Wearable systems for shoulder kinematics assessment: a systematic review

Arianna Carnevale, Umile Giuseppe Longo, Emiliano Schena, Carlo Massaroni, Daniela Lo Presti, Alessandra Berton, Vincenzo Candela, Vincenzo Denaro, Arianna Carnevale, Umile Giuseppe Longo, Emiliano Schena, Carlo Massaroni, Daniela Lo Presti, Alessandra Berton, Vincenzo Candela, Vincenzo Denaro

Abstract

Background: Wearable sensors are acquiring more and more influence in diagnostic and rehabilitation field to assess motor abilities of people with neurological or musculoskeletal impairments. The aim of this systematic literature review is to analyze the wearable systems for monitoring shoulder kinematics and their applicability in clinical settings and rehabilitation.

Methods: A comprehensive search of PubMed, Medline, Google Scholar and IEEE Xplore was performed and results were included up to July 2019. All studies concerning wearable sensors to assess shoulder kinematics were retrieved.

Results: Seventy-three studies were included because they have fulfilled the inclusion criteria. The results showed that magneto and/or inertial sensors are the most used. Wearable sensors measuring upper limb and/or shoulder kinematics have been proposed to be applied in patients with different pathological conditions such as stroke, multiple sclerosis, osteoarthritis, rotator cuff tear. Sensors placement and method of attachment were broadly heterogeneous among the examined studies.

Conclusions: Wearable systems are a promising solution to provide quantitative and meaningful clinical information about progress in a rehabilitation pathway and to extrapolate meaningful parameters in the diagnosis of shoulder pathologies. There is a strong need for development of this novel technologies which undeniably serves in shoulder evaluation and therapy.

Keywords: Inertial sensors; Shoulder kinematics; Smart textile; Upper limb; Wearable system.

Conflict of interest statement

UGL and AB are members of the Editorial Board of BMC Musculoskeletal Disorders. The remaining authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
PRISMA 2009 flow diagram
Fig. 2
Fig. 2
Placement of sensing units (NOTE One study [90] is not included because the specific position of each sensor nodes is not so clear. Legend: N = number of studies, U = Unilateral, B = Bilateral)
Fig. 3
Fig. 3
a Anatomy of the Upper limb; b Anatomy of the Shoulder complex

