Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Subject Identification Across Large Expression Variations Using 3D Facial Landmarks Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

Towards Stroke Patients’ Upper-Limb Automatic Motor Assessment Using Smartwatches

verfasst von : Asma Bensalah, Jialuo Chen, Alicia Fornés, Cristina Carmona-Duarte, Josep Lladós, Miguel Ángel Ferrer

Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Assessing the physical condition in rehabilitation scenarios is a challenging problem, since it involves Human Activity Recognition (HAR) and kinematic analysis methods. In addition, the difficulties increase in unconstrained rehabilitation scenarios, which are much closer to the real use cases. In particular, our aim is to design an upper-limb assessment pipeline for stroke patients using smartwatches. We focus on the HAR task, as it is the first part of the assessing pipeline. Our main target is to automatically detect and recognize four key movements inspired by the Fugl-Meyer assessment scale, which are performed in both constrained and unconstrained scenarios. In addition to the application protocol and dataset, we propose two detection and classification baseline methods. We believe that the proposed framework, dataset and baseline results will serve to foster this research field.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Development of an Augmented Reality System Based on Marker Tracking for Robotic Assisted Minimally Invasive Spine Surgery
Nächstes Kapitel Multimodal Detection of Tonic–Clonic Seizures Based on 3D Acceleration and Heart Rate Data from an In-Ear Sensor
Fußnoten
Literatur
1.
Lee, K.B., et al.: Six-month functional recovery of stroke patients: a multi-time-point study. Int. J. Rehabil. Res. 38(2), 173 (2015)CrossRef
2.
Zunino, A., et al.: Video gesture analysis for autism spectrum disorder detection. In: 24th International Conference on Pattern Recognition (ICPR), pp. 3421–3426. IEEE (2018)
3.
Rueda, F.M., Fink, G.A.: Learning attribute representation for human activity recognition. In 24th International Conference on Pattern Recognition (ICPR), pp. 523–528. IEEE (2018)
4.
5.
Kim, J., Parnell, C., Wichmann, T., DeWeerth, S.P.: Longitudinal wearable tremor measurement system with activity recognition algorithms for upper limb tremor. In: 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 6166–6169 (2016)
6.
Morris, D., Saponas, T.S., Guillory, A., Kelner, I.: Recofit: using a wearable sensor to find, recognize, and count repetitive exercises. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3225–3234 (2014)
7.
Oña, E.D., Jardón, A., Monge, E., Molina, F., Cano, R., Balaguer, C.: Towards automated assessment of upper limbs motor function based on fugl-meyer test and virtual environment. In: Masia, L., Micera, S., Akay, M., Pons, J.L. (eds.) ICNR 2018. BB, vol. 21, pp. 297–301. Springer, Cham (2019). https://​doi.​org/​10.​1007/​978-3-030-01845-0_​60CrossRef
8.
Schindler, R., Bouillon, M., Plamondon, R., Fischer, A.: Extending the sigma-lognormal model of the kinematic theory to three dimensions. In: Proceedings of the International Conference on Pattern Recognition and Artificial Intelligence (2018)
9.
10.
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 1–33 (2013)CrossRef
11.
Clarkson, B.P.: Life patterns: structure from wearable sensors. PhD thesis, Massachusetts Institute of Technology (2002)
12.
13.
He, Z., Jin, L.: Activity recognition from acceleration data based on discrete consine transform and SVM. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 5041–5044. IEEE (2009)
14.
Bhat, G., Deb, R., Chaurasia, V.V., Shill, H., Ogras, U.Y.: Online human activity recognition using low-power wearable devices. In: IEEE/ACM International Conference on Computer-Aided Design (ICCAD), pp. 1–8. IEEE (2018)
15.
Dehghani, A., Sarbishei, O., Glatard, T., Shihab, E.: A quantitative comparison of overlapping and non-overlapping sliding windows for human activity recognition using inertial sensors. Sensors 19(22), 5026 (2019)CrossRef
16.
