in assessing fast head rotations kinematics. Kinematics was simultaneously recorded by systems during the execution of the DidRen Laser test and performed by 15 participants and nine patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing–Bablok regressions, and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events’ measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in both clinical practice and research environments. En ligne : https://www.mdpi.com/1424-8220/20/3/833/htm Permalink : ./index.php?lvl=notice_display&id=84502 [article] DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test [document électronique] / Renaud Hage ; Christine Detrembleur ; Frédéric Dierick ; Laurent Pitance ; L. Jojczyk ; Wesley Estievenart ; Fabien Buisseret . - 2019. https://doi.org/10.3390/s20030833 Langues : Anglais (eng) in Sensors > 20 (3) (Novembre 2019) Mots-clés : inertial sensor  kinematics  head rotation  ecological research Résumé : first_page settings Open AccessArticle DYSKIMOT: An Ultra-Low-Cost Inertial Sensor to Assess Head’s Rotational Kinematics in Adults during the Didren-Laser Test by Renaud Hage 1,2,* [OrcID] , Christine Detrembleur 1 [OrcID] , Frédéric Dierick 2,3, Laurent Pitance 1, Laurent Jojczyk 2, Wesley Estievenart 2 and Fabien Buisseret 2,4 1 Laboratoire NMSK, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, 1200 Brussels, Belgium 2 CeREF, Chaussée de Binche 159, 7000 Mons, Belgium 3 Centre National de Rééducation Fonctionnelle et de Réadaptation—Rehazenter, Laboratoire d’Analyse du Mouvement et de la Posture (LAMP), 2674 Luxembourg, Luxembourg 4 Service de Physique Nucléaire et Subnucléaire, UMONS, Research Institute for Complex Systems, 20 Place du Parc, 7000 Mons, Belgium * Author to whom correspondence should be addressed. Sensors 2020, 20(3), 833; https://doi.org/10.3390/s20030833 Received: 27 November 2019 / Revised: 10 January 2020 / Accepted: 3 February 2020 / Published: 4 February 2020 (This article belongs to the Special Issue Low-Cost Sensors and Biological Signals) Download PDF Browse Figures Cite This Paper Abstract Various noninvasive measurement devices can be used to assess cervical motion. The size, complexity, and cost of gold-standard systems make them not suited to clinical practice, and actually difficult to use outside a dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available, but without any packaging or a user-friendly interface. The so-called DYSKIMOT is a home-designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in “real-life situations”. DYSKIMOT was compared with a gold-standard optoelectronic system (Elite). Our goal was to evaluate the DYSKIMOT accuracy in assessing fast head rotations kinematics. Kinematics was simultaneously recorded by systems during the execution of the DidRen Laser test and performed by 15 participants and nine patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing–Bablok regressions, and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events’ measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in both clinical practice and research environments. En ligne : https://www.mdpi.com/1424-8220/20/3/833/htm Permalink : ./index.php?lvl=notice_display&id=84502

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Ergonomic Risk Assessment of Developing Musculoskeletal Disorders in Workers with the Microsoft Kinect: TRACK TMS / Fabien Buisseret  

