McGrath MJ, Scanaill CN. Wellness, fitness, and lifestyle sensing applications. In: Sensor technologies. Springer; 2013. p. 217–48.
Zheng YL, Ding XR, Poon CCY, Lo BPL, Zhang H, Zhou XL, et al. Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng. 2014;61(5):1538–54.
Article
Google Scholar
De Vito L, Postolache O, Rapuano S. Measurements and sensors for motion tracking in motor rehabilitation. IEEE Instrum Meas Mag. 2014;17(3):30–8.
Article
Google Scholar
Cappozzo A, Della Croce U, Leardini A, Chiari L. Human movement analysis using stereophotogrammetry: part 1: theoretical background. Gait Posture. 2005;21(2):186–96.
Google Scholar
Moeslund TB, Hilton A, Krüger V. A survey of advances in vision-based human motion capture and analysis. Comput Vis Image Underst. 2006;104(2):90–126.
Article
Google Scholar
Saber-Sheikh K, Bryant EC, Glazzard C, Hamel A, Lee RY. Feasibility of using inertial sensors to assess human movement. Manual Ther. 2010;15(1):122–5.
Article
Google Scholar
Shull PB, Jirattigalachote W, Hunt MA, Cutkosky MR, Delp SL. Quantified self and human movement: a review on the clinical impact of wearable sensing and feedback for gait analysis and intervention. Gait Posture. 2014;40(1):11–9.
Article
Google Scholar
Leardini A, Lullini G, Giannini S, Berti L, Ortolani M, Caravaggi P. Validation of the angular measurements of a new inertial-measurement-unit based rehabilitation system: comparison with state-of-the-art gait analysis. J Neuroeng Rehabil. 2014;11(1):1–7.
Article
Google Scholar
Robert-Lachaine X, Mecheri H, Larue C, Plamondon A. Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis. Med Biol Eng Comput. 2016;55:609–19.
Article
Google Scholar
Swan M. Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0. J Sens Actuator Netw. 2012;1(3):217–53.
Article
Google Scholar
Ferguson T, Rowlands AV, Olds T, Maher C. The validity of consumer-level, activity monitors in healthy adults worn in free-living conditions: a cross-sectional study. Int J Behav Nutr Phys Act. 2015;12(1):42.
Article
Google Scholar
Orwat C, Rashid A, Holtmann C, Wolk M, Scheermesser M, Kosow H, et al. Adopting pervasive computing for routine use in healthcare. IEEE Pervasive Comput. 2010;9(2):64–71.
Article
Google Scholar
Banaee H, Ahmed MU, Loutfi A. Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges. Sensors. 2013;13(12):17472–500.
Article
Google Scholar
Lange B, Chang CY, Suma E, Newman B, Rizzo AS, Bolas M. Development and evaluation of low cost game-based balance rehabilitation tool using the Microsoft Kinect sensor. In: Intl. Conf. of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2011. p. 1831–4.
Pantelopoulos A, Bourbakis NG. A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans Syst Man Cybern Part C Appl Rev. 2010;40(1):1–12.
Article
Google Scholar
Trombetta M, Henrique PPB, Brum MR, Colussi EL, De Marchi ACB, Rieder R. Motion Rehab AVE 3D: a VR-based exergame for post-stroke rehabilitation. Comput Methods Prog Biomed. 2017;151:15–20.
Article
Google Scholar
Poppe R. Vision-based human motion analysis: an overview. Comput Vis Image Underst. 2007;108(1):4–18.
Article
Google Scholar
Della Croce U, Leardini A, Chiari L, Cappozzo A. Human movement analysis using stereophotogrammetry: part 4: assessment of anatomical landmark misplacement and its effects on joint kinematics. Gait Posture. 2005;21(2):226–37.
Article
Google Scholar
Merriaux P, Dupuis Y, Boutteau R, Vasseur P, Savatier X. A study of vicon system positioning performance. Sensors. 2017;17(7):1591.
Article
Google Scholar
Mündermann L, Corazza S, Andriacchi TP. The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications. J Neuroeng Rehabil. 2006;3(1):6.
Article
Google Scholar
Chen L, Wei H, Ferryman J. A survey of human motion analysis using depth imagery. Pattern Recogn Lett. 2013;34(15):1995–2006.
