Jennifer Jiménez
ABSTRACT
In the context of chronic diseases, it is difficult to maintain adherence and motivation in rehabilitation treatment. The inclusion of technological tools that help to obtain objective data that allow the patient to be monitored and provide feedback on the development of exercises is very useful to increase long-term adherence to treatment.
Devices such as depth sensors and smartbands, which collect information on movement angles and heart rate among other parameters, can be introduced into the rehabilitation room without being intrusive.
In the TeNDER project, a tool has been developed that allows the monitoring of activities performed during the execution of regular therapy and the analysis of these data to be presented as feedback via a mobile application to patients and caregivers/family members; and thanks to a web app as evolution data to health professionals.
- Giovanni Abbruzzese, Roberta Marchese, Laura Avanzino, and Elisa Pelosin. 2016. Rehabilitation for Parkinson’s disease: Current outlook and future challenges. Parkinsonism & Related Disorders 22 Suppl 1 (-01 2016), 60.Google Scholar
- Mohd Aliff, Mohamed Alif Dinie, Ismail Yusof, and NS Sani. 2019. Development of Smart Glove Rehabilitation Device (SGRD) for Parkinson’s Disease. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 9, 2 (2019).Google Scholar
- Amin Amini, Konstantinos Banitsas, and William R. Young. 2019. Kinect4FOG: monitoring and improving mobility in people with Parkinson’s using a novel system incorporating the Microsoft Kinect v2. Disability and Rehabilitation: Assistive Technology 14, 6(2019), 566–573. https://doi.org/10.1080/17483107.2018.1467975Google ScholarCross Ref
- Alberto Belmonte-Hernández, Thomas Theodoridis, Marta Burgos González, Gustavo Hernández-Peñaloza, Vassilios Solachidis, Jennifer Jiménez Ramos, Federico Álvarez, Nicholas Vretos, Laura Carrasco, and Petros Daras. 2019. A Novel Framework for Physical Therapy Rehabilitation Monitoring and Assessment in Parkinson Disease Patients Using Depth Information. In Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments (Rhodes, Greece) (PETRA ’19). Association for Computing Machinery, New York, NY, USA, 535–539. https://doi.org/10.1145/3316782.3322759Google ScholarDigital Library
- Igor Bisio, Chiara Garibotto, Fabio Lavagetto, and Andrea Sciarrone. 2019. When eHealth Meets IoT: A Smart Wireless System for Post-Stroke Home Rehabilitation. IEEE Wireless Communications 26, 6 (2019), 24–29. https://doi.org/10.1109/MWC.001.1900125Google ScholarDigital Library
- Robert Brown. 2019. Exploring Speech Recognition And Synthesis APIs In Windows Vista. Technical Report. https://docs.microsoft.com/en-us/archive/msdn-magazine/2006/january/exploring-speech-recognition-and-synthesis-apis-in-windows-vistaGoogle Scholar
- Anargyros Chatzitofis, David Monaghan, Edmond Mitchell, Freddie Honohan, Dimitrios Zarpalas, Noel E. O’Connor, and Petros Daras. 2015. HeartHealth: A Cardiovascular Disease Home-based Rehabilitation System. Procedia Computer Science 63 (2015), 340–347. https://doi.org/10.1016/j.procs.2015.08.352Google ScholarCross Ref
- Anargyros Chatzitofis, Dimitris Zarpalas, and Petros Daras. 2018. A Computerized System for Real-Time Exercise Performance Monitoring and e-Coaching Using Motion Capture Data. In Precision Medicine Powered by pHealth and Connected Health, Nicos Maglaveras, Ioanna Chouvarda, and Paulo de Carvalho (Eds.). Springer Singapore, Singapore, 243–247.Google Scholar
- Stefano Corazza, Lars Mündermann, Emiliano Gambaretto, Giancarlo Ferrigno, and Thomas Andriacchi. 2010. Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation. International Journal of Computer Vision 87 (03 2010), 156–169. https://doi.org/10.1007/s11263-009-0284-3Google ScholarDigital Library
- Augusto Garcia-Agundez, Ann-Kristin Folkerts, Robert Konrad, Polona Caserman, Thomas Tregel, Mareike Goosses, Stefan Göbel, and Elke Kalbe. 2019. Recent advances in rehabilitation for Parkinson’s Disease with Exergames: A Systematic Review. Journal of NeuroEngineering and Rehabilitation 16, 1 (29 January 2019), 17. https://doi.org/10.1186/s12984-019-0492-1Google ScholarCross Ref
- Muhammad Ahmed Khan, Bayram Metin Bayram, Rig Das, and Sadasivan Puthusserypady. 2021. Electromyography and Inertial Motion Sensors Based Wearable Data Acquisition System for Stroke Patients: A Pilot Study. Annu Int Conf IEEE Eng Med Biol Soc 2021 (November 2021), 6953–6956.Google ScholarCross Ref
