About: Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor

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scientific article published on 01 January 2019

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rdf:type	Item
description	wetenschappelijk artikel (nl) artikull shkencor i botuar më 01 janar 2019 (sq) scientific article published on 01 January 2019 (en) artículu científicu espublizáu en xineru de 2019 (ast) im Jahr 2019 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована 1 січня 2019 (uk) գիտական հոդված հրատարակված 2019 թվականի հունվարի 1-ին (hy)
publication date	wds:Q92891162-FA8B15A4-2C0F-4FDE-9824-FA375F2BD4DC
publication date	2019-01-01 00:00:00Z (xsd:dateTime)
cites work	wds:Q92891162-132DD053-2D16-4D61-8700-382E7AED13E0 wds:Q92891162-1B0D4437-250F-4676-A2CB-86791174AF98 wds:Q92891162-2D3FA671-FFD3-4655-A07E-A51669B3431A wds:Q92891162-37239DB6-2061-4060-856F-2B2516DAFE85 wds:Q92891162-39254635-0919-46F8-AC56-A13B59913CEC wds:Q92891162-3ACDC178-7C77-43B7-BD03-61262401C5CB wds:Q92891162-4C847E26-43E7-40DF-8290-18C94C921552 wds:Q92891162-514D73AC-43D0-4F81-B97E-A40DBC092BCF wds:Q92891162-5375CE7A-4DB3-415C-940C-E16F027CCAA1 wds:Q92891162-55EE350B-709C-4359-BA30-01BA7D9C977F wds:Q92891162-5EBFEDD9-3607-4705-8B50-25B06347FF21 wds:Q92891162-649D8E59-E735-47D1-AC47-CE7FAF561BD4 wds:Q92891162-6CA93D4C-4199-4A70-8432-1348A8BDD14F wds:Q92891162-95A23784-093F-488A-8145-D832800C065D wds:Q92891162-AB075540-D191-4E6F-AC6E-EAF69E2DA631 wds:Q92891162-AD4CDB11-E181-4E7D-A8FC-2A20776B11C4 wds:Q92891162-AD633B4A-8A7E-4B26-8857-C52557A0CB91 wds:Q92891162-B4A5AE79-3037-4E7D-8305-877B2616FB6C wds:Q92891162-BB759DDD-5595-44D9-A74D-949D3DC50F17 wds:Q92891162-CC5DFE08-1587-4F6C-9DCC-E82DDDE4B446 wds:Q92891162-CD0F6B8A-60D0-4556-AFEE-B2F4B0BA6A42 wds:Q92891162-D5423888-1F27-4861-9FE3-B7506A67EF69 wds:Q92891162-D5A60959-0EEE-4201-9A9C-DF907C50C732 wds:Q92891162-DC357C4A-05D5-42C9-A912-E721835B9BC7 wds:Q92891162-F149BEEB-BCE3-492E-8299-7FC42F690E92 wds:Q92891162-FA771149-6718-4DBC-9E61-B9532B595A35
cites work	Biofeedback in rehabilitation. An inertial and magnetic sensor based technique for joint angle measurement. Task Force on Standards for Measurement in Physical Therapy. Standards for tests and measurements in physical therapy practice Psychometric properties of measurement tools for quantifying knee joint position and movement: a systematic review Clinical measurement of range of motion. Review of goniometry emphasizing reliability and validity Reliability of goniometric measurements and visual estimates of knee range of motion obtained in a clinical setting Intra- and intertester reliability and criterion validity of the parallelogram and universal goniometers for measuring maximum active knee flexion and extension of patients with knee restrictions Reliability and concurrent validity of knee angle measurement: smart phone app versus universal goniometer used by experienced and novice clinicians. Performance Evaluation of Smartphone Inertial Sensors Measurement for Range of Motion Estimation of IMU and MARG orientation using a gradient descent algorithm. IMU-based joint angle measurement for gait analysis Functional calibration procedure for 3D knee joint angle description using inertial sensors. Development of a low-cost attitude and heading reference system using a three-axis rotating platform Reproducibility of goniometric measurement of the knee in the in-hospital phase following total knee arthroplasty A quantitative analysis of knee motion during activities of daily living Inertial sensor-based knee flexion/extension angle estimation Development of a body joint angle measurement system using IMU sensors. Evaluation of knee range of motion: Correlation between measurements using a universal goniometer and a smartphone goniometric application Human Joint Angle Estimation with Inertial Sensors and Validation with A Robot Arm. Wearable Sensor-Based Exercise Biofeedback for Orthopaedic Rehabilitation: A Mixed Methods User Evaluation of a Prototype System Shoulder and elbow joint angle tracking with inertial sensors. A basic study on variable-gain Kalman filter based on angle error calculated from acceleration signals for lower limb angle measurement with inertial sensors Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review Skin mounted accelerometer system for measuring knee range of motion. Joint Motions of the Lower Limb during Ergometer Cycling Artificial intelligence and machine learning \| applications in musculoskeletal physiotherapy
author name string	wds:Q92891162-41CAFF77-F7B9-48DE-B365-F6E7936316BC wds:Q92891162-BEACEE9B-1525-4E05-BC22-11B6424B9B84 wds:Q92891162-D161964A-F47A-4ACA-8C63-A3395A1A676F
author name string	Martin O'Reilly Alexandria Remus Sean Drummond
rdfs:label	Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (en) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (nl) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (sq)
skos:prefLabel	Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (en) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (nl) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (sq)
name	Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (en) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (nl) Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (sq)
author	wds:Q92891162-48CA748C-8CE6-472B-A390-D278500FA84E wds:Q92891162-F18ED76F-D6F7-464C-9013-22036D87C5DD
author	Brian Caulfield Rob Argent
title	wds:Q92891162-143391DB-FAFD-48F9-ABD5-0232EC2828E6
title	Evaluating the use of machine learning in the assessment of joint angle using a single inertial sensor (en)
page(s)	wds:Q92891162-8E8EB101-89CC-45F8-85EF-711AB288A3C1
page(s)	2055668319868544
instance of	wds:Q92891162-F352877D-5038-471B-A76B-8A8BC4A12E3A
instance of	scholarly article
main subject	wds:Q92891162-9C2A12F3-611F-40BF-BEF0-413DC20BD61E
main subject	machine learning
PubMed ID	wds:Q92891162-7A895D8E-173F-4602-B4EF-4E5DB94F0C28
PubMed ID	http://rdf.ncbi.nlm.nih.gov/pubchem/reference/31452927
PubMed ID	31452927
published in	wds:Q92891162-E9BEC451-B081-4BF0-863B-23D4C71ED103
published in	Journal of rehabilitation and assistive technologies engineering
volume	wds:Q92891162-18B6B843-DD12-40E5-9359-3661C877AA6D
volume	6
DOI	wds:Q92891162-0FDBF59C-E67D-4294-B398-2D360ED995F8
DOI	http://dx.doi.org/10.1177/2055668319868544
DOI	10.1177/2055668319868544
describes a project that uses	wds:Q92891162-7B09FB73-C910-4C20-9625-F6FB56F0705A

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