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

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.

Automated summary

This study introduces the smartphone as an alternative for identifying gestures by detecting muscular contractions through a mobile device attached to the forearm. The system demonstrated high accuracy in evaluating hand postures, providing a simple and effective method for gesture recognition.