Application of least square method for muscular strength estimation in hand motion recognition using surface EMG.
In this study, we describe the application of least square method for muscular strength estimation in hand motion recognition based on surface electromyogram (SEMG). Although the muscular strength can consider the various evaluation methods, a grasp force is applied as an index to evaluate the muscular strength. Today, SEMG, which is measured from skin surface, is widely used as a control signal for many devices. Because, SEMG is one of the most important biological signal in which the human motion intention is directly reflected. And various devices using SEMG are reported by lots of researchers. Those devices which use SEMG as a control signal, we call them SEMG system. In SEMG system, to achieve high accuracy recognition is an important requirement. Conventionally SEMG system mainly focused on how to achieve this objective. Although it is also important to estimate muscular strength of motions, most of them cannot detect power of muscle. The ability to estimate muscular strength is a very important factor to control the SEMG systems. Thus, our objective of this study is to develop the estimation method for muscular strength by application of least square method, and reflecting the result of measured power to the controlled object. Since it was known that SEMG is formed by physiological variations in the state of muscle fiber membranes, it is thought that it can be related with grasp force. We applied to the least-squares method to construct a relationship between SEMG and grasp force. In order to construct an effective evaluation model, four SEMG measurement locations in consideration of individual difference were decided by the Monte Carlo method.
Nakano
Takemi
T
Department of Electrical and Electronic Engineering, TOKAI University, Japan. 8adpm032@mail.tokai-u.jp
Nagata
Kentaro
K
Yamada
Masafumi
M
Magatani
Kazushige
K
D016002:Discriminant Analysis; D004566:Electrodes; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D004867:Equipment Design; D006225:Hand / Q000502:physiology*; D018737:Hand Strength; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D009010:Monte Carlo Method; D009038:Motion; D009132:Muscles / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012815:Signal Processing, Computer-Assisted