References

    1. Cutti AG, Veeger HE. Shoulder biomechanics: today’s consensus and tomorrow’s perspectives. Med Biol Eng Comput. 2009;47(5):463–466. doi: 10.1007/s11517-009-0487-3.
    1. Longo UG, Vasta S, Maffulli N, Denaro V. Scoring systems for the functional assessment of patients with rotator cuff pathology. Sports Med Arthrosc Rev. 2011;19(3):310–320. doi: 10.1097/JSA.0b013e31820af9b6.
    1. Longo UG, Berton A, Ahrens PM, Maffulli N, Denaro V. Clinical tests for the diagnosis of rotator cuff disease. Sports Med Arthrosc Rev. 2011;19(3):266–278. doi: 10.1097/JSA.0b013e3182250c8b.
    1. Longo UG, Saris D, Poolman RW, Berton A, Denaro V. Instruments to assess patients with rotator cuff pathology: a systematic review of measurement properties. Knee Surg Sports Traumatol Arthrosc. 2012;20(10):1961–1970. doi: 10.1007/s00167-011-1827-z.
    1. Duc C, Farron A, Pichonnaz C, Jolles BM, Bassin JP, Aminian K. Distribution of arm velocity and frequency of arm usage during daily activity: objective outcome evaluation after shoulder surgery. Gait Posture. 2013;38(2):247–252. doi: 10.1016/j.gaitpost.2012.11.021.
    1. Langohr GDG, Haverstock JP, Johnson JA, Athwal GS. Comparing daily shoulder motion and frequency after anatomic and reverse shoulder arthroplasty. J Shoulder Elb Surg. 2018;27(2):325–332. doi: 10.1016/j.jse.2017.09.023.
    1. Repnik E, Puh U, Goljar N, Munih M, Mihelj M. Using Inertial Measurement Units and Electromyography to Quantify Movement during Action Research Arm Test Execution. Sensors (Basel) 2018;18:9. doi: 10.3390/s18092767.
    1. Bartalesi R, Lorussi F, Tesconi M, Tognetti A, Zupone G, Rossi DD: Wearable kinesthetic system for capturing and classifying upper limb gesture. In: First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems World Haptics Conference: 18–20 March 2005 2005; 2005: 535–536.
    1. Massaroni C, Di Tocco J, Presti DL, Schena E, Bressi F, Bravi M, Miccinilli S, Sterzi S, Longo UG, Berton A: Influence of motion artifacts on a smart garment for monitoring respiratory rate. In: 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA): 2019: IEEE; 2019: 1–6.
    1. Presti DL, Massaroni C, Di Tocco J, Schena E, Formica D, Caponero MA, Longo UG, Carnevale A, D’Abbraccio J, Massari L: Cardiac monitoring with a smart textile based on polymer-encapsulated FBG: influence of sensor positioning. In: 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA): 2019: IEEE; 2019: 1–6.
    1. Esfahani MIM, Nussbaum MA. A “smart” undershirt for tracking upper body motions: task classification and angle estimation. IEEE Sensors J. 2018;18(18):7650–7658. doi: 10.1109/JSEN.2018.2859626.
    1. Jordan K, Haywood KL, Dziedzic K, Garratt AM, Jones PW, Ong BN, Dawes PT. Assessment of the 3-dimensional Fastrak measurement system in measuring range of motion in ankylosing spondylitis. J Rheumatol. 2004;31(11):2207–2215.
    1. Illyés A, Kiss RM. Method for determining the spatial position of the shoulder with ultrasound-based motion analyzer. J Electromyogr Kinesiol. 2006;16(1):79–88. doi: 10.1016/j.jelekin.2005.06.007.
    1. Kuster RP, Heinlein B, Bauer CM, Graf ES. Accuracy of KinectOne to quantify kinematics of the upper body. Gait Posture. 2016;47:80–85. doi: 10.1016/j.gaitpost.2016.04.004.
    1. Pérez R, Costa Ú, Torrent M, Solana J, Opisso E, Cáceres C, Tormos JM, Medina J, Gómez EJ. Upper limb portable motion analysis system based on inertial technology for neurorehabilitation purposes. Sensors (Basel) 2010;10(12):10733–10751. doi: 10.3390/s101210733.
    1. Lambrecht JM, Kirsch RF. Miniature low-power inertial sensors: promising technology for implantable motion capture systems. IEEE Trans Neural Syst Rehabil Eng. 2014;22(6):1138–1147. doi: 10.1109/TNSRE.2014.2324825.
