Concurrent validity of a wearable IMU for objective assessments of functional movement quality and control of the lumbar spine

Beange, Kristen H.E.; Chan, Adrian D. C.; Beaudette, Shawn M.; Graham, Ryan B.

doi:10.1016/j.jbiomech.2019.109356

Cited by 56 publications

(47 citation statements)

References 48 publications

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“…Another aspect of the research was the accuracy offered by IMUs, which is why they were compared with systems recognized as gold standards in motion analysis [ 41 , 42 , 43 , 44 , 45 ], obtaining acceptable and trustworthy results in different fields, including that of medicine. Moreover, one of the five experiments described in [ 14 ] approached the comparison of IMUs to the Vicon Motion Analysis system configured with seven Vicon Bonita infrared cameras.…”

Section: Resultsmentioning

confidence: 99%

“…In this sense, comparisons were made of different motion capture systems with respect to Vicon, OptiTrack, or Qualisys considered to be the gold standard, against which those systems using inertial measurement units have shown a comparable performance [ 41 , 42 , 43 , 44 , 45 , 62 ] denoting the reliability, accessibility, accuracy, and portability offered by IMUs. In this way, inertial sensors become a good option to be used in the medical field to support motor and functional recovery processes, which require precise measurements with an accessible cost in order to be mass-produced.…”

Section: Discussionmentioning

confidence: 99%

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Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Alarcón-Aldana

Callejas-Cuervo

Bó

2020

Sensors

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The use of videogames and motion capture systems in rehabilitation contributes to the recovery of the patient. This systematic review aimed to explore the works related to these technologies. The PRISMA method (Preferred Reporting Items for Systematic reviews and Meta-Analyses) was used to search the databases Scopus, PubMed, IEEE Xplore, and Web of Science, taking into consideration four aspects: physical rehabilitation, the use of videogames, motion capture technologies, and upper limb rehabilitation. The literature selection was limited to open access works published between 2015 and 2020, obtaining 19 articles that met the inclusion criteria. The works reported the use of inertial measurement units (37%), a Kinect sensor (48%), and other technologies (15%). It was identified that 26% used commercial products, while 74% were developed independently. Another finding was that 47% of the works focus on post-stroke motor recovery. Finally, diverse studies sought to support physical rehabilitation using motion capture systems incorporating inertial units, which offer precision and accessibility at a low cost. There is a clear need to continue generating proposals that confront the challenges of rehabilitation with technologies which offer precision and healthcare coverage, and which, additionally, integrate elements that foster the patient’s motivation and participation.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Alarcón-Aldana

Callejas-Cuervo

Bó

2020

Sensors

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“…Con respecto estructuras específicas de la articulación de la rodilla y en relación con su complejo ligamentoso, las IMU fueron útiles en la medición del desplazamiento angular, rotacional externo, aceleración del segmento (24) e índice de simetría de movimientos de las extremidades inferiores durante la prueba del escalón, estos se aplicaron en personas sanas y atletas con lesión de ligamentos de la rodilla, proporcionando un análisis cuantitativo para detectar diferencias cinemáticas después de una lesión de estas estructuras (25) . Además, combinados con plataformas de fuerza, permitieron determinar alteración en la distribución de cargas de la rodilla y su relación con la velocidad angular de esta misma articulación, posterior a una reconstrucción de ligamento cruzado anterior (52) ; los resultados de estos estudios indicaron que estos dispositivos se perfilan como una herramienta complementaria para el cuerpo médico, en pro de facilitar la toma de decisiones con respecto al retorno del deportista, después de la lesión de los ligamentos de la rodilla (25) .…”

Section: Discussionunclassified

“…Mantilla, 2017 (21) Emplear los acelerómetros en rehabilitación funcional de personas con condiciones de salud especifica (21) Leardini, et al, 2014 (22) Gómez, et al, 2018 (23) Yasuma, et al, 2020 (24) Kim, et al, 2020 (25) Wilches, et al, 2007 (26) Beange, et al, 2019 (27) Ganesan, et al, 2015 (28) Wang, et al, 2017 (29) Fantozzi, et al, 2015 (30) Utilizar las unidades IMU en rehabilitación física como forma de retroalimentación a los usuarios (22)(23)(24)(25)(26) o a dispositivos como exoesqueletos, que participan en procesos de rehabilitación funcional (28,29,30) Johnston, et al, 2019 (31) Li, et al, 2020 (32) Liu, et al, 2018 (33) Evaluar el control postural (31) , rangos de movimiento articular y patrones de movimiento (32,33) Caliandro, et al, 2019 (34) Gerber, et al, 2019 (35) Wolff, et al, 2020 (36) Oubre, et al, 2020 (37) Facilitar los procesos de neuro-rehabilitación en usuarios con parálisis cerebral, ataxia y secuelas de enfermedad cerebro vascular (34)(35)(36)(37) Clemens, et al, 2020 (38) Permitir detectar diferencias biomecánicas en personas con amputaciones transfemorales y transtibiales (38) Los sensores inerciales establecen medidas en los procesos de micro y macro ciclos de atletas amateurs o élite, cuantificando las condiciones de velocidad y aceleración de gestos técnicos específicos (2,8) , o incluso medir fases y ciclos de actividades como la carrera y la marcha (9) ; brindando...…”

