Hand Gesture Recognition on a Resource-Limited Interactive Wristband

Abstract

Most of the reported hand gesture recognition algorithms require high computational resources, i.e., fast MCU frequency and significant memory, which are highly inapplicable to the cost-effectiveness of consumer electronics products. This paper proposes a hand gesture recognition algorithm running on an interactive wristband, with computational resource requirements as low as Flash < 5 KB, RAM < 1 KB. Firstly, we calculated the three-axis linear acceleration by fusing accelerometer and gyroscope data with a complementary filter. Then, by recording the order of acceleration vectors crossing axes in the world coordinate frame, we defined a new feature code named axis-crossing code. Finally, we set templates for eight hand gestures to recognize new samples. We compared this algorithm’s performance with the widely used dynamic time warping (DTW) algorithm and recurrent neural network (BiLSTM and GRU). The results show that the accuracies of the proposed algorithm and RNNs are higher than DTW and that the time cost of the proposed algorithm is much less than those of DTW and RNNs. The average recognition accuracy is 99.8% on the collected dataset and 97.1% in the actual user-independent case. In general, the proposed algorithm is suitable and competitive in consumer electronics. This work has been volume-produced and patent-granted.

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree

“…As a common data type, time series is a sequence of discrete data obtained from a target with a fixed frequency in a period. A fundamental task regarding the time series is to measure the similarity between two given ones, which is critical to downstream works in terms of classification [ 1 , 2 , 3 , 4 , 5 ], clustering [ 6 , 7 , 8 , 9 , 10 ] and pattern recognition [ 11 , 12 , 13 , 14 ]. The dynamic time warping (DTW) [ 15 ] algorithm and its variants [ 16 , 17 , 18 ] are competent in similarity evaluation [ 19 ].…”