Public ISBD Titre : Ergonomic Risk Assessment of Developing Musculoskeletal Disorders in Workers with the Microsoft Kinect: TRACK TMS Type de document : document électronique Auteurs : Fabien Buisseret ; Frédéric Dierick ; O. Hamzaoui ; L. Jojczyk Année de publication : 2017 Note générale : Cet article est une pré-publication. La version définitive a été publiée dans la revue "IRBM. Innovation and Research in BioMedical engineering", Volume 39, Issue 6, Pages 377-450 (December 2018) sous le doi.org/10.1016/j.irbm.2018.10.0036. Langues : Anglais (eng) Mots-clés : 3D kinematics  Kinect  Ergonomics  OCRA  Musicians Résumé : Background Routine ergonomic assessment of postures and gestures in the workplace are mostly conducted by visual observations, either direct or based on video recordings. Nowadays, low-cost three-dimensional cameras like Microsoft Kinect offers the possibility of recording the full kinematics of workers in a non-intrusive way, providing a more precise, and reliable assessment of their motor strategies. Methods We have developed a tracking application using the Kinect SDK for Windows in C♯, allowing the simultaneous recording of the three-dimensional coordinates of all the body points tracked by the Microsoft Kinect at a sampling frequency of 30 Hz and an expected accuracy of 3 cm. Measurements are performed on violinists, whose playing is representative of a work situation involving repeated gestures and postures that can be described as non-ergonomic. Results Microsoft Kinect can be efficiently used to quantify the motion performed by the violinists. Playing strategies can even be noticed despite the low-cost nature of the sensor used. Conclusion Low-cost three-dimensional cameras can be a useful aid in ergonomic risk assessment of developing musculoskeletal disorders and give the example of the repetition of movements and postural items included in the OCRA checklist, whose scoring can be facilitated by such a device. En ligne : https://arxiv.org/pdf/1710.09682.pdf Permalink : ./index.php?lvl=notice_display&id=84493 Ergonomic Risk Assessment of Developing Musculoskeletal Disorders in Workers with the Microsoft Kinect: TRACK TMS [document électronique] / Fabien Buisseret ; Frédéric Dierick ; O. Hamzaoui ; L. Jojczyk . - 2017. Cet article est une pré-publication. La version définitive a été publiée dans la revue "IRBM. Innovation and Research in BioMedical engineering", Volume 39, Issue 6, Pages 377-450 (December 2018) sous le doi.org/10.1016/j.irbm.2018.10.0036. Langues : Anglais (eng) Mots-clés : 3D kinematics  Kinect  Ergonomics  OCRA  Musicians Résumé : Background Routine ergonomic assessment of postures and gestures in the workplace are mostly conducted by visual observations, either direct or based on video recordings. Nowadays, low-cost three-dimensional cameras like Microsoft Kinect offers the possibility of recording the full kinematics of workers in a non-intrusive way, providing a more precise, and reliable assessment of their motor strategies. Methods We have developed a tracking application using the Kinect SDK for Windows in C♯, allowing the simultaneous recording of the three-dimensional coordinates of all the body points tracked by the Microsoft Kinect at a sampling frequency of 30 Hz and an expected accuracy of 3 cm. Measurements are performed on violinists, whose playing is representative of a work situation involving repeated gestures and postures that can be described as non-ergonomic. Results Microsoft Kinect can be efficiently used to quantify the motion performed by the violinists. Playing strategies can even be noticed despite the low-cost nature of the sensor used. Conclusion Low-cost three-dimensional cameras can be a useful aid in ergonomic risk assessment of developing musculoskeletal disorders and give the example of the repetition of movements and postural items included in the OCRA checklist, whose scoring can be facilitated by such a device. En ligne : https://arxiv.org/pdf/1710.09682.pdf Permalink : ./index.php?lvl=notice_display&id=84493

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Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People / Fabien Buisseret  

Public ISBD Titre : Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People Type de document : document électronique Auteurs : Fabien Buisseret ; Louis Catinus ; Remi Grenard ; L. Jojczyk ; Dylan Fievez ; Vincent Barvaux ; Frédéric Dierick Année de publication : 2020 Langues : Français (fre) Résumé : Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants. En ligne : https://luck.synhera.be/bitstream/handle/123456789/271/sensors-20-03207.pdf?sequ [...] Permalink : ./index.php?lvl=notice_display&id=98163 Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People [document électronique] / Fabien Buisseret ; Louis Catinus ; Remi Grenard ; L. Jojczyk ; Dylan Fievez ; Vincent Barvaux ; Frédéric Dierick . - 2020. Langues : Français (fre) Résumé : Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants. En ligne : https://luck.synhera.be/bitstream/handle/123456789/271/sensors-20-03207.pdf?sequ [...] Permalink : ./index.php?lvl=notice_display&id=98163

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