    1. 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):1073–1080. doi: 10.1080/02640414.2015.1088659.
    1. Presti DL, Massaroni C, Formica D, Saccomandi P, Giurazza F, Caponero MA, Schena E. Smart textile based on 12 fiber Bragg gratings array for vital signs monitoring. IEEE Sensors J. 2017;17(18):6037–6043. doi: 10.1109/JSEN.2017.2731788.
    1. Massaroni C, Carraro E, Vianello A, Miccinilli S, Morrone M, Levai IK, Schena E, Saccomandi P, Sterzi S, Dickinson JW, et al. Optoelectronic Plethysmography in clinical practice and research: a review. Respiration. 2017;93(5):339–354. doi: 10.1159/000462916.
    1. Massaroni C, Venanzi C, Silvatti AP, Lo Presti D, Saccomandi P, Formica D, Giurazza F, Caponero MA, Schena E. Smart textile for respiratory monitoring and thoraco-abdominal motion pattern evaluation. J Biophotonics. 2018;11(5):e201700263. doi: 10.1002/jbio.201700263.
    1. de Lucena DS, Stoller O, Rowe JB, Chan V, Reinkensmeyer DJ. Wearable sensing for rehabilitation after stroke: bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery. IEEE Int Conf Rehabil Robot. 2017;2017:1603–1608.
    1. Patel S, Park H, Bonato P, Chan L, Rodgers M. A review of wearable sensors and systems with application in rehabilitation. Journal of neuroengineering and rehabilitation. 2012;9:21. doi: 10.1186/1743-0003-9-21.
    1. Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel) 2014;14(2):3362–3394. doi: 10.3390/s140203362.
    1. Bergmann JH, Chandaria V, McGregor A. Wearable and implantable sensors: the patient's perspective. Sensors (Basel) 2012;12(12):16695–16709. doi: 10.3390/s121216695.
    1. Caldani L, Pacelli M, Farina D, Paradiso R. E-textile platforms for rehabilitation. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:5181–5184.
    1. Wang Q, De Baets L, Timmermans A, Chen W, Giacolini L, Matheve T, Markopoulos P. Motor Control Training for the Shoulder with Smart Garments. Sensors (Basel) 2017;17:7.
    1. Burns DM, Leung N, Hardisty M, Whyne CM, Henry P, McLachlin S. Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch. Physiol Meas. 2018;39(7):075007. doi: 10.1088/1361-6579/aacfd9.
    1. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gøtzsche PC, Ioannidis JP, Clarke M, Devereaux PJ, Kleijnen J, Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62(10):e1–34. doi: 10.1016/j.jclinepi.2009.06.006.
    1. Coley B, Jolles BM, Farron A, Bourgeois A, Nussbaumer F, Pichonnaz C, Aminian K. Outcome evaluation in shoulder surgery using 3D kinematics sensors. Gait Posture. 2007;25(4):523–532. doi: 10.1016/j.gaitpost.2006.06.016.
    1. Coley B, Jolles BM, Farron A, Pichonnaz C, Bassin JP, Aminian K. Estimating dominant upper-limb segments during daily activity. Gait Posture. 2008;27(3):368–375. doi: 10.1016/j.gaitpost.2007.05.005.
    1. Jolles BM, Duc C, Coley B, Aminian K, Pichonnaz C, Bassin JP, Farron A. Objective evaluation of shoulder function using body-fixed sensors: a new way to detect early treatment failures? J Shoulder Elb Surg. 2011;20(7):1074–1081. doi: 10.1016/j.jse.2011.05.026.
    1. Körver RJ, Senden R, Heyligers IC, Grimm B. Objective outcome evaluation using inertial sensors in subacromial impingement syndrome: a five-year follow-up study. Physiol Meas. 2014;35(4):677–686. doi: 10.1088/0967-3334/35/4/677.
    1. van den Noort JC, Wiertsema SH, Hekman KMC, Schönhuth CP, Dekker J, Harlaar J. Reliability and precision of 3D wireless measurement of scapular kinematics. Med Biol Eng Comput. 2014;52(11):921–931. doi: 10.1007/s11517-014-1186-2.
    1. Pichonnaz C, Duc C, Jolles BM, Aminian K, Bassin JP, Farron A. Alteration and recovery of arm usage in daily activities after rotator cuff surgery. J Shoulder Elb Surg. 2015;24(9):1346–1352. doi: 10.1016/j.jse.2015.01.017.
