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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Identification of Hand Gestures Using the Inertial Measurement Unit of a Smartphone: A Proof-of-Concept Study

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Author(s):
Fujiwara, Eric [1] ; Rodrigues, Matheus dos Santos [1] ; Gomes, Matheus Kaue [1] ; Wu, Yu Tzu [1] ; Suzuki, Carlos Kenichi [1]
Total Authors: 5
Affiliation:
[1] Univ Estadual Campinas, Sch Mech Engn, Lab Photon Mat & Devices, BR-13083860 Campinas - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE SENSORS JOURNAL; v. 21, n. 12, p. 13916-13923, JUN 15 2021.
Web of Science Citations: 0
Abstract

Assessing the hand postures is crucial to implement gesture-based user-computer interfaces for controlling robots and assistive devices. Apart from data gloves and optical tracking, techniques such as surface electromyography and force myography provide a straightforward, non-invasive way to estimate poses and intentions through the forearm muscles assessment. However, most of the myography systems rely on bulky, dedicated hardware with arrays of electrodes or force probes. Therefore, this work introduces the smartphone as an alternative for identifying gestures: with the mobile device attached to the forearm, the embedded inertial measurement unit detects muscular contractions produced during the transitions between postures, yielding signatures in acquired waveforms. After computing the correlation of measured and template patterns, a competitive layer votes the class with greater probability and identifies the gesture. Prior characterization studies evaluated the effect of smartphone placement and forearm orientation in the sensor response, revealing that the IMU signatures are repeatable and robust to positioning deviations. Next, using 10-fold cross-validation, the system discerned four gestures (fist, open hand, wave in, and wave out) performed by six volunteers in ten repetitions, providing 96.6% and 94.1% average accuracies for self-calibration and inter-participant analyses, respectively. The smartphone figures as a ubiquitous and straightforward alternative for assessing gestures, with further applications in human-robot interaction and assistive technologies. (AU)

FAPESP's process: 17/25666-2 - Development of an optical fiber force myography sensor for applications in human-robot interfaces
Grantee:Eric Fujiwara
Support type: Regular Research Grants