Article
Google Scholar
Corazza S, Muendermann L, Chaudhari A, Demattio T, Cobelli C, Andriacchi TP. A markerless motion capture system to study musculoskeletal biomechanics: visual hull and simulated annealing approach. Ann Biomed Eng. 2006;34(6):1019–29.
Article
Google Scholar
Schmitz A, Ye M, Shapiro R, Yang R, Noehren B. Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system. J Biomech. 2014;47(2):587–91.
Article
Google Scholar
Shotton J, Sharp T, Kipman A, Fitzgibbon A, Finocchio M, Blake A, et al. Real-time human pose recognition in parts from single depth images. Commun ACM. 2013;56(1):116–24.
Article
Google Scholar
Zhang Z. Microsoft kinect sensor and its effect. IEEE Multimed. 2012;19(2):4–10.
Article
Google Scholar
Han J, Shao L, Xu D, Shotton J. Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans Cybern. 2013;43(5):1318–34.
Article
Google Scholar
Mousavi Hondori H, Khademi M. A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. J Med Eng. 2014;. https://doi.org/10.1155/2014/846514.
Article
Google Scholar
Yang L, Zhang L, Dong H, Alelaiwi A, El Saddik A. Evaluating and improving the depth accuracy of kinect for Windows v2. IEEE Sens J. 2015;15(8):4275–85.
Article
Google Scholar
Capecci M, Ceravolo M, Ferracuti F, Iarlori S, Longhi S, Romeo L, et al. Accuracy evaluation of the Kinect v2 sensor during dynamic movements in a rehabilitation scenario. In: Intl. Conf. of the Eng. in Medicine and Biology Society (EMBC). IEEE; 2016. p. 5409–12.
Corti A, Giancola S, Mainetti G, Sala R. A metrological characterization of the kinect V2 time-of-flight camera. Robot Autonom Syst. 2016;75:584–94.
Article
Google Scholar
Sarbolandi H, Lefloch D, Kolb A. Kinect range sensing: structured-light versus time-of-flight kinect. Comput Vis Image Underst. 2015;139:1–20.
Article
Google Scholar
Pagliari D, Pinto L. Calibration of kinect for xbox one and comparison between the two generations of Microsoft sensors. Sensors. 2015;15(11):27569–89.
Article
Google Scholar
Mortazavi F, Nadian-Ghomsheh A. Stability of Kinect for range of motion analysis in static stretching exercises. PLoS ONE. 2018;13(7):e0200992.
Article
Google Scholar
Pedro LM, de Paula Caurin GA. Kinect evaluation for human body movement analysis. In: Biomedical robotics and biomechatronics (BioRob). IEEE; 2012. p. 1856–61.
Da Gama A, Fallavollita P, Teichrieb V, Navab N. Motor rehabilitation using kinect: a systematic review. Games Health J. 2015;4(2):123–35.
Article
Google Scholar
Clark RA, Pua YH, Oliveira CC, Bower KJ, Thilarajah S, McGaw R, et al. Reliability and concurrent validity of the Microsoft Xbox one kinect for assessment of standing balance and postural control. Gait Posture. 2015;42(2):210–3.
Article
Google Scholar
Grooten WJA, Sandberg L, Ressman J, Diamantoglou N, Johansson E, Rasmussen-Barr E. Reliability and validity of a novel kinect-based software program for measuring posture, balance and side-bending. BMC Musculoskelet Disord. 2018;19(1):6.
Article
Google Scholar
Tran TH, Le TL, Hoang VN, Vu H. Continuous detection of human fall using multimodal features from kinect sensors in scalable environment. Comput Methods Prog Biomed. 2017;146:151–65.
Article
Google Scholar
Kuster RP, Heinlein B, Bauer CM, Graf ES. Accuracy of kinectone to quantify kinematics of the upper body. Gait Posture. 2016;47:80–5.
Article
Google Scholar
Otte K, Kayser B, Mansow-Model S, Verrel J, Paul F, Brandt AU, et al. Accuracy and reliability of the kinect version 2 for clinical measurement of motor function. PLoS ONE. 2016;11(11):e0166532.
Article
Google Scholar
Ma M, Proffitt R, Skubic M. Validation of a kinect V2 based rehabilitation game. PLoS ONE. 2018;13(8):e0202338.