    1. Roldán-Jiménez C, Cuesta-Vargas AI. Studying upper-limb kinematics using inertial sensors: a cross-sectional study. BMC Res Notes. 2015;8:532. doi: 10.1186/s13104-015-1517-x.
    1. van den Noort JC, Wiertsema SH, Hekman KM, Schönhuth CP, Dekker J, Harlaar J. Measurement of scapular dyskinesis using wireless inertial and magnetic sensors: importance of scapula calibration. J Biomech. 2015;48(12):3460–3468. doi: 10.1016/j.jbiomech.2015.05.036.
    1. Cutti AG, Giovanardi A, Rocchi L, Davalli A, Sacchetti R. Ambulatory measurement of shoulder and elbow kinematics through inertial and magnetic sensors. Med Biol Eng Comput. 2008;46(2):169–178. doi: 10.1007/s11517-007-0296-5.
    1. Roldán-Jiménez C, Cuesta-Vargas AI. Age-related changes analyzing shoulder kinematics by means of inertial sensors. Clin Biomech (Bristol, Avon) 2016;37:70–76. doi: 10.1016/j.clinbiomech.2016.06.004.
    1. Aslani N, Noroozi S, Davenport P, Hartley R, Dupac M, Sewell P. Development of a 3D workspace shoulder assessment tool incorporating electromyography and an inertial measurement unit-a preliminary study. Med Biol Eng Comput. 2018;56(6):1003–1011. doi: 10.1007/s11517-017-1745-4.
    1. Carbonaro N, Lucchesi I, Lorusssi F, Tognetti A: Tele-monitoring and tele-rehabilitation of the shoulder muscular-skeletal diseases through wearable systems. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 18–21 July 2018 2018; 2018: 4410–4413.
    1. Hurd WJ, Morrow MM, Miller EJ, Adams RA, Sperling JW, Kaufman KR. Patient-reported and objectively measured function before and after reverse shoulder Arthroplasty. J Geriatr Phys Ther. 2018;41(3):126–133. doi: 10.1519/JPT.0000000000000112.
    1. Hester T, Hughes R, Sherrill DM, Knorr B, Akay M, Stein J, Bonato P: Using wearable sensors to measure motor abilities following stroke. In: International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06): 3–5 April 2006 2006; 2006: 4 pp.-8.
    1. Zhou H, Hu H, Harris ND, Hammerton J. Applications of wearable inertial sensors in estimation of upper limb movements. Biomedical Signal Processing and Control. 2006;1(1):22–32. doi: 10.1016/j.bspc.2006.03.001.
    1. Willmann RD, Lanfermann G, Saini P, Timmermans A, te Vrugt J, Winter S. Home stroke rehabilitation for the upper limbs. Conf Proc IEEE Eng Med Biol Soc. 2007;2007:4015–4018.
    1. Zhou H, Stone T, Hu H, Harris N. Use of multiple wearable inertial sensors in upper limb motion tracking. Med Eng Phys. 2008;30(1):123–133. doi: 10.1016/j.medengphy.2006.11.010.
    1. Giorgino T, Tormene P, Lorussi F, Rossi DD, Quaglini S. Sensor evaluation for wearable strain gauges in neurological rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2009;17(4):409–415. doi: 10.1109/TNSRE.2009.2019584.
    1. Lee GX, Low KS, Taher T. Unrestrained measurement of arm motion based on a wearable wireless sensor network. IEEE Trans Instrum Meas. 2010;59(5):1309–1317. doi: 10.1109/TIM.2010.2043974.
    1. Chee Kian L, Chen I, Zhiqiang L, Yeo SH: A low cost wearable wireless sensing system for upper limb home rehabilitation. In: 2010 IEEE Conference on Robotics, Automation and Mechatronics: 28–30 June 2010 2010; 2010: 1–8.
    1. Patel S, Hughes R, Hester T, Stein J, Akay M, Dy JG, Bonato P. A novel approach to monitor rehabilitation outcomes in stroke survivors using wearable technology. Proc IEEE. 2010;98(3):450–461. doi: 10.1109/JPROC.2009.2038727.
    1. Bento VF, Cruz VT, Ribeiro DD, Cunha JPS: Towards a movement quantification system capable of automatic evaluation of upper limb motor function after neurological injury. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society: 30 Aug.-3 Sept. 2011 2011; 2011: 5456–5460.
    1. Nguyen KD, Chen I, Luo Z, Yeo SH, Duh HB. A wearable sensing system for tracking and monitoring of functional arm movement. IEEE/ASME Transactions on Mechatronics. 2011;16(2):213–220. doi: 10.1109/TMECH.2009.2039222.
