Literature DB >> 25571210

Application of wireless inertial measurement units and EMG sensors for studying deglutition - Preliminary results.

U Imtiaz, K Yamamura, W Kong, S Sessa, Z Lin, L Bartolomeo, H Ishii, M Zecca, Y Yamada, A Takanishi.   

Abstract

Different types of sensors are being used to study deglutition and mastication. These often suffer from problems related to portability, cost, reliability, comfort etc. that make it difficult to use for long term studies. An inertial measurement based sensor seems a good fit in this application; however its use has not been explored much for the specific application of deglutition research. In this paper, we present a system comprised of an IMU and EMG sensor that are integrated together as a single system. With a preliminary experiment, we determine that the system can be used for measuring the head-neck posture during swallowing in addition to other parameters during the swallowing phase. The EMG sensor may not always be a reliable source of physiological data especially for small clustered muscles like the ones responsible for swallowing. In this case, we explore the possibility of using gyroscopic data for the recognition of deglutition events.

Mesh:

Year:  2014        PMID: 25571210     DOI: 10.1109/EMBC.2014.6944842

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  A Supporting Platform for Semi-Automatic Hyoid Bone Tracking and Parameter Extraction from Videofluoroscopic Images for the Diagnosis of Dysphagia Patients.

Authors:  Jun Chang Lee; Kyoung Won Nam; Dong Pyo Jang; Nam Jong Paik; Ju Seok Ryu; In Young Kim
Journal:  Dysphagia       Date:  2016-11-17       Impact factor: 3.438

2.  Analyzing Breathing Signals and Swallow Sequence Locality for Solid Food Intake Monitoring.

Authors:  Bo Dong; Subir Biswas
Journal:  J Med Biol Eng       Date:  2016-12-09       Impact factor: 1.553

3.  Automatic Detection of the Pharyngeal Phase in Raw Videos for the Videofluoroscopic Swallowing Study Using Efficient Data Collection and 3D Convolutional Networks .

Authors:  Jong Taek Lee; Eunhee Park; Tae-Du Jung
Journal:  Sensors (Basel)       Date:  2019-09-07       Impact factor: 3.576

4.  Deep Learning in Gait Parameter Prediction for OA and TKA Patients Wearing IMU Sensors.

Authors:  Mohsen Sharifi Renani; Casey A Myers; Rohola Zandie; Mohammad H Mahoor; Bradley S Davidson; Chadd W Clary
Journal:  Sensors (Basel)       Date:  2020-09-28       Impact factor: 3.576

  4 in total

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