Article
Google Scholar
Mentiplay BF, Perraton LG, Bower KJ, Pua YH, McGaw R, Heywood S, et al. Gait assessment using the Microsoft Xbox one kinect: concurrent validity and inter-day reliability of spatiotemporal and kinematic variables. J Biomech. 2015;48(10):2166–70.
Article
Google Scholar
Dolatabadi E, Taati B, Mihailidis A. Concurrent validity of the Microsoft Kinect for Windows v2 for measuring spatiotemporal gait parameters. Med Eng Phys. 2016;38(9):952–8.
Article
Google Scholar
Müller B, Ilg W, Giese MA, Ludolph N. Validation of enhanced kinect sensor based motion capturing for gait assessment. PLoS ONE. 2017;12(4):e0175813.
Article
Google Scholar
Valdés BA, Hilderman CG, Hung CT, Shirzad N, Van der Loos HM. Usability testing of gaming and social media applications for stroke and cerebral palsy upper limb rehabilitation. In: 36th annual international conference of the IEEE engineering in medicine and biology society. IEEE. 2014;2014:3602–5.
Brokaw EB, Eckel E, Brewer BR. Usability evaluation of a kinematics focused kinect therapy program for individuals with stroke. Technol Health Care. 2015;23(2):143–51.
Article
Google Scholar
Xu X, McGorry RW. The validity of the first and second generation Microsoft Kinect\(^{{rm TM}}\) for identifying joint center locations during static postures. Appl Ergon. 2015;49:47–54.
Article
Google Scholar
Wang Q, Kurillo G, Ofli F, Bajcsy R. Evaluation of pose tracking accuracy in the first and second generations of microsoft kinect. In: Intl. Conf. on healthcare informatics (ICHI). IEEE; 2015. p. 380–9.
Obdržálek Š, Kurillo G, Ofli F, Bajcsy R, Seto E, Jimison H, et al. Accuracy and robustness of Kinect pose estimation in the context of coaching of elderly population. In: Intl. Conf of the IEEE engineering in medicine and biology society (EMBS). IEEE; 2012. p. 1188–93.
Clark RA, Pua YH, Fortin K, Ritchie C, Webster KE, Denehy L, et al. Validity of the Microsoft Kinect for assessment of postural control. Gait Posture. 2012;36(3):372–7.
Article
Google Scholar
Bonnechere B, Jansen B, Salvia P, Bouzahouene H, Omelina L, Moiseev F, et al. Validity and reliability of the kinect within functional assessment activities: comparison with standard stereophotogrammetry. Gait Posture. 2014;39(1):593–8.
Article
Google Scholar
Galna B, Barry G, Jackson D, Mhiripiri D, Olivier P, Rochester L. Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture. 2014;39(4):1062–8.
Article
Google Scholar
van Diest M, Stegenga J, Wörtche HJ, Postema K, Verkerke GJ, Lamoth CJ. Suitability of kinect for measuring whole body movement patterns during exergaming. J Biomech. 2014;47(12):2925–32.
Article
Google Scholar
Schmitz A, Ye M, Boggess G, Shapiro R, Yang R, Noehren B. The measurement of in vivo joint angles during a squat using a single camera markerless motion capture system as compared to a marker based system. Gait Posture. 2015;41(2):694–8.
Article
Google Scholar
Takeda R, Tadano S, Natorigawa A, Todoh M, Yoshinari S. Gait posture estimation using wearable acceleration and gyro sensors. J Biomech. 2009;42(15):2486–94.
Article
Google Scholar
Cutti AG, Ferrari A, Garofalo P, Raggi M, Cappello A, Ferrari A. Outwalk: a protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput. 2010;48(1):17–25.
Article
Google Scholar
Ahmadi A, Mitchell E, Richter C, Destelle F, Gowing M, O’Connor NE, et al. Toward automatic activity classification and movement assessment during a sports training session. IEEE Internet Things J. 2015;2(1):23–32.
Article
Google Scholar
Giggins OM, Sweeney KT, Caulfield B. Rehabilitation exercise assessment using inertial sensors: a cross-sectional analytical study. J Neuroeng Rehabil. 2014;11(1):1–10.
Article
Google Scholar
Bergamini E, Ligorio G, Summa A, Vannozzi G, Cappozzo A, Sabatini AM. Estimating orientation using magnetic and inertial sensors and different sensor fusion approaches: accuracy assessment in manual and locomotion tasks. Sensors. 2014;14(10):18625–49.
