Sensor Systems for Gesture Recognition II

Dear Colleagues,

Gesture recognition (GR) aims to interpret human gestures by means of mathematical algorithms. Its achievement will have widespread applications in a number of different fields, with impacts that can help or meaningfully improve our quality of life.

In the real world, GR can interpret communication meanings at a distance or can “translate” “sign language” into written sentences or as a synthetized voice.  In a virtual reality (VR) and augmented reality (AR) world, GR can allow navigation and interaction, as occurs, for instance, with the user interface (UI) of a smart TV controlled by hand gestures.

The possible applications are countless, and we can mention just a few. In the health field, GR makes it possible to augment the motion capabilities of disabled people or to support surgeons in surgical settings. In gaming, GR frees gamers from input devices such as keyboards, mouses, and joysticks. In the automotive industry, GR allows drivers to control car appliances (see BMW 7 Series). In cinematography, GR is used to computer-generate effects or creatures. In everyday life, GR is a means to interact with smartphone apps (e.g., uSens, Inc.; Gestigon GmbH). In human–robot interactions, GR keeps the operator in safe conditions, while their gestures become the remote commands for tele-operating a robot. GR also enables music creation, converting human movements into sounds.

GR is achieved through (1) data acquisition, (2) pattern identification, and (3) interpretation (each of these phases can consist of different stages).

Data can be acquired by means of sensor systems based on different measurement principles, such as mechanical, magnetic, optic, acoustic, inertial principles, or hybrid sensors. Within this frame, optical technologies are historically the most explored ones (since 1870, when animal movements were analyzed via image sequences) and represent the current state of the art. However, optical technologies are expensive and require a dedicated room and skilled personnel. Therefore, non-optical technologies, in particular those based on wearable sensors, are increasingly gaining importance.

In order to obtain GR, different methods can be adopted for data segmentation, feature extraction, and classification. These methods highly depend on the type of data (according to the adopted type of sensor system) and the type of gestures to be recognized.

The supervised on unsupervised) recognition of patterns in data (i.e., regularities, arrangements, characteristics) can be approached by machine learning or heuristics, and can be linked to artificial intelligence (AI).

In sum, sensor systems for gesture recognition deal with an ensemble of topics that can singularly or jointly be accessed, and that represent a great opportunity for further developments, with widespread potential applications.

This call for papers invites technical contributions to a Sensors Special Issue providing an up-to-date overview on “Sensor Systems for Gesture Recognition”. This Special Issue will deal with theory, solutions, and innovative applications. Potential topics include, but are not limited to:

Sensor systems

Gesture recognition

Gesture recognition technologies

Gesture extraction methods

Gesture detection sensors

 Wearable sensors

 Human tracking

 Human postures and movements

 Motion detection and tracking

 Hand gesture recognition

 Sign language recognition

 Gait analysis

 Remote controlling

 Pattern recognition for gesture recognition

 Machine learning for gesture recognition

 Applications of gesture recognitions

 Algorithms for gesture recognition

Prof. Dr. Giovanni Saggio
Guest Editor

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