Motions of the fingers are complex since hand grasping and manipulation are conducted by spatial and temporal coordination of forearm muscles and tendons. The dominant methods based on surface electromyography (sEMG) could not offer satisfactory solutions for finger motion classification due to its inherent nature of measuring the electrical activity of motor units at the skin's surface. In order to recognize morphological changes of forearm muscles for accurate hand motion prediction, ultrasound imaging is employed to investigate the feasibility of detecting mechanical deformation of deep muscle compartments in potential clinical applications. In this study, finger motion classification has been represented as subproblems: recognizing the discrete finger motions and predicting the continuous finger angles. Predefined 14 finger motions are presented in both sEMG signals and ultrasound images and captured simultaneously. Linear discriminant analysis classifier shows the ultrasound has better average accuracy (95.88%) than the sEMG (90.14%). On the other hand, the study of predicting the metacarpophalangeal (MCP) joint angle of each finger in nonperiod movements also confirms that classification method based on ultrasound achieves better results (average correlation 0.89 $\pm$ 0.07 and NRMSE 0.15 $\pm$ 0.05) than sEMG (0.81 $\pm$ 0.09 and 0.19 $\pm$ 0.05). The research outcomes evidently demonstrate that the ultrasound can be a feasible solution for muscle-driven machine interface, such as accurate finger motion control of prostheses and wearable robotic devices.

While myoelectric pattern recognition is a prevailing way for gesture recognition, the inherent nonstationarity of electromyography signals hinders its long-term application. This study aims to prove a hypothesis that morphological information of muscle contraction detected by ultrasound image is potentially suitable for long-term use. A set of ultrasound-based algorithms are proposed to realize robust hand gesture recognition over multiple days, with user training only at the first day. A markerless calibration algorithm is first presented to position the ultrasound probe during donning and doffing; an algorithm combining speeded-up robust features (SURF) and bag-of-features (BoF) model being immune to ultrasound probe shift and rotation is then introduced; a self-enhancing classification method is next adopted to update classification model automatically by incorporating useful knowledge from testing data; finally the performance of long-term hand gesture recognition with zero re-training is validated by a six-day experiment of six healthy subjects, whose outcomes strongly support the hypothesis with about 94% of gesture recognition accuracy for each testing day. This study confirms the feasibility of adoption of ultrasound sensing for long-term musculature related applications.

No abstract

No abstract

Does social media keep me alarmed? The effects of expectations surrounding social media attributes and exposure to messages of social (in)stability on substitutive social media news use Huang, Y.; Boukes, M.