Article
Google Scholar
Seel T, Raisch J, Schauer T. IMU-based joint angle measurement for gait analysis. Sensors. 2014;14(4):6891–909.
Article
Google Scholar
Fantozzi S, Giovanardi A, Magalhães FA, Di Michele R, Cortesi M, Gatta G. Assessment of three-dimensional joint kinematics of the upper limb during simulated swimming using wearable inertial-magnetic measurement units. J Sports Sci. 2016;34(11):1073–80.
Article
Google Scholar
Papi E, Osei-Kuffour D, Chen YMA, McGregor AH. Use of wearable technology for performance assessment: a validation study. Med Eng Phys. 2015;37(7):698–704.
Article
Google Scholar
Lebel K, Boissy P, Nguyen H, Duval C. Inertial measurement systems for segments and joints kinematics assessment: towards an understanding of the variations in sensors accuracy. Biomed Eng OnLine. 2017;16(1):56.
Article
Google Scholar
Chiang CY, Chen KH, Liu KC, Hsu SJP, Chan CT. Data collection and analysis using wearable sensors for monitoring knee range of motion after total knee arthroplasty. Sensors. 2017;17(2):418.
Article
Google Scholar
Schall MC Jr, Fethke NB, Chen H, Oyama S, Douphrate DI. Accuracy and repeatability of an inertial measurement unit system for field-based occupational studies. Ergonomics. 2015;59:591–602.
Article
Google Scholar
Tian Y, Meng X, Tao D, Liu D, Feng C. Upper limb motion tracking with the integration of IMU and kinect. Neurocomputing. 2015;159:207–18.
Article
Google Scholar
Destelle F, Ahmadi A, O’Connor NE, Moran K, Chatzitofis A, Zarpalas D, et al. Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In: 22nd European signal processing conference (EUSIPCO). IEEE; 2014. p. 371–5.
Kyrarini M, Wang X, Gräser A. Comparison of vision-based and sensor-based systems for joint angle gait analysis. In: IEEE intl. symp. on medical measurements and applications (MeMeA). IEEE; 2015. p. 375–9.
Yang GZ, Yacoub M. Body sensor networks. Berlin: Springer; 2006.
Book
Google Scholar
Hanson MA, Powell HC Jr, Barth AT, Ringgenberg K, Calhoun BH, Aylor JH, et al. Body area sensor networks: challenges and opportunities. Computer. 2009;1:58–65.
Article
Google Scholar
Roetenberg D, Luinge H, Slycke P. Xsens MVN: full 6DOF human motion tracking using miniature inertial sensors. Xsens Motion Technologies BV, technical report; 2009.
Lane ND, Miluzzo E, Lu H, Peebles D, Choudhury T, Campbell AT. A survey of mobile phone sensing. Commun Mag IEEE. 2010;48(9):140–50.
Article
Google Scholar
Burns A, Greene BR, McGrath MJ, O’Shea TJ, Kuris B, Ayer SM, et al. SHIMMER—a wireless sensor platform for noninvasive biomedical research. Sens J IEEE. 2010;10(9):1527–34.
Article
Google Scholar
Harms H, Amft O, Winkler R, Schumm J, Kusserow M, Tröster G. Ethos: miniature orientation sensor for wearable human motion analysis. In: IEEE sensors. IEEE; 2010. p. 1037–42.
Brigante C, Abbate N, Basile A, Faulisi AC, Sessa S. Towards miniaturization of a MEMS-based wearable motion capture system. IEEE Trans Ind Electron. 2011;58(8):3234–41.
Article
Google Scholar
Bruckner HP, Nowosielski R, Kluge H, Blume H. Mobile and wireless inertial sensor platform for motion capturing in stroke rehabilitation sessions. In: IEEE intl. workshop on advances in sensors and interfaces (IWASI). IEEE; 2013. p. 14–9.
Comotti D, Ermidoro M, Galizzi M, Vitali AL. Development of a wireless low-power multi-sensor network for motion tracking applications. In: Intl. conf. on wearable and implantable body sensor networks (BSN). IEEE; 2013. p. 1–6.
