A prosthetic limb managed by sensors-based electronic system: Experimental results on amputees

Gaetani, Federico; Fazio, Roberto De; Zappatore, Giovanni Antonio; Visconti, Paolo

doi:10.11591/eei.v9i2.2101

Cited by 12 publications

(18 citation statements)

References 21 publications

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“…The initial search yielded 108 articles. Once the duplicates were removed and eligibility criteria were applied, 10 articles with a total of 180 participants were included in the review [ 4 , 7 , 9 , 10 , 11 , 18 , 19 , 20 , 21 , 22 ]. The characteristics of included studies are shown in Table 2 and Table 3 .…”

Section: Resultsmentioning

confidence: 99%

An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review

Marcos-Antón

Gor‐García‐Fogeda

Cano‐de‐la‐Cuerda

2022

JCM

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Background: The ability to perform activities of daily living (ADL) is essential to preserving functional independence and quality of life. In recent years, rehabilitation strategies based on new technologies, such as MYO Armband®, have been implemented to improve dexterity in people with upper limb impairment. Over the last few years, many studies have been published focusing on the accuracy of the MYO Armband® to capture electromyographic and inertial data, as well as the clinical effects of using it as a rehabilitation tool in people with loss of upper limb function. Nevertheless, to our knowledge, there has been no systematic review of this subject. Methods: A systematically comprehensive literature search was conducted in order to identify original studies that answered the PICO question (patient/population, intervention, comparison, and outcome): What is the accuracy level and the clinical effects of the MYO Armband® in people with motor impairment of the upper limb compared with other assessment techniques or interventions or no intervention whatsoever? The following data sources were used: Pubmed, Scopus, Web of Science, ScienceDirect, Physiotherapy Evidence Database, and the Cochrane Library. After identifying the eligible articles, a cross-search of their references was also completed for additional studies. The following data were extracted from the papers: study design, disease or condition, intervention, sample, dosage, outcome measures or data collection procedure and data analysis and results. The authors independently collected these data following the CONSORT 2010 statement when possible, and eventually reached a consensus on the extracted data, resolving disagreements through discussion. To assess the methodological quality of papers included, the tool for the critical appraisal of epidemiological cross-sectional studies was used, since only case series studies were identified after the search. Additionally, the articles were classified according to the levels of evidence and grades of recommendation for diagnosis studies established by the Oxford Center for Evidence-Based Medicine. Also, The Cochrane Handbook for Systematic Reviews of Interventions was used by two independent reviewers to assess risk of bias, assessing the six different domains. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed to carry out this review. Results: 10 articles with a total 180 participants were included in the review. The characteristics of included studies, sample and intervention characteristics, outcome measures, the accuracy of the system and effects of the interventions and the assessment of methodological quality of the studies and risk of bias are shown. Conclusions: Therapy with the MYO Armband® has shown clinical changes in range of motion, dexterity, performance, functionality and satisfaction. It has also proven to be an accurate system to capture signals from the forearm muscles in people with motor impairment of the upper limb. However, further research should be conducted using bigger samples, well-defined protocols, comparing with control groups or comparing with other assessment or therapeutic tools, since the studies published so far present a high risk of bias and low level of evidence and grade of recommendation.

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Section: Resultsmentioning

confidence: 99%

An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review

Marcos-Antón

Gor‐García‐Fogeda

Cano‐de‐la‐Cuerda

2022

JCM

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“…Burboa et al, (17) en su estudio, llevaron a cabo una relación entre los efectos del entrenamiento con respecto a los cambios en la angulación articular de miembros inferiores, encontrando que sí existe relación entre la fatiga generada en una sesión y los cambios en la angulación articular, siendo evidente la fiabilidad de las IMU en estas actividades. De igual manera, se emplearon como herramientas para la realización de evaluación de movimientos en diferentes entornos (13) , haciendo uso de ellos en gran medida, en estudios relacionados con los parámetros de la marcha, en la medición de variables como velocidad, posicionamiento de los miembros inferiores (10) , parámetros anormales de la marcha (11) y estimación de la rotación del movimiento de los usuarios para ser recreados y analizados con mayor profundidad en softwares de análisis de movimiento (51) .…”

Section: Discussionunclassified

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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“…Today technology is increasingly in close contact with humanity, an inseparable bond that promises, used with intellect and parsimony, to improve our lives in every aspect [ 1 , 2 , 3 ]. Thanks to the Internet of Things (IoT), these technologies allow us to be connected and keep under control every aspect of our life, from the home to the car, to health monitoring [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

Fazio

Perrone

Velázquez

et al. 2021

Sensors

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The evolution of low power electronics and the availability of new smart materials are opening new frontiers to develop wearable systems for medical applications, lifestyle monitoring, and performance detection. This paper presents the development and realization of a novel smart insole for monitoring the plantar pressure distribution and gait parameters; indeed, it includes a piezoresistive sensing matrix based on a Velostat layer for transducing applied pressure into an electric signal. At first, an accurate and complete characterization of Velostat-based pressure sensors is reported as a function of sizes, support material, and pressure trend. The realization and testing of a low-cost and reliable piezoresistive sensing matrix based on a sandwich structure are discussed. This last is interfaced with a low power conditioning and processing section based on an Arduino Lilypad board and an analog multiplexer for acquiring the pressure data. The insole includes a 3-axis capacitive accelerometer for detecting the gait parameters (swing time and stance phase time) featuring the walking. A Bluetooth Low Energy (BLE) 5.0 module is included for transmitting in real-time the acquired data toward a PC, tablet or smartphone, for displaying and processing them using a custom Processing® application. Moreover, the smart insole is equipped with a piezoelectric harvesting section for scavenging energy from walking. The onfield tests indicate that for a walking speed higher than 1 ms−1, the device’s power requirements (i.e., ) was fulfilled. However, more than 9 days of autonomy are guaranteed by the integrated 380-mAh Lipo battery in the total absence of energy contributions from the harvesting section.

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A prosthetic limb managed by sensors-based electronic system: Experimental results on amputees

Cited by 12 publications

References 21 publications

An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review

An sEMG-Controlled Forearm Bracelet for Assessing and Training Manual Dexterity in Rehabilitation: A Systematic Review

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

Development of a Self-Powered Piezo-Resistive Smart Insole Equipped with Low-Power BLE Connectivity for Remote Gait Monitoring

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