Joint motion estimation method based on myoelectricity myotone model and unscented particle filtering

Abstract

The invention relates to a joint motion estimation method based on a myoelectricity and myotone model and unscented particle filtering. The method comprises the following steps: firstly, acquiring surface myoelectricity and muscle sound signals of biceps brachii muscle, triceps brachii muscle, radial brachii muscle, trapezius muscle, adductor muscle, anterior deltoid muscle, lateral deltoid muscleand pectoralis major muscle of an upper limb shoulder joint and an elbow joint of a human body in a synchronous continuous motion state, and respectively performing band-pass filtering processing; then, extracting Wilson amplitude and fuzzy entropy features of the surface myoelectricity and myotone signals; combining the physiological muscle model and joint kinematics through parameter substitution and simplification to form a joint motion model, and forming a measurement equation by using the extracted features to serve as feedback of the joint motion model to obtain a myoelectricity myotonestate space model; and finally, estimating the synchronous continuous motion of the shoulder joint and the elbow joint through an unscented particle filter algorithm. Compared with a traditional multi-joint synchronous continuous motion estimation method, the method has the advantage that the prediction precision and the real-time performance are obviously improved.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for pattern recognition of myoelectric and myotone signals, in particular to a method for estimating multi-joint synchronous continuous motion based on a state space model of myoelectricity and myotone and traceless particle filter. Background technique [0002] Surface electromyography (sEMG) signal is a commonly used input signal source in human-computer interaction. sEMG is a weak action potential generated by muscle cells upon nerve activation and can be detected from superficial muscles through surface electrodes. sEMG contains rich information, and has the characteristics of simple collection and non-invasiveness. It has become a research hotspot in the field of human-computer interaction and has important research value. Current research on sEMG usually focuses on the recognition of human motion intention, including discrete movements and continuous movements. A...

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Problems solved by technology

However, there are two problems in the use of HMM. One is that HMM involves many complex physiological parameters, which are difficult to identify and requires a large amount of calculation. Second, in HMM, the motion state is indirectly calculated by the torque recognized by sEMG, which will bring cumulative errors. , reducing the estimation accuracy

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