Rodríguez-Martín D, Pérez-López C, Samà A, Cabestany J, Català A. A wearable inertial measurement unit for long-term monitoring in the dependency care area. Sensors. 2013;13(10):14079–104.
Article
Google Scholar
Sabatini AM. Estimating three-dimensional orientation of human body parts by inertial/magnetic sensing. Sensors. 2011;11(2):1489–525.
Article
MathSciNet
Google Scholar
Bugané F, Benedetti M, Casadio G, Attala S, Biagi F, Manca M, et al. Estimation of spatial-temporal gait parameters in level walking based on a single accelerometer: validation on normal subjects by standard gait analysis. Comput Methods Prog Biomed. 2012;108(1):129–37.
Article
Google Scholar
Young AD. Comparison of orientation filter algorithms for realtime wireless inertial posture tracking. In: Intl. workshop on wearable and implantable body sensor networks (BSN). IEEE; 2009. p. 59–4.
Sabatini AM. Inertial sensing in biomechanics: a survey of computational techniques bridging motion analysis and personal navigation. In: Computational intelligence for movement sciences: neural networks and other emerging techniques. IGI Global; 2006. p. 70–100.
Mahony R, Hamel T, Pflimlin JM. Nonlinear complementary filters on the special orthogonal group. IEEE Trans Autom Control. 2008;53(5):1203–18.
Article
MathSciNet
MATH
Google Scholar
Madgwick SO, Harrison AJ, Vaidyanathan R. Estimation of IMU and MARG orientation using a gradient descent algorithm. In: IEEE intl. conf. on rehabilitation robotics (ICORR). IEEE; 2011. p. 1–7.
Bulling A, Blanke U, Schiele B. A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput Surv (CSUR). 2014;46(3):33.
Article
Google Scholar
Morris D, Saponas TS, Guillory A, Kelner I. RecoFit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM; 2014. p. 3225–34.
Altini M, Penders J, Vullers R, Amft O. Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning. IEEE J Biomed Health Inform. 2015;19(1):219–26.
Article
Google Scholar
Casamassima F, Ferrari A, Milosevic B, Ginis P, Farella E, Rocchi L. A wearable system for gait training in subjects with Parkinson’s disease. Sensors. 2014;14(4):6229–46.
Article
Google Scholar
Ferrari A, Ginis P, Hardegger M, Casamassima F, Rocchi L, Chiari L. A mobile Kalman-filter based solution for the real-time estimation of spatio-temporal gait parameters. IEEE Trans Neural Syst Rehabil Eng. 2016;24(7):764–73.
Article
Google Scholar
Picerno P, Camomilla V, Capranica L. Countermovement jump performance assessment using a wearable 3D inertial measurement unit. J Sports Sci. 2011;29(2):139–46.
Article
Google Scholar
Milosevic B, Farella E. Wearable Inertial Sensor for Jump Performance Analysis. In: Proc. of the 2015 workshop on wearable systems and applications (WearSys). ACM; 2015. p. 15–20.
Buganè F, Benedetti MG, D’Angeli V, Leardini A. Estimation of pelvis kinematics in level walking based on a single inertial sensor positioned close to the sacrum: validation on healthy subjects with stereophotogrammetric system. Biomed Eng OnLine. 2014;13(1):146.
Article
Google Scholar
Hubble RP, Naughton GA, Silburn PA, Cole MH. Wearable sensor use for assessing standing balance and walking stability in people with Parkinson’s disease: a systematic review. PLoS ONE. 2015;10(4):e0123705.
Article
Google Scholar
Cui J, Chen J, Qu G, Starkman J, Zeng X, Madigan E, et al. Wearable Gait Lab System providing quantitative statistical support for human balance tests. Smart Health. 2017;3:27–38.
Article
Google Scholar
Bagalà F, Becker C, Cappello A, Chiari L, Aminian K, Hausdorff JM, et al. Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE. 2012;7(5):e37062.
Article
Google Scholar
Ferrari A, Cutti AG, Garofalo P, Raggi M, Heijboer M, Cappello A, et al. First in vivo assessment of “Outwalk”: a novel protocol for clinical gait analysis based on inertial and magnetic sensors. Med Biol Eng Comput. 2010;48(1):1.
