**Preface to "Sensor Systems for Gesture Recognition II"**

Gesture recognition (GR) aims to interpret human gestures with impacts in a number of different application fields.

This Special Issue is devoted to describing and examining up-to-date technologies to measure gestures, algorithms for interpreting data, and applications related to GR.

The technologies involve camera-based systems (e.g. an optical motion capture system), and wearable sensors (e.g. an accelerometer, gyroscope, inertial measurement unit (IMU), magnetic inertial measurement unit (MIMU), electromyography (EMG), surface electromyography (sEMG), force myography (FMG), and data/sensory glove).

Data interpretations are detailed here by means of certain metrics (e.g. Euclidean distance) or of a number of classifiers (e.g. artificial neural network (ANN), grasshopper extreme learner (KTGEL), reinforcement learning (RL), deep Q-network (DQN), Random Forest (RF)).

The adopted applications are for medical purposes (e.g. rehabilitation training, control of electric prostheses, gait behavior recognition, cerebral palsy evaluation, and performance in surgical skill assessment), for social matters (e.g. emotion recognition and judgment, hand signs, sign language recognition, and activity recognition), for sports activity analysis (e.g. football kicks), for machine interaction (e.g. human–computer interaction and visual object tracking), and for animal-related application (e.g. detecting fatigue).

This Special Issue is addressed to all the researchers, professionals, and designers interested in GR and to all the users driven by curiosity and passion.

The Guest Editor expresses acknowledgment and thanks to all the involved authors.

**Giovanni Saggio** *Editor*