- Mengxuan Ma, Rachel Proffitt, and Marjorie Skubic. 2018. Validation of a Kinect V2 based rehabilitation game. PLOS ONE 13, 8 (August 2018), 1–15. https://doi.org/10.1371/journal.pone.0202338Google Scholar
- Niveditha Muthukrishnan, James J. Abbas, and Narayanan Krishnamurthi. 2020. A Wearable Sensor System to Measure Step-Based Gait Parameters for Parkinson’s Disease Rehabilitation. Sensors 20, 22 (2020). https://doi.org/10.3390/s20226417Google Scholar
- Mohammad Reza Naeemabadi, Birthe Irene Dinesen, Ole Kæseler Andersen, Samira Najafi, and John Hansen. 2018. Evaluating accuracy and usability of Microsoft Kinect sensors and wearable sensor for tele knee rehabilitation after knee operation. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, Biostec 2018; Biodevices 2018, 19-21 January 2018, Funchal, Madeira, Portugal, Sergi Bermudez i Badia, Alberto Cliquet, Alberto Cliquet, Hugo Gamboa, and Ana Fred (Eds.). Vol. 1. SCITEPRESS Digital Library, 128–135. https://doi.org/10.5220/0006578201280135Google ScholarCross Ref
- Avinash Parnandi, Eric Wade, and Maja Mataric. 2010. Motor function assessment using wearable inertial sensors. Annu Int Conf IEEE Eng Med Biol Soc 2010 (2010), 86–89.Google ScholarCross Ref
- Annette O. A. Plouvier, Tim C. Olde Hartman, Anne van Litsenburg, Bastiaan R. Bloem, Chris van Weel, and Antoine L. M. Lagro-Janssen. 2018. Being in control of Parkinson’s disease: A qualitative study of community-dwelling patients’ coping with changes in care. European Journal of General Practice 24, 1 (2018), 138–145. https://doi.org/10.1080/13814788.2018.1447561 arXiv:https://doi.org/10.1080/13814788.2018.1447561PMID: 29569501.Google ScholarCross Ref
- ICT4Life Project. [n.d.]. CORDIS ICT4Life - ICT services for Life Improvement For the Elderly. https://cordis.europa.eu/project/id/690090/esGoogle Scholar
- TeNDER Project. 2022. H2020 TeNDER Project Website. https://www.tender-health.eu/Google Scholar
- Ashwin Rajkumar, Fabio Vulpi, Satish Reddy Bethi, Hassam Khan Wazir, Preeti Raghavan, and Vikram Kapila. 2019. Wearable Inertial Sensors for Range of Motion Assessment. IEEE Sens J 20, 7 (December 2019), 3777–3787.Google Scholar
- Pablo Cornejo Thumm, Nir Giladi, Jeffrey M. Hausdorff, and Anat Mirelman. 2021. Tele-Rehabilitation with Virtual Reality: A Case Report on the Simultaneous, Remote Training of Two Patients with Parkinson Disease. American Journal of Physical Medicine & Rehabilitation 100, 5(2021), 1–15. https://journals.lww.com/ajpmr/Fulltext/2021/05000/Tele_Rehabilitation_with_Virtual_Reality__A_Case.5.aspxGoogle Scholar
- Peicheng Yang, Lei Xie, Chuyu Wang, and Sanglu Lu. 2019. IMU-Kinect: A Motion Sensor-Based Gait Monitoring System for Intelligent Healthcare. Association for Computing Machinery, New York, NY, USA, 350––353. https://doi.org/10.1145/3341162.3343766Google ScholarDigital Library
Index Terms
- Monitoring of motor function in the rehabilitation room
Index terms have been assigned to the content through auto-classification.
Recommendations
Development of posture adjustment supporting device of the head and neck for swallowing rehabilitation
i-CREATe '13: Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive TechnologyBackground & Aim: Stroke patients often develop dysphagia and sometimes have aspiration pneumonia. To prevent aspiration, dyshagic stroke patients usually undergo a program of comprehensive swallowing rehabilitation, which involves the provision of ...
Evaluating rehabilitation exercise performance using a single inertial measurement unit
PervasiveHealth '13: Proceedings of the 7th International Conference on Pervasive Computing Technologies for HealthcareInertial measurement units (IMUs) may be used during exercise to provide feedback on exercise technique. However the number of IMUs that are required to deliver effective feedback during lower limb exercises has not yet been established. This ...
Lower Limb Exoskeleton Using Recumbent Cycling Modality for Post-stroke Rehabilitation
ICIRA 2013: Proceedings of the 6th International Conference on Intelligent Robotics and Applications - Volume 8102This paper presents a compact exoskeleton robot for post-stroke patients at the early stage of lower limb rehabilitation as soon as they become stable. Unlike the usual walking gait, a new training modality mimicking the popular exercise named recumbent ...
Comments