MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition

TLDR

The paper proposes three gesture recognition models that identify seven hand gestures (up, down, left, right, tick, circle, cross) from MEMS 3‑axis accelerometer data. The system uses three MEMS accelerometers to capture 3‑axis acceleration, transmits the data via Bluetooth, segments gestures automatically, extracts a sign‑sequence feature reduced to an 8‑number code, and matches this code against stored templates for recognition. Across 72 experiments totaling 628 gestures, the best model achieved 95.6% overall accuracy, with individual gesture accuracies between 91% and 100%, demonstrating effective nonspecific‑user hand‑gesture recognition without prior training.

Abstract

This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.