    1. Ding ZQ, Luo ZQ, Causo A, Chen IM, Yue KX, Yeo SH, Ling KV. Inertia sensor-based guidance system for upperlimb posture correction. Med Eng Phys. 2013;35(2):269–276. doi: 10.1016/j.medengphy.2011.09.002.
    1. Lee WW, Yen SC, Tay A, Zhao Z, Xu TM, Ling KK, Ng YS, Chew E, Cheong AL, Huat GK. A smartphone-centric system for the range of motion assessment in stroke patients. IEEE J Biomed Health Inform. 2014;18(6):1839–1847. doi: 10.1109/JBHI.2014.2301449.
    1. Bai L, Pepper MG, Yan Y, Spurgeon SK, Sakel M, Phillips M. Quantitative assessment of upper limb motion in neurorehabilitation utilizing inertial sensors. IEEE Trans Neural Syst Rehabil Eng. 2015;23(2):232–243. doi: 10.1109/TNSRE.2014.2369740.
    1. Ertzgaard P, Öhberg F, Gerdle B, Grip H. A new way of assessing arm function in activity using kinematic exposure variation analysis and portable inertial sensors--a validity study. Man Ther. 2016;21:241–249. doi: 10.1016/j.math.2015.09.004.
    1. Lorussi F, Carbonaro N, De Rossi D, Tognetti A. A bi-articular model for scapular-humeral rhythm reconstruction through data from wearable sensors. J Neuroeng Rehabil. 2016;13:40. doi: 10.1186/s12984-016-0149-2.
    1. Mazomenos EB, Biswas D, Cranny A, Rajan A, Maharatna K, Achner J, Klemke J, Jöbges M, Ortmann S, Langendörfer P. Detecting elementary arm movements by tracking upper limb joint angles with MARG sensors. IEEE Journal of Biomedical and Health Informatics. 2016;20(4):1088–1099. doi: 10.1109/JBHI.2015.2431472.
    1. Jiang Y, Qin Y, Kim I, Wang Y: Towards an IoT-based upper limb rehabilitation assessment system. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 11–15 July 2017 2017; 2017: 2414–2417.
    1. Li Y, Zhang X, Gong Y, Cheng Y, Gao X, Chen X. Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors. Sensors (Basel) 2017;17:3. doi: 10.3390/s18010003.
    1. Newman CJ, Bruchez R, Roches S, Jequier Gygax M, Duc C, Dadashi F, Massé F, Aminian K. Measuring upper limb function in children with hemiparesis with 3D inertial sensors. Childs Nerv Syst. 2017;33(12):2159–2168. doi: 10.1007/s00381-017-3580-1.
    1. Yang X, Tan J: Tracking of Human Joints Using Twist and Exponential Map. In: 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER): 31 July-4 Aug. 2017 2017; 2017: 592–597.
    1. Daunoraviciene K, Ziziene J, Griskevicius J, Pauk J, Ovcinikova A, Kizlaitiene R, Kaubrys G. Quantitative assessment of upper extremities motor function in multiple sclerosis. Technol Health Care. 2018;26(S2):647–653. doi: 10.3233/THC-182511.
    1. Jung H, Park J, Jeong J, Ryu T, Kim Y, Lee SI: A wearable monitoring system for at-home stroke rehabilitation exercises: A preliminary study. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI): 4–7 March 2018 2018; 2018: 13–16.
    1. Lin LF, Lin YJ, Lin ZH, Chuang LY, Hsu WC, Lin YH. Feasibility and efficacy of wearable devices for upper limb rehabilitation in patients with chronic stroke: a randomized controlled pilot study. Eur J Phys Rehabil Med. 2018;54(3):388–396.
    1. Parel I, Cutti AG, Fiumana G, Porcellini G, Verni G, Accardo AP. Ambulatory measurement of the scapulohumeral rhythm: intra- and inter-operator agreement of a protocol based on inertial and magnetic sensors. Gait Posture. 2012;35(4):636–640. doi: 10.1016/j.gaitpost.2011.12.015.
    1. Daponte P, Vito LD, Sementa C: A wireless-based home rehabilitation system for monitoring 3D movements. In: 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA): 4–5 May 2013 2013; 2013: 282–287.
    1. Daponte P, Vito LD, Sementa C: Validation of a home rehabilitation system for range of motion measurements of limb functions. In: 2013 IEEE International Symposium on Medical Measurements and Applications (MeMeA): 4–5 May 2013 2013; 2013: 288–293.