Article
Google Scholar
Palmerini L, Mellone S, Avanzolini G, Valzania F, Chiari L. Quantification of motor impairment in Parkinson’s disease using an instrumented timed up and go test. IEEE Trans Neural Syst Rehabil Eng. 2013;21(4):664–73.
Article
Google Scholar
Zhao Z, Etemad SA, Arya A, Whitehead A. Usability and motivational effects of a gamified exercise and fitness system based on wearable devices. In: International conference of design, user experience, and usability. Springer; 2016. p. 333–44.
Zhao Z, Arya A, Whitehead A, Chan G, Etemad SA. Keeping users engaged through feature updates: a long-term study of using wearable-based exergames. In: Conference on human factors in computing systems (CHI); 2017. p. 1053–64.
Wang Q, Markopoulos P, Yu B, Chen W, Timmermans A. Interactive wearable systems for upper body rehabilitation: a systematic review. J Neuroeng Rehabil. 2017;14(1):20.
Article
Google Scholar
Glonek G, Wojciechowski A. Kinect and IMU sensors imprecisions compensation method for human limbs tracking. In: Intl. conf. on computer vision and graphics. Springer; 2016. p. 316–28.
Haggag H, Hossny M, Nahavandi S, Haggag O. An adaptable system for rgb-d based human body detection and pose estimation: Incorporating attached props. In: IEEE int. conf. on systems, man, and cybernetics (SMC); 2016. p. 001544–9.
Shcheglov K, Evans C, Gutierrez R, Tang TK. Temperature dependent characteristics of the JPL silicon MEMS gyroscope. In: Aerospace conference proceedings. vol. 1. IEEE; 2000. p. 403–11.
Wen M, Wang W, Luo Z, Xu Y, Wu X, Hou F, et al. Modeling and analysis of temperature effect on MEMS gyroscope. In: Electronic components and technology conference (ECTC), 2014 IEEE 64th. IEEE; 2014. p. 2048–52.
Farrell J. Aided navigation: GPS with high rate sensors. New York: McGraw-Hill; 2008.
Google Scholar
Kekade S, Hseieh CH, Islam MM, Atique S, Khalfan AM, Li YC, et al. The usefulness and actual use of wearable devices among the elderly population. Comput Methods Prog Biomed. 2018;153:137–59.
Article
Google Scholar
Hiremath S, Yang G, Mankodiya K. Wearable Internet of Things: Concept, architectural components and promises for person-centered healthcare. In: Intl. conf. on wireless mobile communication and healthcare (Mobihealth). IEEE; 2014. p. 304–7.
Webster D, Celik O. Systematic review of kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil. 2014;11(1):1.
Article
Google Scholar
Chen H, Schall MC, Fethke N. Accuracy of angular displacements and velocities from inertial-based inclinometers. Appl Ergon. 2018;67:151–61.
Article
Google Scholar
McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of three-dimensional kinematic gait measurements: a systematic review. Gait Posture. 2009;29(3):360–9.
Article
Google Scholar
Ricci L, Taffoni F, Formica D. On the orientation error of IMU: investigating static and dynamic accuracy targeting human motion. PLoS ONE. 2016;11(9):e0161940.
Article
Google Scholar
Roell M, Roecker K, Gehring D, Mahler H, Gollhofer A. Player monitoring in indoor team sports: concurrent validity of inertial measurement units to quantify average and peak acceleration values. Front Physiol. 2018;9:141.
Article
Google Scholar
Leardini A, Sawacha Z, Paolini G, Ingrosso S, Nativo R, Benedetti MG. A new anatomically based protocol for gait analysis in children. Gait Posture. 2007;26(4):560–71.
Article
Google Scholar
Wu G, Siegler S, Allard P, Kirtley C, Leardini A, Rosenbaum D, et al. ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion-part I: ankle, hip, and spine. J Biomech. 2002;35(4):543–8.
Article
Google Scholar
Moran RW, Schneiders AG, Major KM, Sullivan SJ. How reliable are functional movement screening scores? A systematic review of rater reliability. Br J Sports Med. 2016;50(9):527–36.
Article
Google Scholar
Nae J, Creaby MW, Nilsson G, Crossley KM, Ageberg E. Measurement properties of a test battery to assess postural orientation during functional tasks in patients undergoing anterior cruciate ligament injury rehabilitation. J Orthopaedic Sports Phys Ther. 2017;47(11):863–73.
Google Scholar