Section: Autores/ Año Usos Y Aplicaciones De Los Imu's En Fisioterapiaunclassified

Uso de sensores inerciales en fisioterapia: Una aproximación a procesos de evaluación del movimiento humano

et al. 2020

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Introducción: Los sensores inerciales o unidad de medición inercial (IMU) del inglés Inertial measurement unit, son pequeños dispositivos capaces de medir la aceleración lineal y la velocidad angular, siendo útiles en el área de la salud para la cuantificación y valoración objetiva del movimiento corporal humano. Objetivo: Analizar la información sobre el uso de sensores inerciales como una aproximación a los procesos de evaluación del movimiento corporal humano. Materiales y métodos: Se realizó búsqueda en bases de datos, empleando términos: sensores inerciales, salud, fisioterapia, acelerómetro, actividad física, movimiento y rehabilitación, y sus combinaciones. Como criterios de exclusión se tuvo: artículos exclusivos del campo de ingeniería con información no aplicable a fisioterapia. Resultados: Una IMU es compatible con aplicaciones (APP), con el objetivo de obtener datos de movimiento tridimensionales y como evaluación e intervención, o que permita cuantificar los resultados de la acción motora. Conclusiones: Las IMU tienen amplias posibilidades en áreas afines a la rehabilitación y otras referentes al entrenamiento y el área deportiva; por lo, cual es necesario estandarizar protocolos que permitan la medición de patrones motores que favorezcan los procesos de rehabilitación.

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“…Machine learning techniques 4 over data from wearable motion tracking devices based on inertial measurement units (IMUs) [5][6][7][8][9] are another major research line, including their use for medical purposes. 10 Diverse feature extraction methods have been proposed, such as the fractal dimension 11 for the characterization of walking path tortuosity of aging person, space-time representations of actions, 12 or the application of conditional random fields 13 for human motion recognition. Information fusion for human activity recognition [14][15][16] has targeted the combination of same kind sensors.…”

Section: Introductionmentioning

confidence: 99%

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

Graña

Aguilar-Moreno

Asiaín

et al. 2020

Int. J. Neur. Syst.

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Human activity recognition and neural activity analysis are the basis for human computational neureoethology research dealing with the simultaneous analysis of behavioral ethogram descriptions and neural activity measurements. Wireless electroencephalography (EEG) and wireless inertial measurement units (IMU) allow the realization of experimental data recording with improved ecological validity where the subjects can be carrying out natural activities while data recording is minimally invasive. Specifically, we aim to show that EEG and IMU data fusion allows improved human activity recognition in a natural setting. We have defined an experimental protocol composed of natural sitting, standing and walking activities, and we have recruited subjects in two sites: in-house ([Formula: see text]) and out-house ([Formula: see text]) populations with different demographics. Experimental protocol data capture was carried out with validated commercial systems. Classifier model training and validation were carried out with scikit-learn open source machine learning python package. EEG features consist of the amplitude of the standard EEG frequency bands. Inertial features were the instantaneous position of the body tracked points after a moving average smoothing to remove noise. We carry out three validation processes: a 10-fold cross-validation process per experimental protocol repetition, (b) the inference of the ethograms, and (c) the transfer learning from each experimental protocol repetition to the remaining repetitions. The in-house accuracy results were lower and much more variable than the out-house sessions results. In general, random forest was the best performing classifier model. Best cross-validation results, ethogram accuracy, and transfer learning were achieved from the fusion of EEG and IMUs data. Transfer learning behaved poorly compared to classification on the same protocol repetition, but it has accuracy still greater than 0.75 on average for the out-house data sessions. Transfer leaning accuracy among repetitions of the same subject was above 0.88 on average. Ethogram prediction accuracy was above 0.96 on average. Therefore, we conclude that wireless EEG and IMUs allow for the definition of natural experimental designs with high ecological validity toward human computational neuroethology research. The fusion of both EEG and IMUs signals improves activity and ethogram recognition.

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Concurrent validity of a wearable IMU for objective assessments of functional movement quality and control of the lumbar spine

Cited by 56 publications

References 48 publications

Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Upper Limb Physical Rehabilitation Using Serious Videogames and Motion Capture Systems: A Systematic Review

Uso de sensores inerciales en fisioterapia: Una aproximación a procesos de evaluación del movimiento humano

Improved Activity Recognition Combining Inertial Motion Sensors and Electroencephalogram Signals

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