    1. Pan J-I, Chung H-W, Huang J-J. Intelligent shoulder joint home-based self-rehabilitation monitoring system. Int J Smart Home. 2013;7(5):395–404. doi: 10.14257/ijsh.2013.7.5.38.
    1. Thiemjarus S, Marukatat S, Poomchoompol P. A method for shoulder range-of-motion estimation using a single wireless sensor node. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:5907–5910.
    1. Rawashdeh SA, Rafeldt DA, Uhl TL, Lumpp JE: Wearable motion capture unit for shoulder injury prevention. In: 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN): 9–12 June 2015 2015; 2015: 1–6.
    1. Álvarez D, Alvarez JC, González RC, López AM. Upper limb joint angle measurement in occupational health. Comput Methods Biomech Biomed Engin. 2016;19(2):159–170. doi: 10.1080/10255842.2014.997718.
    1. Lee H, Cho J, Kim J: Printable skin adhesive stretch sensor for measuring multi-axis human joint angles. In: 2016 IEEE International Conference on Robotics and Automation (ICRA): 16–21 May 2016 2016; 2016: 4975–4980.
    1. Tran TM, Vejarano G: Prediction of received signal strength from human joint angles in body area networks. In: 2016 International Conference on Computing, Networking and Communications (ICNC): 15–18 Feb. 2016 2016; 2016: 1–6.
    1. Rawashdeh SA, Rafeldt DA, Uhl TL. Wearable IMU for Shoulder Injury Prevention in Overhead Sports. Sensors (Basel) 2016;16:11. doi: 10.3390/s16111847.
    1. Wu Y, Chen K, Fu C. Natural gesture modeling and recognition approach based on joint movements and arm orientations. IEEE Sensors J. 2016;16(21):7753–7761. doi: 10.1109/JSEN.2016.2599019.
    1. Ramkumar PN, Haeberle HS, Navarro SM, Sultan AA, Mont MA, Ricchetti ET, Schickendantz MS, Iannotti JP. Mobile technology and telemedicine for shoulder range of motion: validation of a motion-based machine-learning software development kit. J Shoulder Elb Surg. 2018;27(7):1198–1204. doi: 10.1016/j.jse.2018.01.013.
    1. Jung Y, Kang D, Kim J: Upper body motion tracking with inertial sensors. In: 2010 IEEE International Conference on Robotics and Biomimetics: 14–18 Dec. 2010 2010; 2010: 1746-1751.
    1. El-Gohary M, Holmstrom L, Huisinga J, King E, McNames J, Horak F. Upper limb joint angle tracking with inertial sensors. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:5629–5632.
    1. Zhang Z, Wong W, Wu J. Ubiquitous human upper-limb motion estimation using wearable sensors. IEEE Trans Inf Technol Biomed. 2011;15(4):513–521. doi: 10.1109/TITB.2011.2159122.
    1. El-Gohary M, McNames J. Shoulder and elbow joint angle tracking with inertial sensors. IEEE Trans Biomed Eng. 2012;59(9):2635–2641. doi: 10.1109/TBME.2012.2208750.
    1. Lee GX, Low K. A factorized quaternion approach to determine the arm motions using Triaxial accelerometers with anatomical and sensor constraints. IEEE Trans Instrum Meas. 2012;61(6):1793–1802. doi: 10.1109/TIM.2011.2181884.
    1. Hsu Y, Wang J, Lin Y, Chen S, Tsai Y, Chu C, Chang C: A wearable inertial-sensing-based body sensor network for shoulder range of motion assessment. In: 2013 1st International Conference on Orange Technologies (ICOT): 12–16 March 2013 2013; 2013: 328–331.
    1. Ricci L, Formica D, Sparaci L, Lasorsa FR, Taffoni F, Tamilia E, Guglielmelli E. A new calibration methodology for thorax and upper limbs motion capture in children using magneto and inertial sensors. Sensors (Basel) 2014;14(1):1057–1072. doi: 10.3390/s140101057.
    1. Roldan-Jimenez C, Cuesta-Vargas A, Bennett P. Studying upper-limb kinematics using inertial sensors embedded in Mobile phones. JMIR Rehabil Assist Technol. 2015;2(1):e4. doi: 10.2196/rehab.4101.
