CONTENTS
12.1 Introduction ................................................................................................ 220
12.2 System Configuration ................................................................................222
12.2.1 Multichannel sEMG Sensor Ring ................................................222
12.2.2 sEMG Signal Preprocessing .........................................................223
12.3 Classification of Hand Movements .........................................................223
12.3.1 Automatic Relocation of sEMG Electrodes ................................223
12.3.2 Feature Extraction from Multiple Channels .............................. 226
12.4 Identification of Movement Force and Speed ........................................ 230
12.4.1 STFT Method .................................................................................. 231
12.4.2 Features Based on STFT ................................................................ 231
12.4.3 Experimental Results ..................................................................... 232
12.5 Summary ..................................................................................................... 233
References ............................................................................................................. 237
12
Classification of Hand Motion
Using Surface EMG Signals
Xueyan Tang, Yunhui Liu, Congyi Lu, and Weilun Poon
Chinese University of Hong Kong
Hong Kong, China
Abstract
The human hand has multiple degrees of freedom (DOFs) to achieve
high dexterity. Identifying the five-finger movements using surface electromyography (sEMG) is challenging. Moreover, the success rate of identifying the hand movements is sensitive to many aspects; for example,
the sEMG electrode placements, variant movement forces, or movement
speeds. In this chapter, a robust sEMG system for identifying the hand
movements is developed. First, a multichannel sEMG sensor ring is
designed, which is easy to wear on the human forearm even without
knowledge of the exact location of the corresponding muscles. However,
a new problem of using the multichannel sensor ring is followed and
unsolved in the current research. The problem is how to relocate the
sEMG electrodes with the same sequence as the last trial. This chapter introduces the concordance correlation coefficient to investigate the
219
12.1 Introduction ................................................................................................ 220
12.2 System Configuration ................................................................................222
12.2.1 Multichannel sEMG Sensor Ring ................................................222
12.2.2 sEMG Signal Preprocessing .........................................................223
12.3 Classification of Hand Movements .........................................................223
12.3.1 Automatic Relocation of sEMG Electrodes ................................223
12.3.2 Feature Extraction from Multiple Channels .............................. 226
12.4 Identification of Movement Force and Speed ........................................ 230
12.4.1 STFT Method .................................................................................. 231
12.4.2 Features Based on STFT ................................................................ 231
12.4.3 Experimental Results ..................................................................... 232
12.5 Summary ..................................................................................................... 233
References ............................................................................................................. 237
12
Classification of Hand Motion
Using Surface EMG Signals
Xueyan Tang, Yunhui Liu, Congyi Lu, and Weilun Poon
Chinese University of Hong Kong
Hong Kong, China
Abstract
The human hand has multiple degrees of freedom (DOFs) to achieve
high dexterity. Identifying the five-finger movements using surface electromyography (sEMG) is challenging. Moreover, the success rate of identifying the hand movements is sensitive to many aspects; for example,
the sEMG electrode placements, variant movement forces, or movement
speeds. In this chapter, a robust sEMG system for identifying the hand
movements is developed. First, a multichannel sEMG sensor ring is
designed, which is easy to wear on the human forearm even without
knowledge of the exact location of the corresponding muscles. However,
a new problem of using the multichannel sensor ring is followed and
unsolved in the current research. The problem is how to relocate the
sEMG electrodes with the same sequence as the last trial. This chapter introduces the concordance correlation coefficient to investigate the
219