Zhang, Y., Zhang, Y., Zhang, Z., Bao, J., Song, Y.: Human activity recognition based on time series analysis using U-net. arXiv preprint arXiv:​1809.​08113 (2018)
17.
Rastegari, E., Ali, H.: A bag-of-words feature engineering approach for assessing health conditions using accelerometer data. Smart Health 16, 100116 (2020)CrossRef
18.
Yao, R., Lin, G., Shi, Q., Ranasinghe, D.C.: Efficient dense labeling of human activity sequences from wearables using fully convolutional networks. Pattern Recogn. 78, 252–266 (2017)CrossRef
19.
Ahmed, N., Rafiq, J.I., Islam, M.R.: Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors 20(1), 317 (2020)CrossRef
20.
Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., Luaces, M.R.: A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors 20(8), 2200 (2020)CrossRef
21.
Souza, W., Kavitha, R.: Human activity recognition using accelerometer and gyroscope sensors. Int. J. Eng. Technol. pp. 1171–1179 (2017)
22.
Ayumi, V., Fanany, M.I.: A comparison of SVM and RVM for human action recognition. Internetworking Indonesia J. 8(1), 29–33 (2016)
23.
Zhang, M., Sawchuk, A.A.: Human daily activity recognition with sparse representation using wearable sensors. IEEE J. Biomed. Health Inform. 3, 553–560 (2013)CrossRef
24.
Xu, W., Zhang, M., Sawchuk, A.A., Sarrafzadeh, M.: Robust human activity and sensor location corecognition via sparse signal representation. IEEE Trans. Biomed. Eng. 11, 3169–3176 (2012)
25.
Mehrang, S., Pietilä, J., Korhonen, I.: An activity recognition framework deploying the random forest classifier and a single optical heart rate monitoring and triaxial accelerometer wrist-band. Sensors 18(2), 613 (2018)CrossRef
26.
Xu, L., Yang, W., Cao, Y., Li, Q.: Human activity recognition based on random forests. In: Liu, Y., Zhao, L., Cai, G., Xiao, G., Li, K., Wang, L., eds. 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2017, Guilin, China, July 29–31, 2017, pp. 548–553. IEEE (2017)
27.
Ha, S., Choi, S.: Convolutional neural networks for human activity recognition using multiple accelerometer and gyroscope sensors. In: International Joint Conference on Neural Networks (IJCNN), pp. 381–388 (2016)
28.
Ordóñez, F., Roggen, D.: Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1), 115 (2016)CrossRef
29.
Tian, Y., Zhang, J., Chen, L., Geng, Y., Wang, X.: Selective ensemble based on extreme learning machine for sensor-based human activity recognition. Sensors 19(16), 3468 (2019)CrossRef
30.
Ryanne, J.M., et al.: Recognizing complex upper extremity activities using body worn sensors. PLoS One 10(3), e0118642 (2015)CrossRef
31.
Butt, A.H., et al.: Assessment of purposeful movements for post-stroke patients in activites of daily living with wearable sensor device. In: IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–8 (2019)
32.
Rosati, S., Balestra, G., Knaflitz, M.: Comparison of different sets of features for human activity recognition by wearable sensors. Sensors 18(122), 4189 (2018)CrossRef
33.
Pires, I.M., et al.: Pattern recognition techniques for the identification of activities of daily living using mobile device accelerometer. Electronics 9(3), 509 (2017)CrossRef
34.
Cervantes, J., Garcia-Lamont, F., Rodríguez, L., Lopez, A.: A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408, 189–215 (2020)CrossRef
35.
Banegas-Luna, A.J., et al.: When will the mist clear? on the interpretability of machine learning for medical applications: a survey. arXiv preprint arXiv:​2010.​00353 (2020)
36.
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional network for EEG-based brain-computer interfaces. J. Neural Eng. 15(5), 056013 (2016)CrossRef
Metadaten
Titel
Towards Stroke Patients’ Upper-Limb Automatic Motor Assessment Using Smartwatches
verfasst von
Asma Bensalah
Jialuo Chen
Alicia Fornés
Cristina Carmona-Duarte
Josep Lladós
Miguel Ángel Ferrer
Copyright-Jahr
2021
Buch
Pattern Recognition. ICPR International Workshops and Challenges

Print ISBN: 978-3-030-68762-5

Electronic ISBN: 978-3-030-68763-2

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-68763-2_36

Premium Partner

    Bildnachweise