    1. Meng D, Shoepe T, Vejarano G. Accuracy improvement on the measurement of human-joint angles. IEEE J Biomed Health Inform. 2016;20(2):498–507. doi: 10.1109/JBHI.2015.2394467.
    1. Crabolu M, Pani D, Raffo L, Conti M, Crivelli P, Cereatti A. In vivo estimation of the shoulder joint center of rotation using magneto-inertial sensors: MRI-based accuracy and repeatability assessment. Biomed Eng Online. 2017;16(1):34. doi: 10.1186/s12938-017-0324-0.
    1. Kim HJ, Lee YS, Kim D: Arm Motion Estimation Algorithm Using MYO Armband. In: 2017 First IEEE International Conference on Robotic Computing (IRC): 10–12 April 2017 2017; 2017: 376–381.
    1. Morrow MMB, Lowndes B, Fortune E, Kaufman KR, Hallbeck MS. Validation of inertial measurement units for upper body kinematics. J Appl Biomech. 2017;33(3):227–232. doi: 10.1123/jab.2016-0120.
    1. Rose M, Curtze C, O'Sullivan J, El-Gohary M, Crawford D, Friess D, Brady JM. Wearable inertial sensors allow for quantitative assessment of shoulder and elbow kinematics in a cadaveric knee arthroscopy model. Arthroscopy. 2017;33(12):2110–2116. doi: 10.1016/j.arthro.2017.06.042.
    1. Tian Y, Li Y, Zhu L, Tan J: Inertial-based real-time human upper limb tracking using twists and exponential maps in free-living environments. In: 2017 2nd International Conference on Advanced Robotics and Mechatronics (ICARM): 27–31 Aug. 2017 2017; 2017: 552–557.
    1. Pathirana PN, Karunarathne MS, Williams GL, Nam PT, Durrant-Whyte H. Robust and accurate capture of human joint pose using an inertial sensor. IEEE Journal of Translational Engineering in Health and Medicine. 2018;6:1–11. doi: 10.1109/JTEHM.2018.2877980.
    1. Filippeschi A, Schmitz N, Miezal M, Bleser G, Ruffaldi E, Stricker D. Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion. Sensors (Basel) 2017;17:6. doi: 10.3390/s17061257.
    1. Madgwick SO, Harrison AJ, Vaidyanathan A. Estimation of IMU and MARG orientation using a gradient descent algorithm. IEEE Int Conf Rehabil Robot. 2011;2011:5975346.
    1. Atalay O, Kennon W. Knitted strain sensors: impact of design parameters on sensing properties. Sensors. 2014;14(3):4712–4730. doi: 10.3390/s140304712.
    1. Holm R: Electric contacts: theory and application: Springer Science & Business Media; 2013.
    1. Yang J, Feng X, Kim JH, Rajulu S. Review of biomechanical models for human shoulder complex. International Journal of Human Factors Modelling and Simulation. 2010;1(3):271–293. doi: 10.1504/IJHFMS.2010.036791.
    1. Liu Y, Zhang Y, Zeng M: Joint parameter estimation using Magneto and Inertial measurement units. In: 2017 36th Chinese Control Conference (CCC): 26–28 July 2017 2017; 2017: 2225–2230.
    1. Chen X, Zhang J, Hamel WR, Tan J: An inertial-based human motion tracking system with twists and exponential maps. In: 2014 IEEE International Conference on Robotics and Automation (ICRA): 31 May-7 June 2014 2014; 2014: 5665–5670.
    1. Sugamoto K, Harada T, Machida A, Inui H, Miyamoto T, Takeuchi E, Yoshikawa H, Ochi T. Scapulohumeral rhythm: relationship between motion velocity and rhythm. Clin Orthop Relat Res. 2002;401:119–124. doi: 10.1097/00003086-200208000-00014.
    1. Struyf F, Nijs J, Baeyens JP, Mottram S, Meeusen R. Scapular positioning and movement in unimpaired shoulders, shoulder impingement syndrome, and glenohumeral instability. Scand J Med Sci Sports. 2011;21(3):352–358. doi: 10.1111/j.1600-0838.2010.01274.x.
    1. Beckmann JS, Lew D. Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities. Genome Med. 2016;8(1):134. doi: 10.1186/s13073-016-0388-7.
    1. Sikka RS, Baer M, Raja A, Stuart M, Tompkins M. Analytics in sports medicine: implications and responsibilities that accompany the era of big data. J Bone Joint Surg Am. 2019;101(3):276–283. doi: 10.2106/JBJS.17.01601.

Source: PubMed

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